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| Rogerson receives quarter-million dollar donation in support of massive senior l |
| Posted on Friday, August 14 @ 00:04:23 PDT (9 reads) | |
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Rogerson communities announced today the receipt of a charitable gift of $250,000 made by darlene l. Boroviak. The gift will cover predevelopment expenses related to the organization’s planned redevelopment of its daggett-crandall-newcomb home (dcn) community in norton.
the transformational project represents a tenfold expansion of rogerson’s footprint and mission in southeastern massachusetts. Currently a 25-bed, single-level assisted living residence, dcn is being reimagined as a purpose-built senior living campus offering 103 independent living apartments, 100 independent living cottages, and 48 supportive living beds, including memory care. Fifty-five acres of fields and woodlands around the development will be conserved for resident and community use.
the donation is a current-use gift and will be used to advance project entitlements and financing activities. Anticipated costs include legal fees associated with the 40b permitting process; ongoing environmental review and analysis; civil and architectural design services; and financial consulting support as the project progresses with prospective financing partners, lenders, and other stakeholders.
boroviak has served on the dcn board of directors for close to 14 years, and in 2020 joined the rogerson board of directors. She joined the strategic committee of rogerson’s board, and currently holds the position of board clerk. She is also an emerita professor of political science at wheaton college in norton.
“my work with dcn and with rogerson has really convinced me that as a society, we dont have enough senior housing, and we need more,” said boroviak, noting in particular the scarcity of affordable housing for older adults in the norton area. “Im excited about being in a position to be able to make the donation and it pleases me very much to be able to do that.”
rogerson president and ceo walter ramos commented, “darlene’s gift is not only extraordinarily generous, it is a statement—a clear example for others to follow. The need for this kind of investment in homes and care for older adults has never been greater, and rogerson is fortunate to have leadership that both recognizes this urgency and answers the call.”
rogerson communities provides affordable homes, memory care, senior living, and adult day health solutions for older adults in massachusetts. A resource to the community since 1860, today we serve more than 2,000 older adults across 25 communities and programs, providing a diversity of individuals with greater independence and longevity.
rogerson’s mission is to enhance the well-being of older adults by elevating housing, community, and care to address their needs as they age. Our goal is a world where every older adult feels valued and can age with dignity in their community. More information can be found at rogerson.Org. |
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| UH Maui College receives $660K to enhance AI, cybersecurity education |
| Posted on Friday, August 14 @ 00:04:23 PDT (5 reads) | |
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To tackle a critical nationwide shortage of cybersecurity and artificial intelligence (ai) professionals, the university of hawai?I maui college has secured a three-year, $441,645 national science foundation (nsf) award to launch a groundbreaking project: “cyberai innovation: ai-enhanced cyber data analytics education.”
led by principal investigator debasis bhattacharya and co-investigator thomas blamey, the initiative builds on previous nsf-funded efforts and directly addresses a vital local need, as hawai?I currently ranks among the top five states in the nation for unmet cybersecurity workforce demand.
“the intersection of ai, data analytics and cybersecurity is a critical frontier for modern digital defense,” said bhattacharya, who also serves as the director of the center for cybersecurity education and research. “With hawai?I facing such a steep challenge in meeting cybersecurity job demands, this grant enables us to build robust, early-career pipelines. We are excited to empower local educators and students with the advanced skills needed to protect our critical systems.”
new curriculum, training
the project will introduce an ai-integrated curriculum by embedding six new modules—including adversarial machine learning, ai-powered threat detection, secure ai pipelines and cyberai ethics—across six core computer science courses at uh maui college. Additionally, a three-year professional development program will deliver statewide faculty training across all seven uh community colleges and hawai?I department of education (hidoe) secondary schools.
to cultivate early interest, the college will leverage the community of practice created by the nsf project csp4hawaii (a collaboration aimed at improving computer science education at the state level) to expand k–14 academic pathways. The project also extends beyond it, developing customized cyberai micro-modules for non-it fields such as healthcare/nursing, automotive technology and accounting/finance.
ai, gencyber cybersecurity camps
uh maui college has also secured a supplement award of $129,190 from the nsf to host two ai camps during the summers of 2027 and 2028 in conjunction with the hidoe. These camps will provide middle and high school students with basic ai literacy, safety, well-being, vibe coding (software development utilizing ai) experience and guidance on responsible usage.
to continue the tradition of cybersecurity summer camps funded by the national security agency’s gencyber, uh maui college has been awarded $89,537 to conduct two in-person student summer camps on o?Ahu and an online student camp during the summer of 2027. More details on all these camps will be available at gencyber hawai?I. |
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| Dual pathways of generative AI use: role ambiguity and self-efficacy in employee |
| Posted on Friday, August 14 @ 00:04:23 PDT (5 reads) | |
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Abstract
introduction:
the growing use of generative ai tools in everyday work improves efficiency but also creates uncertainty about how employees interpret their roles and whether they are willing to collaborate with ai. Drawing on role theory, this study aims to investigate how generative ai (genai) use relates to employee-ai collaboration via two distinct pathways: role ambiguity as a hindrance mechanism and role breadth self-efficacy as an enabling mechanism. This study further examines the moderating role of ai literacy in the above relationships.
methods:
survey data were collected from 541 employees working in chinese high-technology firms, and structural equation modeling was adopted to test the proposed research model.
results:
the empirical results reveal that genai use simultaneously increases role ambiguity and role breadth self-efficacy. Role ambiguity negatively predicts employee-ai collaboration, whereas role breadth self-efficacy exerts a positive effect. Ai literacy significantly moderates both pathways: it weakens the positive association between genai use and role ambiguity, and strengthens the positive link between genai use and role breadth self-efficacy. Furthermore, employee-ai collaboration is positively associated with job performance.
discussion:
these findings extend role theory by verifying that genai functions as both a disruptor and an enabler of employees’ role perceptions, and ai literacy serves as a vital boundary condition shaping how employees construct and understand their work roles when adopting genai. Practically, organizations are suggested to clarify work role boundaries, foster employees’ role breadth self-efficacy, and deliver systematic ai literacy training to facilitate high-quality human-ai collaboration.
1 introduction
generative artificial intelligence (genai) is reshaping how work is performed across a wide range of industries, driven by core technological advances such as large language models, transformer architectures, and the capacity to generate human-like text, code, and creative content (). These capabilities enable genai tools to provide suggestive guidance, generate drafts, and produce outputs that would otherwise require substantial human effort ().
recent academic research has increasingly explored genai’s potential to enhance workplace productivity and to foster employee engagement, satisfaction, enthusiasm, loyalty, and innovation capabilities (; ). Although genai has demonstrated remarkable capabilities, often surpassing humans in speed and content generation, its rapid adoption has sparked intense debate about the future of work (). Critics have highlighted pressing concerns such as privacy vulnerabilities, diminished emphasis on human judgment, inherent bias in automated systems, mistrust of algorithmic decision-making, and the shift from human-to-human collaboration to hybrid human-ai workflows (). Beneath these concerns lies a more fundamental issue: as genai takes over tasks that were previously central to employees’ professional identity, employees may become uncertain about the boundaries of their own role and about what is expected of them in an ai-augmented workplace. Empirical evidence indicates that these role-related uncertainties are exacerbating anxiety among workers, fueling digital fatigue and triggering concerns about career relevance in the era of generative ai (). Whether and how employees are willing to collaborate with genai at work has therefore become a pressing question. However, the psychological mechanisms underlying employees’ collaboration intentions with genai remain insufficiently understood.
the introduction of genai necessitates a fundamental shift in how employees perceive their work roles. Research has shown that the balance between human agency and genai assistance influences employee well-being, motivation, and performance. A lack of alignment may undermine their effectiveness in fulfilling their duties (). The advent of genai has profoundly disrupted conventional work paradigms, precipitating transformative shifts in how employees approach their tasks (). Individuals may experience a sense of helplessness in maintaining this equilibrium as they become aware of genai’s disruptive potential (). Specifically, genai use may trigger two opposing cognitive responses: on one hand, it may blur the boundaries of job responsibilities and create uncertainty about role expectations, leading to role ambiguity; on the other hand, it may expand employees’ perceived capacity to take on broader responsibilities, enhancing role breadth self-efficacy (; ; ). Not all employees, however, respond to genai use in the same way. Some employees interpret genai as a threat that undermines their role clarity, while others view it as an opportunity to expand their role capabilities. This suggests that individual differences may determine whether employees respond to genai with role ambiguity or role breadth self-efficacy. Nevertheless, the mechanisms through which genai-induced role perceptions shape employees’ intentions to collaborate with genai are not yet fully understood.
specifically, this study addresses two questions. First, how does genai use relate to employee-ai collaboration through role-based cognitive mechanisms? We propose that genai use is related to collaboration intentions through two parallel pathways: role ambiguity, which acts as a hindrance mechanism, and role breadth self-efficacy, which acts as an enabling mechanism. Second, building on the observation that employees differ in how they respond to genai, what individual difference explains why some employees experience role ambiguity while others develop role breadth self-efficacy? We propose that ai literacy, defined as individuals’ ability to understand, interact with, and evaluate ai systems (), serves as a critical boundary condition that weakens the hindrance pathway and strengthens the enabling pathway.
although several psychological mechanisms may explain how genai use relates to employee-ai collaboration, we focus on role ambiguity and role breadth self-efficacy for three reasons. First, genai does not merely assist with task execution; it disrupts the very definition of employees’ professional roles by generating content that was previously central to their identity. This role-based disruption is precisely what role theory is designed to explain. Role theory posits that new technologies primarily influence employees through changes in role perceptions. Unlike trust in ai or ai anxiety, which are general attitudes toward technology, role ambiguity and role breadth self-efficacy directly capture how employees reinterpret their professional identity when genai enters the workplace. This role-based reinterpretation is the core theoretical mechanism that distinguishes genai from traditional workplace technologies. Second, prior research has identified role ambiguity as a key stressor in technology adoption contexts (; ), and role breadth self-efficacy as a critical driver of proactive behaviors (; ). However, no study has examined whether genai simultaneously triggers both mechanisms. Third, genai is unique because it simultaneously disrupts role clarity and enhances role capability. This paradoxical effect is best captured by the dual pathways of role ambiguity and role breadth self-efficacy. This study answers the call for a more nuanced understanding of how ai shapes employees’ psychological states and behavioral intentions. Other mechanisms, such as trust or anxiety, tend to operate in a single direction and cannot capture this duality.
to answer these questions, we draw on role theory (), which posits that new technologies can significantly reshape employees’ role perceptions and subsequent behaviors. We collected survey data from 541 employees at chinese high-technology firms and tested our conceptual model using structural equation modeling. The remainder of this paper is organized as follows. Section “2 literature foundation and hypotheses” reviews the theoretical background and develops the hypotheses. Section “3 materials and methods” describes the methodology. Section “4 results” presents the results. Section “5 discussion” discusses the findings, theoretical contributions, and practical implications. Section “6 limitations and future research” addresses limitations and directions for future research.
2 literature foundation and hypotheses
2.1 generative artificial intelligence (genai)
generative artificial intelligence (genai) refers to advanced ai systems capable of generating new content, including text, images, code, audio, and other media formats, based on patterns learned from extensive training data (; ). The focal point of this technological transformation is large language models (llms), sophisticated neural network architectures that can generate coherent, contextually relevant, and human-like responses to user prompts (). Genai has demonstrated unprecedented capabilities in understanding context, generating creative content, answering complex questions, and even exhibiting incipient reasoning abilities that were previously considered exclusive to human intelligence (). In this study, genai is defined as artificial intelligence systems that generate novel ideas, solutions, and knowledge in response to employees’ inputs.
genai has been shown to offer employees significant advantages, including enhanced operational efficiency, improved decision-making accuracy, and the ability to generate valuable insights from complex data (). For example, genai systems can automate routine cognitive tasks such as email drafting and report summarization, while simultaneously augmenting human capabilities by providing creative suggestions, data-driven recommendations, and analytical support (). Moreover, genai can generate creative content for various professional functions, overcoming limitations in time, expertise, or cognitive bandwidth (). Consequently, genai has emerged as a strategic priority for organizations seeking to drive business value, enhance innovation, and maintain competitive advantage in increasingly data-driven markets ().
2.2 employee-ai collaboration
employee-ai collaboration refers to employees’ perceived engagement in collaborative behaviors with ai systems, including ai’s participation in decision-making, forecasting, problem-solving, and information evaluation (). Unlike traditional automation where ai replaces human labor, genai collaboration requires employees to remain actively engaged throughout the process. The ai produces drafts, suggestions, or analyses, while the employee retains control over final outputs, evaluation, and refinement (). This collaborative model features iterative interaction, where employees prompt, query, and critique the ai’s outputs, thereby co-creating solutions that neither could achieve alone.
for employees, human-ai collaboration offers tangible benefits that directly contribute to their work performance. When genai systems serve as cognitive partners and analytical assistants, they can quickly generate drafts, explore multiple solutions, and refine outputs through iterative dialogue, thereby reducing the cognitive burden of starting from scratch while maintaining control over final results. Empirical evidence indicates that genai assistance significantly improves employee task performance by fostering innovative work behavior, particularly under conditions of task complexity (). Moreover, employee-ai collaboration has been found to reduce workload pressures, which in turn enables employees to take on additional responsibilities and engage in more proactive work behaviors (). By freeing employees from repetitive or cognitively draining tasks, genai collaboration allows individuals to focus on higher-order activities that require human judgment, creativity, and emotional intelligence, thereby enhancing overall job performance. Accordingly, we propose:
h1: employee-ai collaboration is positively correlated with their job performance.
2.3 dual pathways from role theory: role ambiguity and role breadth self-efficacy
role theory was initially introduced as a theoretical framework to elucidate the mechanisms through which employees’ perceptions of their roles influence organizational outcomes. Theorists have argued that employees are constantly subjected to an array of expectations from their work environment, which define their perceptions of their organizational roles. Role theory provides a foundational lens for understanding how environmental changes, such as the use of genai, reshape employees’ perceptions of their work roles and subsequently influence their behaviors (; ). According to the theory, employees hold multiple expectations about their professional responsibilities. When these expectations become unclear, inconsistent, or inadequately defined, role ambiguity arises. Conversely, when employees feel confident in executing broader, more proactive tasks beyond their formal job descriptions, they exhibit high role breadth self-efficacy. In the context of genai use, both phenomena can be triggered simultaneously, creating two parallel pathways that affect employees’ willingness to collaborate with ai systems.
role ambiguity as a hindrance pathway. Unlike traditional workplace technologies, which have clearly defined functional boundaries (e.G., Spreadsheet software performs calculations), genai produces content that overlaps with employees’ core cognitive and creative tasks. This overlap blurs the distinction between tasks that belong to the employee and tasks that can be delegated to ai, creating a unique condition for role ambiguity to emerge. Based on role theory, role ambiguity occurs when goals and procedures are unclear or ill-defined, preventing employees from fully comprehending the expectations associated with their professional responsibilities (). For some employees, the introduction of genai disrupts this clarity, leading to ambiguity in role enactment.
moreover, when genai outputs conflict with employees’ own professional judgment, expectations, or work standards, role ambiguity may also emerge. Argued that genai’s ability to mimic cognitive, creative, and interpersonal capabilities challenges traditional human-machine boundaries and psychologically threatens workers’ needs for competence, autonomy, and relatedness, directly inducing role ambiguity. Further showed that the introduction of ai into the workplace triggers jurisdictional conflicts, forcing professionals to engage in boundary struggling and boundary retreating, as they struggle to define whose judgment should prevail when ai output diverges from human expertise. Also demonstrated that working with ai leads individuals to displace responsibility onto the technology, eroding their sense of personal accountability and blurring decision authority. Based on these findings, we propose the following hypotheses:
h2: genai is positively correlated with employees’ role ambiguity.
research consistently shows that ambiguous role expectations adversely affect employees’ work outcomes, including lower job satisfaction, reduced organizational commitment, and decreased proactive behavior (; ). Role theory posits that role ambiguity deprives employees of clear behavioral guidance, which reduces their confidence in engaging in role-extending behaviors. Employee-ai collaboration requires employees to proactively integrate ai into their work processes, a behavior that demands clear role expectations. When employees are uncertain about their role boundaries, they are likely to withdraw from such proactive engagement to avoid role conflict or performance evaluation risks ().
complementing this, , in their comprehensive multilevel review of ai in organizations, identified role ambiguity as a key mechanism through which genai use triggers negative employee responses; employees who are uncertain about their evolving role responsibilities tend to withdraw from proactive engagement with ai, viewing it as a threat to their professional identity rather than a collaborative tool. Similarly, reported that unclear role expectations in ai-augmented work environments lead employees to perceive collaboration with ai as cognitively taxing and psychologically risky, reducing their willingness to engage in joint problem-solving with intelligent systems. Collectively, these findings suggest that role ambiguity undermines employee-ai collaboration by draining psychological resources, heightening risk perceptions, and fostering withdrawal tendencies. Therefore, we propose the following hypothesis:
h3: role ambiguity is negatively correlated with employee-ai collaboration.
role breadth self-efficacy as an enabling pathway. According to role theory, role breadth self-efficacy reflects employees’ confidence in taking on broader, more proactive tasks beyond their formal job descriptions (). While role theory identifies this construct, social cognitive theory explains how it develops: self-efficacy is shaped by four sources: mastery experiences, vicarious learning, verbal persuasion, and physiological states. In the context of genai use, each of these four sources can be activated. Specifically, genai provides mastery experiences when employees successfully use it to complete tasks that were previously beyond their skill set; it offers vicarious learning opportunities as employees observe and internalize novel problem-solving strategies from ai-generated outputs; it delivers verbal persuasion through immediate, actionable suggestions and guidance; and it fosters positive physiological states by reducing cognitive load and alleviating anxiety associated with unfamiliar tasks.
when employees successfully use genai to complete tasks that were previously beyond their immediate skill set, they acquire direct evidence of their expanded capability. This positive mastery experience, in turn, strengthens their confidence in taking on even broader responsibilities (). Moreover, by automating routine cognitive tasks such as drafting, summarizing, or code generation, genai alleviates the burden of low-level work, allowing employees to gain more cognitive bandwidth for strategic thinking, creative problem-solving, and role expansion (). In addition, employees can internalize new problem-solving strategies and creative approaches by observing how genai generates novel solutions. According to , this observational learning provides vicarious experiences that enhance employees’ belief in their own ability to perform similar tasks independently. Finally, when facing unfamiliar or complex tasks, employees often experience anxiety and uncertainty. Found that genai provides immediate suggestions, reference solutions, and step-by-step guidance, which lowers the perceived difficulty of the task and alleviates anxiety. This reduction in negative emotional arousal fosters greater confidence in undertaking expanded role responsibilities. Collectively, these theoretical mechanisms and empirical findings indicate that genai use serves as a contextual enabler that bolsters employees’ confidence in their capacity to take on expanded work roles. Therefore, we propose:
h4: genai is positively correlated with employees’ role breadth self-efficacy.
both social cognitive and role theorists agree that role breadth self-efficacy reflects a general willingness to transcend routine role boundaries and embrace expanded responsibilities. Individuals who believe in their ability to perform broader, proactive tasks are more willing to experiment with new technologies, integrate them into their workflows, and engage in joint problem-solving with intelligent systems (). Indeed, self-efficacy impacts the initiation, guidance, exertion, and tenacity of behavior (; ). Empirical evidence supports that employees who possess this confidence tend to approach new work methods, including ai systems, as opportunities for role enhancement rather than threats to their existing job security (; ). They explained that employees with high self-efficacy tend to approach ai systems with curiosity and experimentation, viewing them as tools that extend their existing capabilities. In contrast, those with low self-efficacy may experience anxiety or avoidance when confronted with ai integration. Similarly, suggested that those with increased role breadth efficacy view ai as more advantageous and productive, and tend to take advantage of its automation and enhancement capabilities. In total, employees with strong role breadth self-efficacy are more willing to experiment with ai tools, integrate them into workflows, and engage in joint problem-solving with intelligent systems. Thus, we assume that:
h5: role breadth self-efficacy is positively correlated with employee-ai collaboration.
2.4 ai literacy
role theory posits that environmental stimuli, such as the introduction of ai, reshape employees’ role perceptions (ambiguity and self-efficacy), which in turn influence their behavioral responses, including collaboration with ai. However, role theory primarily explains how individuals perceive and react to environmental changes, but it offers limited insight into why employees differ in how they interpret their roles when using generative ai, and why some employees successfully translate their role perceptions into constructive actions while others do not. Social cognitive theory suggests that the influence of the environment on individuals depends on their cognitive capacity to interpret and act upon their work environment. Among various individual difference variables that could potentially moderate the link, ai literacy is uniquely relevant to the present context. Ai literacy directly addresses the knowledge and skills required to understand, interact with, and evaluate ai systems (). It provides employees with the interpretive framework needed to making sense of ai-induced role changes and acting on their role perceptions effectively.
in a world in which algorithms are increasingly defining reality, the notion of literacy has been expanded. It is no longer limited to traditional abilities but now encompasses multiple areas of digital skills ai literacy covers an individual’s overall ability to understand and interact with artificial intelligence. This includes the ability to critically evaluate ai applications, the ability to express ideas clearly when using ai tools, and the ability to seamlessly collaborate with these systems as partners when solving problems. This is not only about technical knowledge, but also about cultivating wisdom to effectively respond to the ai-driven world (). And proposed a structure for defining ai literacy, focusing on the skill to adeptly employ ai technologies (usage), identify and understand ai technologies during interactions (awareness), critically evaluate ai applications and their outputs (evaluation), and the responsibilities related to ai use (ethics). Similarly, indicated that employees’ ai literacy can be subdivided into four key capabilities: technology-related (understanding the technology), ethical awareness-related (understanding ethical issues related to ai use), human-ai collaboration-related (optimizing teamwork with intelligent systems), and learning-related (concern regarding lifelong learning).
role theory posits that environmental stimuli, such as the introduction of ai, reshape employees’ role perceptions (ambiguity or self-efficacy). However, role theory alone offers limited insight into why employees differ in how they interpret their roles when using genai. Ai literacy can be understood as a critical cognitive resource that helps individuals navigate ai-augmented work environments by providing accurate mental models of ai’s capabilities and limitations (). This knowledge enables employees to clearly distinguish which tasks are appropriate for ai delegation and which must remain under their own control, thereby reducing role boundary ambiguity that might otherwise arise from unclear task allocation (; ). Moreover, ai literacy includes the ability to critically evaluate ai outputs. As found, rather than accepting ai-generated content uncritically, literate users engage in verification, validation, and adjustment. This critical engagement prevents cognitive offloading and skill atrophy, which in turn mitigates doubts about one’s own competence boundaries. In addition, ai literacy equips employees with the capacity to manage conflicts between ai recommendations and their own professional judgment. When discrepancies occur, literate users can understand the reasoning behind ai suggestions and make informed decisions, thus reducing role confusion stemming from such conflicts (). Taken together, these findings imply that ai literacy may serve as a buffer that mitigates or counteracts employees’ role ambiguity when they use genai. Accordingly, we hypothesize:
h6: ai literacy moderates the negative relationship between genai and role ambiguity, that is, this relationship is weaker for employees with stronger ai literacy and stronger at lower levels.
ai literacy encompasses the ability to formulate precise prompts, engage in iterative dialogue with ai systems, and critically evaluate ai-generated outputs, which directly improve the quality of mastery experiences obtained from genai use (). When employees possess the skills to refine prompts, interpret responses, and adjust their queries based on ai feedback, they are more likely to achieve successful task outcomes. These successful experiences serve as potent mastery experiences, which are the strongest driver of self-efficacy (). Further, ai literacy also includes the capacity to extract transferable knowledge from ai-generated examples. By observing how genai structures solutions or generates novel ideas, employees can internalize new problem-solving strategies. This vicarious learning is more effective when the observer understands the underlying logic of ai outputs, which serves as a strong predictor of self-efficacy (). Collectively, ai literacy strengthens the positive relationship between genai use and role breadth self-efficacy. Accordingly, we propose:
h7: ai literacy moderates the positive relationship between genai and role breadth self-efficacy, that is, this relationship is stronger for employees with stronger ai literacy and weaker at lower levels.
table 1 provides an overview of the key constructs examined in this study, along with representative references and a brief description of each construct’s conceptual focus. These references were selected based on their foundational contributions, empirical relevance, and alignment with the measurement approaches adopted in the present research.
table 1
| construct | representative references | key focus / measurement context |
|---|---|---|
| genai | ; | employees’ perceptions of using generative ai (e.G., Chatgpt, copilot) for task-oriented purposes, including drafting, problem-solving, data synthesis, and knowledge acquisition; frequency and engagement with genai in daily work |
| role ambiguity | ; | role ambiguity in human-ai interaction; meta-analysis of role stressors; technostress and role ambiguity as a stressor from technology characteristics |
| role breadth self-efficacy | ; ; | conceptualization and measurement of rbse; rbse as antecedent of proactive behavior; rbse and proactive work behavior |
| employee-ai collaboration | ; ; | trust in ai and employee-ai collaboration; conceptualizing collaboration with ai; ai-employee collaboration and business performance |
| ai literacy | ; ; | ai literacy, apprehension, and acceptance; ai literacy scale development; competencies and design considerations |
| job performance | ; | in-role performance measurement; ai collaboration and performance outcomes |
key studies and constructs in the proposed model.
building on the analysis, we developed the following conceptual framework (see figure 1).
figure 1
3 materials and methods
3.1 participants
this survey was conducted with the employees of five high-tech firms in tianjin, china, which have experience using genai in their work or life. To facilitate participation, a qr code was shared with the respondents, enabling them to conveniently access the webpage and complete the questionnaire. At the beginning of the questionnaire, a detailed explanation was provided, clarifying that the research was centered on employees’ perceptions and reactions to genai.
in total, 580 participants comprised the research sample. Invalid responses were excluded based on the following criteria: (a) completion time less than 120 s (based on pilot test median completion time minus two standard deviations); (b) straight-lining responses (i.E., Identical answers for more than 80% of consecutive items); (c) missing data for more than 10% of the core measurement items. Applying these criteria, 39 out of 580 responses were removed, yielding a final sample of 541 (49% female) valid cases.
the age distribution showed that 27.2%, 31.3%, 23%, 18.5% were below 25, 26–35, 35–46, and over 46 years, respectively. In the sample, 25.5%, 32.7%, 30.4%, and 11.5% had high school or lower, associate degrees, bachelor’s degrees, and master’s or higher, respectively. Management levels were distributed as follows: 63.4% non-managerial staff, 16.5% junior management, 11.3% middle management, 4.5% senior management, and 4.3% other. Regarding tenure, 27.2%, 31.3%, 23%, 15.4%, and 3.1% of employees had worked for less than 1 year, 1–2 years, 3–4 years, 5–6 years, and more than 6 years, respectively.
3.2 methods
where applicable and relevant, the items were adapted from existing literature. Considering the unique characteristics of the chinese culture and organizational context, the original items were translated into chinese through a comprehensive translation process that includes forward and reverse translation to ensure fidelity and accuracy of the intended meaning. Responses were scored on a 5-point likert scale (1 = strongly disagree, 5 = strongly agree).
job performance. Job performance was rated using a 7-item scale derived from , to assess individuals’ work capabilities and performance outcomes. A sample item was “i can fulfil responsibilities specified in job description.” Cronbach’s alpha for the scale was 0.954.
employee-ai collaboration. Employee-ai collaboration was rated using a 5-item scale, derived from to evaluate employees’ readiness to work with ai in decision-making, forecasting, issue resolution, data analysis, recognition of problems, identification of opportunities and risks. A sample item was “ai participates in my problem-solving process.” Cronbach’s alpha for the scale was 0.861.
genai. Genai was rated using a 4-item scale, derived from , to assess employees’ use of genai. A sample item was “i use genai assistants to obtain ideas.” Cronbach’s alpha for the scale was 0.843.
role ambiguity. Role ambiguity was rated using a 6-item scale, based on , evaluating workers’ perceived role uncertainty. A sample item was “i do not know exactly what is expected of me.” Cronbach’s alpha for the scale was 0.886.
role breadth self-efficacy. Role breadth self-efficacy was rated using a 3-item scale, derived from to assess employee confidence in their work roles. A sample item was “i am confident in helping to set goals at work.” Cronbach’s alpha for the scale was 0.835.
ai literacy. Ai literacy was rated using an 8-item scale, derived from , which assesses different aspects of ai literacy, such as ai utilization, comprehension, detection, and ethical considerations. A sample item was “i can assess the advantages and disadvantages associated with the use of ai.” Cronbach’s alpha for the scale was 0.894. The measurement scales used in this study are presented in appendix 1.
4 results
4.1 descriptive statistics and correlations
table 2 displays the summary statistics, including the means, standard deviations, composite reliabilities (cr), average variance extracted (ave), and pearson correlations for all latent variables. Composite reliabilities ranged from 0.895 to 0.962, all exceeding the recommended threshold of 0.70. Aves ranged from 0.574 to 0.783, all above 0.5. The square root of each ave (shown in bold on the diagonal) was larger than its correlations with any other construct, supporting both convergent and discriminant validity.
table 2
| mean | sd | 1 | 2 | 3 | 4 | 5 | 6 | cr | ave | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1. Genai | 2.93 | 1.091 | (0.825) | 0.895 | 0.680 | |||||
| 2. Role ambiguity | 3.01 | 1.135 | 0.335
|
**************************means, standard deviations, and correlations between the latent variables.
bold figures on the diagonal are square roots of ave. Correlations below the diagonal are pearson correlations. Cr, composite reliability; ave, average variance extracted. *P < 0.05, ***p < 0.001.
as anticipated, genai correlated positively with role ambiguity (r = 0.335, p < 0.001) and with role breadth self-efficacy (r = 0.36, p < 0.001). Role ambiguity was negatively related to employee-ai collaboration (r = –0.148, p < 0.01), whereas role breadth self-efficacy showed a positive relationship with employee-ai collaboration (r = 0.18, p < 0.001). Moreover, employee-ai collaboration was positively associated with job performance (r = 0.408, p < 0.001). These results offer initial support for our hypotheses.
4.2 test of the measurement model
we conducted a series of confirmatory factor analyses (cfa) to examine the discriminant validity of the six core constructs (genai, role ambiguity, role breadth self-efficacy, employee-ai collaboration, job performance, and ai literacy). The hypothesized six-factor model was compared against two alternative models: a five-factor model in which role ambiguity and role breadth self-efficacy were combined into one factor, and a three-factor model that further merged ai literacy with the above two factors and also combined employee-ai collaboration with job performance. The fit indices for all models are summarized in table 3.
table 3
| model | ? 2 | df | rmsea | cfi | srmr |
|---|---|---|---|---|---|
| six-factor | 561.94 | 480 | 0.018 | 0.992 | 0.03 |
| five-factor | 1599.69 | 486 | 0.065 | 0.889 | 0.107 |
| three-factor | 4670.48 | 495 | 0.125 | 0.583 | 0.199 |
measurement model result.
the six-factor model demonstrated an excellent fit to the data: ?2(480) = 561.94; cfi = 0.992; rmsea = 0.018; srmr = 0.03. All indices met or exceeded the recommended thresholds (cfi > 0.95, rmsea < 0.05, srmr < 0.08), indicating that the measurement structure was well supported. The five-factor model showed a marginal fit (cfi = 0.889, rmsea = 0.065, srmr = 0.107), and its chi-square difference relative to the six-factor model was highly significant (??2(6) = 1037.75, p < 0.001). The three-factor model yielded a very poor fit (cfi = 0.583, rmsea = 0.125, srmr = 0.199). These results confirm that the six distinct factors are empirically distinguishable and that the measurement model is appropriate for subsequent hypothesis testing.
4.3 testing the hypotheses
we tested our hypotheses using bayesian estimation with mplus 8.3. The potential scale reduction (psr) values were below 1.05 after the final iteration, indicating satisfactory convergence. Table 4 shows the results of hypotheses paths.
table 4
| hypothesis and relationship | ? -Value | 95%ci | outcomes |
|---|---|---|---|
| panel a. Main effects | |||
| genai?Ra | 0.347 | [0.272, 0.415] | h2 supported |
| genai?Rse | 0.347 | [0.273, 0.414] | h4 supported |
| ra?Collab | ?0.232 | [?0.314, ?0.147] | h3 supported |
| rse?Collab | 0.256 | [0.170, 0.338] | h5 supported |
| collab?Jp | 0.409 | [0.335, 0.477] | h1 supported |
| panel b. Moderating effects | |||
| genai × ail?Ra | ?0.204 | [?0.277, ?0.128] | h6 supported |
| genai × ail?Rse | 0.168 | [0.092, 0.242] | h7 supported |
structural model.
ra, role ambiguity; rse, role breadth self-efficacy; collab, employee-ai collaboration; jp, job performance; ail, ai literacy.
the results showed that genai was positively related to role ambiguity (? = 0.347, 95% ci [0.272, 0.415]) and to role breadth self-efficacy (? = 0.347, 95% ci [0.273, 0.414]), supporting h2 and h4, respectively. Role ambiguity had a negative effect on employee-ai collaboration (? = –0.232, 95% ci [–0.314, –0.147]), while role breadth self-efficacy had a positive effect (? = 0.256, 95% ci [0.170, 0.338]), thus supporting h3 and h5. Employee-ai collaboration was positively associated with job performance (? = 0.409, 95% ci [0.335, 0.477]), confirming h1.
we also examined the moderating role of ai literacy. The interaction between genai and ai literacy on role ambiguity was negative and significant (? = –0.204, 95% ci [–0.277, –0.128]), indicating that high ai literacy attenuates the positive association between genai and role ambiguity, thus supporting h6. Likewise, the interaction between genai and ai literacy on role breadth self-efficacy was positive and significant (? = 0.168, 95% ci [0.092, 0.242]), suggesting that ai literacy strengthens the association between genai and role breadth self-efficacy, supporting h7.
to interpret the interactions, we performed simple slope analyses at one standard deviation below and above the mean of ai literacy. Figure 2a presents the results for the moderating effect of ai literacy on the relationship between genai and role ambiguity. It shows that, at low levels of ai literacy (? = 0.558, 95% ci [0.441, 0.673]), the positive relationship was significant, whereas at high levels of ai literacy (? = 0.140, 95% ci [0.032, 0.248]), the relationship became weaker and only marginally significant (with the confidence interval approaching zero). This indicates that high ai literacy attenuates the positive effect of genai on role ambiguity. Figure 2b shows that the relationship between genai and role breadth self-efficacy was positive at both low and high levels of ai literacy. This positive effect was significantly stronger at high ai literacy (? = 0.521, 95% ci [0.415, 0.626]) than at low ai literacy (? = 0.177, 95% ci [0.064, 0.293]), confirming that ai literacy enhances the positive influence of genai on role breadth self-efficacy.
figure 2
5 discussion
this study aimed to uncover how genai relates to employees’ perceived collaboration with ai systems, drawing on role theory. We proposed a dual-path model in which genai relates to employee-ai collaboration through role ambiguity and role breadth self-efficacy, with ai literacy as a moderator. Analyzing survey data from 541 employees in chinese high-technology firms, we found that genai is positively associated with both role ambiguity and role breadth self-efficacy. Role ambiguity is negatively associated with employee-ai collaboration, whereas role breadth self-efficacy is positively associated with it. More importantly, ai literacy significantly moderates both relationships: at high levels of ai literacy, the positive association between genai and role ambiguity is significantly attenuated, while the positive association between genai and role breadth self-efficacy is strengthened. Employee-ai collaboration, in turn, is associated with enhanced job performance. These findings offer several theoretical and practical contributions.
5.1 theoretical implications
first, this study provides a dual-path model that captures both the disruptive and enabling effects of genai on employees’ role perceptions, thereby extending role theory to the context of human–ai collaboration. Prior research has largely focused on ai’s technical capabilities or employees’ general attitudes toward genai (). However, these studies have overlooked the role-based cognitive mechanisms that mediate the relationship between genai use and collaboration intentions. Our findings show that genai simultaneously increases role ambiguity and role breadth self-efficacy. This duality mirrors the automation-augmentation paradox identified by and extends it by demonstrating that both pathways significantly affect collaboration intentions. This finding suggests that genai should not be viewed as uniformly positive or negative; its impact depends on which role perception predominates and how employees interpret ai-induced changes. By uncovering this dual-pathway mechanism, our study answers the call for more nuanced understanding of how ai shapes employees’ psychological states and behavioral intentions ().
second, this study clarifies the role ambiguity pathway and identifies ai literacy as a critical boundary condition that weakens this hindrance mechanism. The significant positive effect of genai on role ambiguity aligns with classic role theory (; ) and prior studies on role stressors in technology contexts (; ). However, our study goes beyond these findings by demonstrating that this relationship is not fixed. Specifically, the moderating effect of ai literacy reveals that role ambiguity is not an inevitable consequence of genai use; rather, it depends on employees’ ability to understand and interact with ai systems. This finding extends previous research that treated ai literacy primarily as a direct antecedent of ai acceptance or performance (; ). Instead, we position ai literacy as a cognitive resource that helps employees distinguish between ai’s capabilities and their own responsibilities, thereby reducing the role ambiguity triggered by genai use. This theoretical insight answers the question of why some employees experience role ambiguity when using genai while others do not.
third, this study clarifies the role breadth self-efficacy pathway and identifies ai literacy as a conditioner that strengthens this enabling mechanism. The positive effect of genai on role breadth self-efficacy supports prior work on proactive behavior (; ) and ai self-efficacy (; ). However, our study is among the few to demonstrate that genai use can enhance role breadth self-efficacy, and that this effect is stronger when employees possess higher ai literacy. This finding is consistent with social cognitive theory (), which emphasizes that mastery experiences are more effective when individuals have the relevant cognitive skills to interpret and learn from these experiences. In our context, ai-literate employees are better positioned to obtain mastery experiences from successful genai use, thereby amplifying their confidence in taking on broader responsibilities. This theoretical insight advances our understanding of how genai can serve as an enabling technology that empowers employees to expand their role capabilities, rather than merely automating their tasks.
fourth, the distinct moderating roles of ai literacy on the two pathways reveal a nuanced picture. Consistent with existing evidence (; ; ), we find that ai literacy weakens genai’s positive effect on role ambiguity while strengthening its positive effect on role breadth self-efficacy. This dual moderating effect underscores that ai literacy is not merely a general competence but a critical cognitive resource that shapes how employees interpret and respond to genai-induced role changes (). By bridging role theory and social cognitive theory, our study establishes ai literacy as a key boundary condition that determines whether genai’s influence tilts toward disruption or empowerment.
5.2 practical implications
our findings offer several practical insights for organizations implementing ai. First, as genai inevitably increases role ambiguity, organizations should proactively manage role clarity. However, our results show that simply clarifying roles may not be sufficient; instead, investing in ai literacy training can help employees reinterpret ambiguity constructively. Specifically, when employees understand ai’s capabilities and limitations, they are more likely to view ambiguous role expectations as opportunities for role expansion rather than as threats. Thus, organizations should combine role clarification with ai literacy programs that cover technical, ethical, and collaborative aspects of ai use (; ).
second, role breadth self-efficacy can be seen as a valuable personal resource. Yet, low ai literacy can render generative ai use less effective in boosting self-efficacy. Organizations should not only encourage proactive role behaviors (e.G., Through job enrichment or participative goal setting) but also provide hands-on ai training that builds employees’ confidence in using ai tools. Co-design workshops, where employees participate in selecting or customizing ai applications, can simultaneously enhance self-efficacy and ai literacy.
third, employee-ai collaboration directly improves job performance. Hence, managers should design workflows that genuinely integrate ai as a collaborative partner rather than a replacement. Performance evaluation systems should reward effective human-ai collaboration, not just individual task completion.
6 limitations and future research
although this study advances our understanding of human-ai collaboration, several limitations need attention. First, this study focused on general-purpose generative ai tools (e.G., Chatgpt, copilot) that employees use voluntarily for task assistance. Other forms of genai, such as domain-specific agents, highly autonomous systems, or mandatorily deployed tools, may affect role perceptions differently. Second, although role theory and social cognitive theory provide a robust foundation, this framework does not fully account for organizational contextual factors that may define human-ai collaboration outcomes. Organizational structures (e.G., Centralized versus decentralized decision-making), cultural norms (e.G., Innovation climate and risk tolerance), and resource allocation strategies (e.G., Ai training budgets) likely moderate how employees perceive and adapt to ai-driven role change (). Future studies can examine how organizational contextual factors interact with individual factors that affect employee-ai collaboration. Therefore, the generalizability of our findings to other ai types requires further investigation. Third, the cross-sectional data from chinese high-technological firms limits causal inferences and generalizability. Future research should test this conceptual framework based on different cultural factors to improve its universality. Fourth, although we collected data from multiple firms to enhance generalizability, all variables were self-reported by the same respondents at the same time, which may introduce common method bias (). To assess this risk, we took several procedural remedies, including ensuring anonymity and confidentiality, using established scales with clear instructions, and separating measurement of independent and dependent variables in the questionnaire. However, we acknowledge this limitation and encourage future research to use multi-source data (e.G., Supervisor-rated job performance) or experimental designs to strengthen causal inference.
7 conclusion
this study examined how genai is associated with employee-ai collaboration through two parallel mechanisms, and how ai literacy moderates these pathways. These findings extend role theory by portraying genai as both a disruptor (increasing role ambiguity) and an enabler (enhancing role breadth self-efficacy), with ai literacy serving as a critical boundary condition that determines how employees form and interpret their role perceptions when using genai. Organizations should not only clarify role boundaries and foster self-efficacy through participatory design but also invest in ai literacy training to help employees interpret ambiguous situations constructively and leverage ai effectively.
statements
data availability statement
the raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
ethics statement
the studies involving humans were approved by the ethics committee of tianjin university of finance and economics pearl river college (protocol code tjufeprc20250219, 2025-2). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
author contributions
qz: validation, conceptualization, supervision, resources, formal analysis, writing – original draft, project administration. Jz: writing – review & editing, validation, conceptualization. Sd: methodology, investigation, software, visualization, data curation, writing – review & editing.
funding
the author(s) declared that financial support was received for this work and/or its publication. This research was funded by tianjin university of finance and economics pearl river college’s school-level teaching reform project, grant number: zjjg25-10y, and tianjin higher education institutions’ project for research on teaching quality and teaching reform, grant number: b251408701.
acknowledgments
we would like to thank editage (www.Editage.Cn) for english language editing.
conflict of interest
the author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
generative ai statement
the author(s) declared that generative ai was not used in the creation of this manuscript.
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publisher’s note
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appendix
appendix a1:
| constructs | measurements |
|---|---|
| genai use | 1. I use genai assistants to obtain ideas 2. I use genai assistants to acquire solutions for problems 3. I use genai assistants to get knowledge 4. I use genai assistants to ask questions |
| employee-ai collaboration | 1. Ai participates in my decision-making process 2. Ai participates in my prediction process 3. Ai participates in my problem-solving process. 4. Ai participates in my information identification and evaluation process. 5. Ai participates in my problems, opportunities, or risk recognition process. |
| role ambiguity | 1. I don’t have clear, planned goals and objectives for my job 2. I don’t know whether i have divided my time properly. 3. I don’t know exactly what is expected of me. 4. I don’t know what my responsibilities are. 5. The explanation of what has to be done is unclear. 6. I feel uncertain about how much authority i have. |
| role breadth self-efficacy | 1. I am confident in helping to set goals at work 2. I am confident in presenting information to a group of colleagues 3. I am confident in designing new procedures for my work area |
| ai literacy | 1. I know the definitions of ai. 2. I can assess what advantages and disadvantages the use of an ai entails. 3. I can imagine possible future uses for ai. 4. I can operate ai applications in everyday life 5. In everyday life, i can work together gainfully with an artificial intelligence 6. I can tell if i am dealing with an application based on artificial intelligence. 7. I can distinguish whether i interact with an ai or a “real human”. 8. I can incorporate ethical considerations when deciding whether to use data provided by ai. |
| job performance | 1. Adequately completes assigned duties 2. Fulfills responsibilities specified in job description 3. Performs tasks that are expected of him 4. Meets formal performance requirements of the job 5. Engages in activities that will directly affect his performance evaluation 6. Neglects aspects of the job he is obligated to perform (r) 7. Fails to perform essential duties (r) |
measurement
summary
keywords
employee-ai collaboration, generative ai, role ambiguity, role theory, self-efficacy
citation
zhang q, zhang j and dong s (2026) dual pathways of generative ai use: role ambiguity and self-efficacy in employee-ai collaboration. Front. Psychol. 17:1793095. Doi: 10.3389/fpsyg.2026.1793095
received
21 january 2026
revised
30 june 2026
accepted
20 july 2026
published
14 august 2026
volume
17 - 2026
edited by
jesus de la fuente, university of navarra, spain
reviewed by
david aguado, universidad autónoma de madrid, spain
v. Viswanath shenoi, koneru lakshmaiah education foundation, india
updates
copyright
© 2026 zhang, zhang and dong.
this is an open-access article distributed under the terms of the creative commons attribution license (cc by). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*correspondence: qiannan zhang, 15620969646@163.Com
disclaimer
all claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. |
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| The DEI Industry Finally Found Its Icarus |
| Posted on Friday, August 14 @ 00:04:23 PDT (6 reads) | |
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Jason ardays resignation is not a personal tragedy. It is an indictment of an industry, in academia and in publishing, that stopped checking ids at the door.
jason arday resigned his cambridge professorship and his jesus college fellowship on august 5, hours after the telegraph revealed he had spent years claiming credit for a book he never wrote. He had listed himself as author of “being young, black and male: challenging the dominant discourse,” attributed to palgrave macmillan, in his own biography, conference material, and an essay on racism in higher education. Palgrave confirmed the manuscript was abandoned years ago and never published. That is not a footnote error. It is the claim that broke him, arriving atop a plagiarism finding, a fabricated fundraising total, and a police force that spent four months investigating the reporter who asked about it all.
what actually happened is simpler: due diligence, in three industries at once, showed up about a decade late.
arday became famous, and was made famous on purpose, as cambridges youngest black professor, appointed in 2023 at 37 on a life story built for daytime television: nonverbal until 11, illiterate into his late teens, autistic, then a phd and a cambridge chair. The bbc, cbs news, and good morning america ran the same redemption arc, none apparently checking the basics.
then a philosopher in belgium did the work journalism exists for. Nathan cofnas, a university of ghent researcher whose own cambridge fellowship ended in 2024 amid backlash over his writing on race and meritocracy, ran ardays 2015 thesis through plagiarism software and published what he found. Times higher educations jack grove had filed 97 pages of evidence with cambridge and liverpool john moores, which granted ardays phd. A statistical analysis commissioned by the telegraph found 188 sentences in that thesis identical or nearly identical to a 2009 thesis by paula zwozdiak-myers, the odds of the overlap calculated at one in a hundred billion.
here the press stops being a bystander and becomes a participant. When times higher education prepared to publish its findings last september, arday responded with carter-ruck, a london defamation firm, whose letter implied the coming story was racially motivated. The magazine spiked 10 months of reporting rather than risk a suit. A metropolitan police officer then called grove to say ardays mental health was suffering and he should stop, despite grove not having emailed him in four months. Scotland yard investigated grove for four months and closed the case with a warning attached. Commissioner sir mark rowley later admitted his force “dropped the ball.”
the personal claims kept unraveling on contact with a calendar. Arday said for years he had raised more than five million pounds for charity single-handedly. Last week he conceded the money actually came from a “syndicate of about 100 people,” none of whom he could name, citing non-disclosure agreements. His “30 marathons in 35 days” became a run he now says took 12 days, not six. Archival records for his fundraising page show £6,285. Ive spent two decades as an expert witness picking apart this kind of gap, and when the number shrinks every time someone checks, thats not an accounting quirk. Its the tell.
to be fair, liverpool john moores reviewed the thesis and called the overlap “honest and reasonable error,” a finding arday and more than 10,000 good law project petitioners now cite as vindication. Arday denies wrongdoing and blames inadequate supervision. That finding predates the telegraphs 188-sentence analysis and the fabricated-book revelation, and neither liverpool john moores nor cambridge has engaged with either since.
cambridges line, for months, was to call the criticism a “vile campaign.” That cracked on august 6, when the university opened a formal investigation into ardays qualifications, separate from the misconduct review already underway. Glasgow, where arday briefly worked, opened its own review. Many cambridge academics, organized by professor priyamvada gopal, then demanded an independent inquiry, including into whether the university helped deter journalists from covering the story. Cambridge agreed to fold its findings into a review of how it appoints senior academics.
meanwhile, the book came out anyway. Great and unfortunate things hit american shelves on august 11, reported a $1.4 million advance intact; the uk edition follows on august 27. The publicity tour didnt survive the week: an rgs fireside chat and an edinburgh bookshop event were both pulled “at the request of the speaker.” First reviews, from the telegraph, the times of london, and the atlantic, are savage, all three noting the memoir omits ardays most colorful, least checkable claims while adding new unverifiable ones. No sales figures exist yet; the book landed only this morning. Dont expect a press release to brag about the number.
thomas sowell spent half a century warning that group identity, treated as a substitute for individually verified achievement, doesnt elevate anyone. It delays the bill and hands it to the next scholar who actually earned the title, now facing suspicion cambridge invited by never picking up the phone. Diversity offices exist to widen the pool of candidates considered, not to exempt the winner from the background check everyone else survives. Cambridges own numbers make the stakes plain: just 0.6 percent of its academics with recorded ethnicity are black.
universities that want the publics trust back have an unglamorous fix: verify the cv before the name goes on the door. Newsrooms have an equally simple one: dont let a law firms letterhead decide what gets printed, and dont let a police phone call decide who gets to ask questions. Publishers have the simplest fix: fact-check the manuscript before you cut the check. All three had a decade to reach these conclusions quietly. They chose the humiliation instead. The book shipped anyway. The rest of us just watched the marathon.
jay rogers is a financial professional with more than 30 years of experience in private equity, private credit, hedge funds, and wealth management. He has a bs from northeastern university and has completed postgraduate studies at ucla, upenn, and harvard. He writes about issues in finance, constitutional law, national security, human nature, and public policy.
editor’s note: do you enjoy townhall’s conservative reporting that takes on the radical left and woke media? Support our work so that we can continue to bring you the truth.
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| California Community College Students Urged to Apply for Financial Aid Before Se |
| Posted on Friday, August 14 @ 00:04:23 PDT (5 reads) | |
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Expanded cal grant c eligibility now includes qualifying short-term career and vocational programs lasting 8 to 15 weeks
sacramento, calif. — California community college students still have time to apply for financial aid for the 2026-27 academic year, as state education officials urge current and prospective students to submit the free application for federal student aid (fafsa) or california dream act application (cadaa) by the sept. 2 cal grant deadline.
the california student aid commission (csac) and the california community colleges chancellor’s office, through its i can go to college campaign, are reminding students that the september deadline provides an important opportunity for community college students who have not yet completed a financial aid application.
submitting a fafsa or cadaa by sept. 2 is critical for eligible california community college students seeking consideration for a cal grant. State grants can help offset college costs including enrollment fees, books, supplies and living expenses.
students are encouraged to apply even if they are uncertain whether they qualify for assistance.
cal grant c expands to short-term career training
california’s 2026-27 state budget expanded cal grant c eligibility to include students enrolled in qualifying short-term career technical education and vocational programs lasting 8 to 15 weeks.
the expansion also extends eligibility to qualifying short-term noncredit programs, opening state financial aid opportunities to more californians seeking accelerated training for employment and career advancement.
the change broadens access to cal grant c for students pursuing occupational and technical education, including programs designed to prepare workers for high-demand fields.
“the september 2 deadline is a vital lifeline to affordable education and career advancement,” said daisy gonzales, executive director of the california student aid commission. “We urge all students and families to complete their fafsa or cadaa today, take advantage of our free cash for college webinars, and help us share this opportunity across every region of our state.”
“financial aid is a game-changer that opens doors to higher education and economic mobility for hundreds of thousands of community college students,” said california community colleges chancellor sonya christian. “Whether you are pursuing a degree, a transfer pathway, or a fast-track 8-to-15-week career training program, financial stability is the foundation of student success. California community colleges are breaking down barriers so that every learner, regardless of background or age, has a fair shot at achieving their educational and career goals.”
“expanding cal grant c to cover short-term vocational and career technical education programs is a monumental victory for california workers and our state’s economy,” said assemblymember david alvarez. “These programs give students the hands-on skills and practical training they need for in-demand jobs in health care, computer technology, and the vocational trades, without burying them in debt. I strongly encourage all students planning to attend a california community college this fall to apply for financial aid before september 2 so they can take full advantage of these expanded state resources.”
free help available for students and families
csac is offering free cash for college workshops and webinars to help students and families complete the fafsa or cadaa before the sept. 2 deadline.
the sessions provide step-by-step assistance from financial aid experts and opportunities for participants to ask questions. High school graduates, current college students, adult learners, returning students, foster youth and families are encouraged to participate.
how students and campuses can take action
- apply for financial aid: complete the fafsa at
fafsa.Govor the cadaa atdream.Csac.Ca.Govby sept. 2 for cal grant consideration at a california community college. - Attend a cash for college session: register for free fafsa and cadaa assistance through
csac.Ca.Gov/cash-for-.College - explore community college programs: visit
icangotocollege.Com for information about colleges, career education, financial aid and student support resources. Information is available in english, spanish, chinese, korean, vietnamese and tagalog. - Access outreach materials: colleges and community organizations can use csac’s sept. 2 deadline toolkit and cal grant c toolkit, including digital graphics, email templates, flyers and faqs, at
csac.Ca.Gov/toolkits.
students who have questions about their eligibility or individual circumstances should contact the financial aid office at the community college they attend or plan to attend.
the sept. 2 deadline gives california community college students who did not apply by the state’s earlier priority deadline another opportunity to seek cal grant assistance for the academic year.
about the california student aid commission
the california student aid commission serves more than 2.2 million students and administers more than $3.9 billion in student financial aid programs and services, including cal grants, the middle class scholarship and the california dream act application. The commission provides information and expertise on college affordability and financing and works to reduce financial barriers to higher education in california. For more information, visit csac.Ca.Gov. |
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| 2026 Sephora Accelerate Brand Omora Beauty Lab Makes Prestige Makeup For Pimple- |
| Posted on Friday, August 14 @ 00:04:23 PDT (5 reads) | |
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“i will probably never post this. I’m shaking. I think it’s a long shot, but fingers crossed,” jenna morales ledbetter, founder of omora beauty lab , said in a video recorded before learning whether she’d be selected for the 2026 sephora accelerate program. “I just wanted to remember how i was feeling.” Flash-forward to her next video, she’s still trembling, but for a different reason: she had just found out she was one of 12 founders selected for the incubator.
it was a full-circle moment for the 26-year-old founder who conceived the idea for omora—a combination of her mother’s maiden name, morales, and amor, the spanish word for love—after shopping at sephora, her favorite beauty retailer. On that trip, her go-to concealer was sold out. She spent nearly an hour scouring ingredients on her trusted sites, acneclinicnyc and incidecoder. Unfortunately, none of the available brand formulas met her criteria for her acne-prone, sensitive skin. “I wished i could walk into a store and find an entire line that i didn’t need to google to find the ingredients and know it would work for my skin,” she says.
she also had another goal. Ledbetter has a bachelor’s degree in psychology from pitzer college and a master’s in public mental health from the johns hopkins bloomberg school of public health. Before becoming a beauty creator, most of her career to date was in health research. Her background, combined with the impact acne and skin conditions have on mental health, including her own, prompted her to create products that could build confidence.
omora beauty lab launched direct-to-consumer in may with seven shades of $34 soft cover concealer, three colors of $30 sun kissed glow liquid bronzer and one shade of $30 rose flush lip and cheek tint—the line’s top seller. Instead of forging ahead with her plans to pursue a ph.D in health policy, especially with changes she saw in the public health landscape, ledbetter followed her passion. The result is omora, a clean, complexion-focused makeup brand for those with acne-prone and sensitive skin.
“i wanted to bring a level of prestige beauty to acne-prone, sensitive skin sufferers who struggle to find that,” she says, adding it was important to have a package consumers would be proud to display on vanities versus the often staid clinical solutions served up by derms. There is big potential in both acne and sensitive skin. Acne is the most common skin condition in the united states, affecting up to 50 million americans annually, according to the american academy of dermatology association. More than $5.3 billion is spent on prescription and over-the-counter acne products, per grand view research. Aveeno’s inaugural state of skin sensitivity report revealed 71% of adults feel they have some degree of sensitive skin.
while acne is typically associated with the teen years, ledbetter’s flare-up started in college. “It blew up seemingly overnight. I had red cystic acne. My entire face was covered,” she recalls. Adult acne, she notes, can be especially hard on young women’s mental health. “It’s one thing when you’re a teenager and everyone has it.” With a background in research, she approached her own skin the way she would any scientific problem. She immersed herself in dermatology studies, ingredient lists and emerging research on inflammation, the skin barrier and acne. As she experimented, one realization stood out: while acne treatments focused on aggressively stripping the skin, her damaged skin barrier needed exactly the opposite.
she says, “my dermatologist kept prescribing stronger and stronger active ingredients. My skin hurt every single day—not because of the acne itself, but because it was so dry and inflamed.” Ledbetter changed her diet, her exercise routine and even her laundry detergent. “I was constantly trying to figure out what was causing it.” She gradually shifted toward barrier-supporting skin care, eliminating fragrance, essential oils and pore-clogging ingredients. Finding skin care became manageable, but makeup was a bigger challenge. “I’ve spent so much money on my skin. It’s not worth it to experiment. I needed to come up with something completely clean for my skin,” she shares.
omora beauty lab founder jenna morales ledbetter as she set out to create her own, ledbetter encountered countless roadblocks from labs. “They said we can’t do what you want with the ingredients you want, or you have to order 10,000 of every shade. I was not willing to budge on my ingredients. I had a very clear list of what i wanted and what i didn’t want in my ingredients,” she said. “When manufacturers asked if i was a cosmetic chemist, i’d ‘no, i just had really bad acne and a lot of time to do research.’” Eventually, she found a small, woman-owned manufacturing partner willing to work alongside her rather than dismiss her vision. Together, they developed formulas that combined high-performance makeup with barrier-supporting ingredients like ceramides, squalane and green tea while excluding fragrance, essential oils and ingredients known to trigger breakouts.
omora beauty lab launched direct-to-consumer in may with seven shades of $34 soft cover concealer three colors of $30 sun kissed glow liquid bronzer and one shade of $30 rose flush lip and cheek tint—the line’s top seller. Ledbetter plans to expand her assortment but remain focused on complexion products. “I wouldn’t launch a mascara,” she said.
although omora was born from ledbetter’sown struggle with adult acne, the brand's customer base already stretches well beyond young adults. “My grandmother uses it every single day and is always telling me about the compliments she receives,” she added. Self-financed through a small friends-and-family round, ledbetter moved from the northeast to california, where she felt she had a better chance at making industry connections. She retains a job coaching at solidcore along with educational consulting and college essay tutoring as she builds her brand.
ledbetter has to be judicious in her spending, but she invested in an fda consultant at $400 per hour to ensure every label met regulatory requirements, money she said was well spent. The fda, she explained, has specific packaging requirements, from the size of ingredient lists relative to the package to the placement and orientation of the product name. “It wasn’t worth the risk to get it wrong,” she said, noting she changed her brand name from vertical to horizontal orientation to meet requirements and had to edit the font size and placement of her product specifications.
she also funneled her budget into dermatologist testing. “If people with acne-prone and sensitive skin are going to trust a new brand, the formulas have to be exceptional from day one.” Ledbetter is intentional with her social media investments. She’s gauging what works best and what doesn’t. For example, she learned that just because a big influencer mentions a brand, it doesn’t always translate into sales. She’s also weighing the impact of pop-ups and other grassroots efforts. Ledbetter shares omora’s behind the-scenes journey openly on social media, documenting both successes and setbacks in an effort to build transparency around the brand. She kicked off her campaign six months prior to launch to build exposure.
while not sharing her sales goals, ledbetter said she is inspired by cassandra morales thurswell, the founder and ceo of kitsch, who has grown her brand to a reported $500 million without any outside investment. After hearing thurswell speak at an event, ledbetter said her biggest takeaway was not to chase growth for growth’s sake or say yes to every opportunity, but to stay focused on building the company.
what she hopes to gain the most from the sephora accelerate is a support network and building relationships with other founders. “One of the big things is that i come from outside the beauty industry so i really want to hear from others who have experience and might be a few steps ahead of me,” she said. Learning what it means to be retail-ready is also mission critical. While she dreams of eventually landing in big retailers, she knows the importance of starting small. Several social media posts following the announcement about the latest sephora cohort singled her brand out for filling a gap in makeup for acne-prone skin.
since launching in 2016, sephora accelerate has helped elevate new founders across the beauty industry. Alumni brands include eadem, kulfi, topicals , bounce curl and oliviaumma . Members of this year’s cohort will gain access to the sephora impact summit and the chance to apply for the $100,000 sephora beauty grant. |
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| Texas College names first female president in 132-year history |
| Posted on Friday, August 14 @ 00:04:23 PDT (6 reads) | |
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Texas college names first female president in 132-year history
dr. Sherrhonda gibbs launches $50 million campaign and new academic programs at tyler hbcu
tyler, texas (kltv) - dr. Sherrhonda gibbs has been named the first female president in texas college’s 132-year history, a milestone marked thursday at a ceremony at the mayfair building in tyler.
“today is a new day for texas college,” gibbs said at the event.
background and experience
gibbs is not new to historically black colleges and universities. She attended an hbcu herself and most recently led the business division at morehouse college, where she helped raise more than $30 million for students. She also attended college as a first-generation student — the fourth child in a single-parent home with six children.
“it’s deeply humbling to be the first female president of texas college,” gibbs said. “I think about all of the women who made great sacrifices for this day to be possible. And i’m going to work diligently and extremely hard to make them proud.”
gibbs said her personal background informs her approach to the challenges students face.
“it was not easy,” she said. “But i bring that experience and understanding the hurdles that our students face. And i know what’s needed to help them succeed.”
financial challenges facing hbcus
hbcus across the country face financial pressure. Nationwide, 80% of hbcu students rely on federal loans to afford school, compared to 55% at non-hbcu schools.
to address funding needs, gibbs said she plans to pursue corporate partnerships as a primary strategy.
“one of the ways i plan to raise money for texas college is through corporate partnerships,” she said. “That’s a key effort that i used at morehouse college to get funding for our co-curricular programs and academic programs that our employers were interested in seeing and having our students become more prepared for the workforce.”
capital campaign and scholarships
gibbs said she is also launching a major fundraising effort.
“i’m partnering with development and advancement professionals to commence a $50 million capital campaign specifically for texas college — to get scholarships for our students, new facilities for our athletics programs, and development opportunities for our faculty and staff,” she said.
she said the college will also revamp its website to make scholarship opportunities more visible to students.
“we’re going to do a complete revamp of our website so students are aware of the scholarship opportunities that they may have available, particularly not just from texas college, but uncf and external organizations,” gibbs said.
gibbs also announced plans for a new center for financial and professional excellence focused on financial literacy.
“we’re going to start a new center for financial and professional excellence that will focus on financial literacy so that our students who do have to apply for and receive financial aid, particularly federal student loans, are ready to handle those loans and that debt proficiently when they get into the workforce,” she said.
new academic programs and initiatives
gibbs outlined a series of new academic programs planned for texas college, including supply chain management and logistics, finance and asset management, math education, and allied health — including associate degrees and credentialing programs.
she also described a new initiative called work integrated learning.
“one of our key strategic initiatives is called work integrated learning, where we affiliate workplace practices into every part and aspect of our curriculum,” gibbs said. “Students will get internships, experiential opportunity, applied learning opportunities, and service learning so that they’re ready for the workforce on day one.”
call for community partnership
at the ceremony, gibbs called on the tyler community to engage with the college.
“we’re ready and willing to partner with those who want to partner with us,” she said. “We need your support. And i’m ready to partner with you at any time that you present the opportunity to us.”
she added: “i stand on the shoulders of brave women, and i hope to live up to that greatness and leave the door open for women and young people who follow behind me.”
smith county commissioners court declared august 13 as dr. Sherrhonda gibbs day in smith county. Gibbs officially begins her role this school year.
copyright 2026 kltv. All rights reserved. |
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| Texas College names first female president in 132-year history |
| Posted on Friday, August 14 @ 00:04:23 PDT (4 reads) | |
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Share add us on google add as a preferred source on google tyler, texas (kltv) - dr. Sherrhonda gibbs has been named the first female president in texas college’s 132-year history, a milestone marked thursday at a ceremony at the mayfair building in tyler.
“today is a new day for texas college,” gibbs said at the event.
background and experience gibbs is not new to historically black colleges and universities. She attended an hbcu herself and most recently led the business division at morehouse college, where she helped raise more than $30 million for students. She also attended college as a first-generation student — the fourth child in a single-parent home with six children.
“it’s deeply humbling to be the first female president of texas college,” gibbs said. “I think about all of the women who made great sacrifices for this day to be possible. And i’m going to work diligently and extremely hard to make them proud.”
gibbs said her personal background informs her approach to the challenges students face.
“it was not easy,” she said. “But i bring that experience and understanding the hurdles that our students face. And i know what’s needed to help them succeed.”
financial challenges facing hbcus hbcus across the country face financial pressure. Nationwide, 80% of hbcu students rely on federal loans to afford school, compared to 55% at non-hbcu schools.
to address funding needs, gibbs said she plans to pursue corporate partnerships as a primary strategy.
“one of the ways i plan to raise money for texas college is through corporate partnerships,” she said. “That’s a key effort that i used at morehouse college to get funding for our co-curricular programs and academic programs that our employers were interested in seeing and having our students become more prepared for the workforce.”
capital campaign and scholarships gibbs said she is also launching a major fundraising effort.
“i’m partnering with development and advancement professionals to commence a $50 million capital campaign specifically for texas college — to get scholarships for our students, new facilities for our athletics programs, and development opportunities for our faculty and staff,” she said.
she said the college will also revamp its website to make scholarship opportunities more visible to students.
“we’re going to do a complete revamp of our website so students are aware of the scholarship opportunities that they may have available, particularly not just from texas college, but uncf and external organizations,” gibbs said.
gibbs also announced plans for a new center for financial and professional excellence focused on financial literacy.
“we’re going to start a new center for financial and professional excellence that will focus on financial literacy so that our students who do have to apply for and receive financial aid, particularly federal student loans, are ready to handle those loans and that debt proficiently when they get into the workforce,” she said.
new academic programs and initiatives gibbs outlined a series of new academic programs planned for texas college, including supply chain management and logistics, finance and asset management, math education, and allied health — including associate degrees and credentialing programs.
she also described a new initiative called work integrated learning.
“one of our key strategic initiatives is called work integrated learning, where we affiliate workplace practices into every part and aspect of our curriculum,” gibbs said. “Students will get internships, experiential opportunity, applied learning opportunities, and service learning so that they’re ready for the workforce on day one.”
call for community partnership at the ceremony, gibbs called on the tyler community to engage with the college.
“we’re ready and willing to partner with those who want to partner with us,” she said. “We need your support. And i’m ready to partner with you at any time that you present the opportunity to us.”
she added: “i stand on the shoulders of brave women, and i hope to live up to that greatness and leave the door open for women and young people who follow behind me.”
smith county commissioners court declared august 13 as dr. Sherrhonda gibbs day in smith county. Gibbs officially begins her role this school year.
copyright 2026 kltv. All rights reserved. |
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| AI Boot Camps: Preparing Unemployed Youth for Future Jobs |
| Posted on Friday, August 14 @ 00:04:23 PDT (5 reads) | |
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The uk government is launching a pilot program in north-west england to train 70 young people, aged 16 to 21, in artificial intelligence tools to help them secure apprenticeships. The initiative targets “neets”—individuals not in education, employment, or training—by teaching them to build ai tools and navigate business systems, with a wider national rollout planned for next summer.
addressing the youth unemployment crisis
data published in may showed that the number of young people aged 16 to 24 neither working nor learning reached 1.01 million between january and march, the highest figure since 2013. According to an interim report by former labour minister alan milburn, this number could rise to one in six young people by 2031, potentially creating a “lost generation” of workers.
lisa nandy, the digital, culture, media and sport secretary, stated that the scheme is designed to “support young people at a crucial juncture in their lives.” Participating businesses, including bae systems and heinz, are expected to offer apprenticeships to the majority of those who complete the three-week training course.
did you know?
the pilot program will utilize ai platforms from major tech developers, including microsoft, openai—the creator of chatgpt—and anthropic, the startup behind the claude chatbot.
bridging the skills gap in an ai-driven workplace
the curriculum focuses on both technical ai proficiency and traditional workplace skills, such as timekeeping and office it systems. Kanishka narayan, the uk’s ai minister, noted that the program intends to provide young people with the specific skills required to “thrive in the ai workplace.”
however, industry experts maintain a cautious outlook. Bouke klein teeselink, an academic at king’s college london, described the boot camp as a “good idea in principle.” He noted that increasing the productivity of junior workers by teaching them how to use ai could make them more attractive to employers. Yet, teeselink expressed skepticism that a three-week duration is sufficient for long-term “ai-readiness,” arguing that the technology demands a process of continuous learning.
market vulnerability and future trends
concerns regarding ai’s impact on the labor market have increasingly centered on school leavers and graduates. Many analysts fear that ai tools are capable of performing the “grunt work” typically assigned to new entrants in white-collar sectors like law, finance, and marketing. Recent data from stanford university indicates a decline in employment for young workers in ai-exposed fields, such as customer service and software engineering.
the pilot program is used to refine an england-wide strategy.
frequently asked questions
- who is eligible for the ai boot camp? The pilot is currently for 70 people aged 16 to 21 in north-west england.
- what skills will students learn? The curriculum includes building ai tools, understanding business applications of ai, and core workplace skills like it system management.
- when will the national program begin? The government plans to roll out an england-wide ai skills scheme next summer, following the results of this pilot.
- which companies are involved? Participating employers include bae systems and heinz, among others.
are you interested in how ai is reshaping the job market? Subscribe to our newsletter for weekly updates on workforce trends and education policy. |
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| Private School Valuation: What Owners Should Know |
| Posted on Friday, August 14 @ 00:04:23 PDT (6 reads) | |
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What is my private school worth? How buyers actually value a school
most owners want one number: what is my school worth. The honest answer is that a private school is not valued the way a manufacturing business or a commercial building is. It is worth a multiple of its earnings, yes, but that multiple is shaped by the durability of your enrollment, the strength of your programs, your regulatory standing, and how much of the schools success walks out the door if you leave. Get that reading right and you price the school fairly. Get it wrong and you either leave money on the table or scare off the buyer who would have paid the most.
i have sat across the table from owners who were certain their school was worth one number and buyers who were just as certain it was worth another. The truth almost always lives in between, and it is knowable.
over more than twenty years advising owners of private schools, boarding schools, career colleges, and language institutes through the sale of their lifes work, i have watched buyers overpay for schools that looked stronger on paper than they were, and i have watched owners accept far less than their school could have commanded because no one framed the value properly. What follows is the same ground i cover with owners before they go to market: the methods buyers actually use, and what pushes a valuation up or down.
why is valuing a private school different from other businesses?
most business valuations rest on a clean relationship between earnings and value. You apply a multiple to profit, adjust for growth and risk, and you have a working estimate. A school carries that logic and then layers real complexity on top of it.
the first layer is that your enrollment is a community, not a customer list. Families chose your school for its culture, its people, and the other families in it. If a buyer changes those things after closing, families leave. An enrollment base that looks rock solid in the data room can thin out in a single admissions cycle if the transition is handled carelessly. I have seen buyers treat tuition like subscription revenue that renews on its own, and then be genuinely surprised when it does not.
the second layer is that quality and value move together. In most businesses you improve margin by cutting costs. In a school, cutting the wrong costs, losing your best teachers or deferring maintenance, damages the very reputation your enrollment depends on. Value in a school is protected by holding quality, not by squeezing it.
the third layer is regulatory standing. Private schools operate under provincial and state oversight, and career colleges carry designation and funding-eligibility requirements that can be suspended or pulled. That standing is a real part of what your school is worth and deserves the same scrutiny as your financial statements.
what methods do buyers use to value a private school?
three approaches do most of the work in private school m&a, and a good valuation usually triangulates across all three.
the earnings multiple. This is the most common method. A buyer takes your normalized ebitda, usually the trailing twelve months adjusted for one-time items and owner-specific expenses, and applies a multiple that reflects your size, quality, growth, and risk. For smaller independent schools and training centers, published market data puts the range around four to eight times ebitda, with accreditation, stable enrollment, and a strong reputation pushing toward the top of that band. Larger, well-run schools and multi-campus operators command more. Across education services broadly, multiples have come down from the highs near thirteen times seen in 2021 but remain elevated, supported by private equity buyers sitting on more than two and a half trillion dollars of capital they need to deploy. Treat any multiple you hear as a starting point, not a promise. It has to be tested against your specific numbers.
asset-based value. If you own your real estate, the property is a distinct source of value that should be assessed on its own. Many transactions now split the two, letting the real estate and the operating school be valued and financed independently. School property in a good location, with a long lease to the operating entity, often carries a premium over ordinary commercial real estate because the income is stable and the use is essential. If you lease your premises, this piece matters far less and your value rests mostly on earnings.
discounted cash flow. For an established school, dcf usually serves as a cross-check rather than the headline number. It earns its keep with newer schools that have a short earnings history but a credible enrollment trajectory, where you model growth to a stabilized occupancy and discount at a rate that reflects the risk of a young operation.
what makes a private school worth more?
buyers pay premiums for schools that reduce their risk. In my experience the biggest driver is enrollment depth and durability. A school with a waitlist, high sibling and re-enrollment rates, and steady numbers through good years and bad is worth considerably more than one that has to rebuild its class every spring. Everything else on the financial statement rests on that foundation.
accreditation and program strength come next. Recognized accreditation and a respected curriculum reduce a buyers diligence burden and reassure parents that nothing fundamental is changing after the sale. Pricing power matters too. A school charging below what its quality and market would support hands the buyer an obvious path to growth, while a school already at the ceiling of what its families will pay has less room to run.
then there is the question of whether the school can run without you. If performance depends on a single founder who knows every family by name, a buyer sees risk. Schools with real institutional strength, distributed leadership, and documented systems command higher prices. Clean regulatory standing and modern, well-kept facilities round out the picture, because both reduce the surprises and the capital a buyer has to absorb after closing.
what drags a valuation down?
the same logic runs in reverse. Enrollment concentrated in one employer, industry, or narrow community is fragile, because if that source contracts the school does too. Heavy dependence on the owner is a consistent discount, especially when families are loyal to a person rather than an institution. Regulatory friction makes a buyer cautious with price. And deferred maintenance hidden inside a healthy-looking income statement is a liability waiting to surface. A school that looks profitable but needs a new roof to stay competitive is worth less than its headline earnings suggest.
what does a private school buyer look at in due diligence?
a credible valuation demands more than the financial statements. Expect a buyer to want at least five years of enrollment data broken down by grade, source, and payment record, with re-enrollment, waitlist depth, and sibling rates weighing more than the headline student count. Expect a full review of licenses, designations, funding eligibility, and inspection history, and scrutiny of teacher turnover and contract terms, because turnover is a leading indicator of problems that never show up in the accounts. Parent satisfaction data and a clear read on local competition complete the file. None of this should feel adversarial. The owners who prepare this material in advance almost always sell faster and for more.
frequently asked questions
what multiple does a private school sell for? For smaller independent schools and training providers, market data commonly points to roughly four to eight times normalized ebitda, with well-established and multi-campus operators trading higher. The right number for your school depends on enrollment durability, accreditation, and how transferable the operation is. A multiple is only meaningful once it is tested against your actual figures.
how long does it take to sell a private school? A well-prepared process typically runs from several months to a bit over a year, depending on the school, the buyer pool, and the diligence involved. Preparation before you go to market is what shortens the timeline.
should i sell the real estate with the school? Not necessarily. Owned property is often valued and sold separately from the operating school, which can widen your buyer pool and improve your total proceeds. It is worth modeling both structures before you decide.
do i need a valuation before i list? Yes. Knowing what your school is realistically worth, and why, lets you set expectations and negotiate from strength rather than react to a buyers first number.
lets talk about what your school is worth
understanding what your school is worth today, and what it could be worth in the right hands, are two different questions, and the gap between them is often where a well-run sale creates real value. If you are thinking about selling, or simply want a grounded read on where your school stands, i would welcome a confidential conversation.
for a deeper look at valuation and the sale process, see our guidance on how to value and sell your school, how to prepare your school for sale, and the steps to sell your school.
reach us directly and in confidence at info@halladayeducationgroup.Com and 1.800.687.1492. |
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