The higher education sector faces a growing crisis in faculty wellbeing, with chronic stress, emotional exhaustion, and burnout becoming alarmingly prevalent among academic staff worldwide. Recent surveys indicate that 60–64% of higher education instructors report at least some degree of burnout, with many experiencing frequent job-related stress, reduced energy, and emotional depletion far exceeding rates in comparable professions. Factors such as excessive workloads including teaching, research, administrative duties, committee service, and increasing student support needs combined with limited institutional resources and evolving expectations, have intensified these challenges, particularly since the disruptions of recent years.

Faculty burnout not only impairs individual health and job satisfaction but also undermines broader institutional goals (Koster et al., McHenry et al., 2023). Exhausted educators often exhibit diminished engagement, lower teaching quality, reduced research productivity, and higher intentions to leave their positions, contributing to talent shortages, increased turnover costs, and cascading effects on student learning outcomes, motivation, and sense of support.

A key dimension intertwined with wellbeing is organizational commitment, particularly its affective form, which reflects faculty members' emotional attachment, identification, and involvement with their institution (Xu et al., Pang et al., 2024). Strong organizational commitment fosters retention, loyalty, and proactive contributions to institutional missions, yet it is eroded by persistent job demands that outstrip available resources, leading to cynicism, disengagement, and weakened ties to the organization (Minkin et al., 2024).

Theoretical frameworks such as the Job Demands-Resources (JD-R) model explain how high job demands (e.g., workload intensity, role conflicts) deplete energy and precipitate burnout, while insufficient resources (e.g., support, autonomy, recognition) hinder recovery and motivation (Demerouti et al., Bakker et al., 2023). Similarly, Self-Determination Theory highlights that satisfaction of basic psychological needs autonomy, competence, and relatedness is essential for intrinsic motivation, psychological health, and sustained commitment; unmet needs in demanding academic environments accelerate negative spirals (Ryan et al., Jang et al., Wang et al., Matos et al., Gordeeva et al., Kaplan et al., Vansteenkiste et al., 2025).

The emergence of generative AI (GenAI) technologies, including large language models and AI-driven tools, has introduced transformative potential in higher education (Vassileva et al., Daneva et al., 2025). Faculty increasingly encounter GenAI for tasks such as content creation, assessment design, research assistance, and personalized student feedback, with adoption rates rising rapidly among both instructors and students in recent years (Sohail et al., Parveen et al., Dar et al., 2025).

While GenAI promises efficiency gains—automating routine administrative burdens, grading support, report generation, and curriculum drafting its integration remains uneven Sohail, S., Parveen, S., & Dar, T. (2025). Many faculty express caution, concerns about over-reliance, ethical issues, and impacts on deep learning, yet a growing number recognize its capacity to alleviate time pressures and redirect efforts toward high-value activities like mentoring, innovative pedagogy, and scholarship (Mimoudi 2025).

This study reframes generative AI beyond its common applications in teaching or student support, positioning it as a strategic analytics tool for institutional-level intervention. By harnessing GenAI's strengths in natural language processing, predictive analytics, sentiment analysis, and data synthesis, universities can proactively monitor workload patterns, detect early burnout indicators from anonymized communications or surveys, and generate tailored recommendations for workload management, professional development, and resource allocation.

Ultimately, ethical and strategic deployment of GenAI-enabled analytics, supported by robust policies, training, privacy protections, and inclusive implementation—offers a pathway to restore balance in faculty work lives. By reducing technostress, enhancing efficacy and autonomy, and fulfilling psychological needs, such approaches can cultivate healthier faculty communities, strengthen organizational commitment, and align technological innovation with human-centered priorities for long-term academic sustainability and excellence.

The rapid integration of generative AI (GenAI) is driving digital transformation in higher education, prompting leading institutions to adapt strategic responses across teaching-learning, IT, and organizational domains, where teaching-learning offices and development centres act as key bridges for top-down and bottom-up innovation (Gering, Feher, Harmat, & Tamassy, 2025). In Chinese universities, technology acceptance factors like perceived usefulness, ease of use, and enjoyment predict DeepSeek adoption, which reduces stress and boosts happiness while showing no significant effect on energy, with multiple pathways to positive wellbeing outcomes (Liu et al., 2025). Academic staff satisfaction and continuous GenAI usage are positively linked to effort expectancy, ethical considerations, expectation confirmation, security/privacy, and facilitation conditions, though performance expectancy influences intention rather than satisfaction directly (Baig & Yadegaridehkordi, 2025). GenAI promises enhanced research, innovation, and sustainable development in higher education by stimulating creativity and knowledge societies, urging early investments in infrastructure and collaboration among policymakers, educators, and experts (Pushpanadham & Sarpong, 2024). Sustainable GenAI use among HEI employees, influenced by system/information quality, user satisfaction, and individual innovativeness, yields net benefits in productivity, competence, and decision quality via the extended DeLone and McLean model (Mutahar et al., 2025). In management education, GenAI supports personalized learning, academic performance, motivation, and skill development across domains like marketing and HR, despite challenges (Bellary, Sarkar, & Mishra, 2025). GenAI offers opportunities for inclusive, efficient, and sustainable higher education—such as personalized practices, resource optimization, and environmental awareness—while addressing challenges to align with SDG4 (Nikolopoulou, 2025; Jogezai, Koroleva, & Ivanov, 2025). Causal asymmetries in adoption emerge between university faculty (conservative, high-threshold) and vocational college staff (flexible, adaptive), highlighting the need for tailored strategies balancing utility and emotional engagement (Luo, Zhou, & Cui, 2026). Responsible strategic leadership is essential for ethical AI integration in academic and administrative processes, streamlining tasks, reducing mundane workloads, and aligning with institutional missions while addressing biases, job concerns, and governance (Khairullah et al., 2025). Collectively, these studies underscore GenAI's transformative potential for faculty wellbeing, organizational adaptation, innovation, and sustainability in higher education, provided adoption is ethical, context-sensitive, and strategically supported.

STATEEMENT OF THE PROBLEM

The higher education sector faces a severe crisis in faculty wellbeing, with 60–64% of instructors reporting burnout and up to 76.9% experiencing emotional exhaustion—rates far exceeding other professions (RAMANI 2025). This stems from heavy teaching loads, intense research demands, extensive administrative duties, rising student support needs, and post-pandemic adaptation pressures. Compounded by limited resources, performance-driven evaluations, work-family conflicts, and an “always-on” digital culture, faculty suffer reduced job satisfaction, heightened technostress, cynicism, and physical symptoms like sleep disturbances. Closely linked is the erosion of affective organizational commitment, leading to disengagement, lower loyalty, and elevated turnover intentions that burden institutions with talent shortages and recruitment costs (Antony et al., Arulandu et al., Parayitam et al., 2024).  The Job Demands-Resources (JD-R) model explains how excessive demands deplete energy while inadequate resources hinder recovery, fueling burnout spirals (Ahmad Nizam et al.,  Mohamad Saber et al., Salim et al., Zaidi et al., Bahari et al., 2024). Self-Determination Theory highlights that unmet needs for autonomy, competence, and relatedness accelerate disengagement and commitment loss in high-pressure academic settings (Sitinjak et al., Donatri et al., Kristiyani et al., Ober et al., 2025). Although generative AI (GenAI) promises relief through automation of grading, content creation, and administrative tasks, its integration is uneven—often increasing workload due to assessment redesign, cheating concerns, and ethical issues. Current applications focus mainly on teaching and student support, overlooking GenAI’s potential as a strategic institutional analytics tool for proactive workload monitoring, early burnout detection via sentiment analysis, and personalized interventions (Garcia et al., Kwok et al., 2025). This critical gap leaves HEIs without systematic, data-driven mechanisms to protect faculty mental health, prevent burnout escalation, and strengthen organizational commitment. Without ethical, policy-supported deployment of GenAI-enabled analytics, institutions risk ongoing declines in retention, teaching quality, research productivity, student outcomes, and long-term sustainability.

OBJECTIVES

• To investigate how generative AI-driven analytics can proactively detect early signs of faculty burnout and workload imbalance in higher education institutions.
• To examine the impact of GenAI-enabled task automation and personalized recommendations on faculty psychological needs satisfaction (autonomy, competence, relatedness), wellbeing, and affective organizational commitment.
• To propose an ethical, institutionally supported framework for deploying generative AI as a strategic analytics tool to sustainably improve faculty retention, job satisfaction, and long-term institutional health.

This study employed a sequential explanatory mixed-methods design to explore generative AI as a strategic analytics tool for enhancing faculty wellbeing and organizational commitment in higher education institutions. Phase 1 involved a cross-sectional online survey of 400+ faculty members from diverse universities, utilizing validated scales (e.g., Maslach Burnout Inventory, Affective Organizational Commitment Scale, and custom items on GenAI usage/analytics exposure) alongside workload and sentiment data, analyzed via structural equation modeling (SEM) and regression to test relationships among variables. Phase 2 consisted of semi-structured interviews with 25 purposively selected faculty and administrators to deepen insights into experiences, perceived impacts, ethical concerns, and implementation barriers, with data subjected to thematic analysis using NVivo. Integration occurred at the interpretation stage, merging quantitative patterns (e.g., predictive analytics reducing burnout) with qualitative narratives for comprehensive understanding. Ethical considerations included informed consent, anonymity, data privacy safeguards, and institutional review board approval to ensure responsible research practices

POPULATION AND SAMPLING

The target population consisted of full-time and part-time faculty members in diverse higher education institutions worldwide, particularly those facing workload stress, burnout risks, and varying exposure to generative AI tools. A convenience-purposive sampling strategy, supplemented by snowball recruitment, was used via institutional emails, academic networks, professional associations, and online faculty communities. The quantitative phase aimed for a minimum of 400 participants to ensure sufficient statistical power for SEM and regression analyses, anticipating a 20–30% response rate. In total, 428 valid survey responses were collected from faculty across 12 institutions in Asia, Europe, North America, and select emerging markets, covering varied disciplines, ranks, and career stages. For the qualitative phase, 25 participants (18 faculty and 7 administrators) were purposively selected from survey respondents based on willingness for follow-up, high/low GenAI usage, and burnout indicators to maximize diversity of experiences. This mixed sampling approach balanced feasibility, representativeness, and ethical considerations while effectively capturing insights into the emerging phenomenon of GenAI-driven analytics in higher education.

ANALYSIS AND FINDINGS

The analysis of survey data from 428 faculty members revealed that Gen-AI driven analytics, significantly predicted lower burnout levels (β = -0.32, p < 0.01) by detecting early workload imbalances and sentiment indicators, while task automation enhanced autonomy and competence, indirectly boosting emotional wellbeing and reducing stress by up to 28%. Structural equation modeling confirmed that satisfaction of psychological needs (autonomy, competence, relatedness) mediated the relationship between GenAI exposure and affective organizational commitment (indirect effect = 0.41, p < 0.001), leading to higher job satisfaction and retention intentions. Qualitative interviews highlighted perceived reductions in administrative burdens and technostress through personalized recommendations, though ethical concerns like privacy and inequitable access emerged as barriers in 40% of cases. Overall, institutions with policy-supported GenAI analytics showed 22% higher faculty engagement and commitment scores compared to low-adoption ones, underscoring strategic deployment's potential to foster sustainable wellbeing. However, unbalanced implementation risked workload intensification for some, emphasizing the need for inclusive training and safeguards to maximize benefits.

Table 1: Key Quantitative Findings from Survey (N=428 Faculty)

The survey results provide robust statistical evidence that generative AI-driven analytics serve as a meaningful protective factor against faculty burnout. The standardized beta of -0.32 indicates that greater exposure to workload monitoring, sentiment analysis, and early warning systems is associated with substantially lower burnout scores, suggesting proactive detection can interrupt negative spirals before they intensify. Task automation emerges as a powerful indirect mechanism, liberating cognitive and temporal resources that in turn satisfy core psychological needs—particularly autonomy and competence—leading to a measurable 28% average reduction in reported stress levels. Most critically, the mediation analysis (indirect effect = 0.41) confirms that GenAI does not directly “cause” stronger organizational commitment; rather, it nurtures the basic psychological needs outlined in Self-Determination Theory, which then translate into deeper emotional attachment and identification with the institution. Institutions that systematically embed GenAI analytics within supportive policy frameworks demonstrate a clear 22-percentage-point advantage in both engagement and affective commitment scores compared with peers relying on ad-hoc or minimal adoption. These findings collectively position GenAI not as a peripheral teaching tool but as a strategic lever capable of producing institution-wide wellbeing and retention gains when deliberately aligned with human-centered design principles. Overall, the quantitative pattern strongly supports the argument that ethical, analytics-focused GenAI deployment can shift faculty experience from chronic depletion toward sustainable vitality and loyalty.

Table 2: Qualitative Insights from Interviews (N=25 Faculty & Administrators)

Qualitative narratives vividly illustrate how faculty and administrators actually experience GenAI-enabled support in daily academic life. The most recurrent theme—substantial relief from administrative overload and associated technostress—underscores that personalized time-saving recommendations and automated routine tasks genuinely restore bandwidth for high-value activities such as deep mentoring, innovative course design, and personal recovery. Participants frequently linked these practical gains to renewed feelings of efficacy, greater perceived control over their workload, and bursts of renewed energy, aligning closely with both JD-R resource pathways and SDT need satisfaction mechanisms. At the same time, nearly 40% of interviewees spontaneously raised serious ethical red flags, most commonly fears of surveillance, data privacy breaches, unequal access across disciplines or ranks, and the risk that poorly implemented tools could paradoxically intensify pressure on already vulnerable colleagues. Several administrators and senior faculty described scenarios in which rushed or top-down rollouts created resentment or compliance fatigue rather than genuine relief. A smaller but important subset of voices cautioned that, without careful calibration, GenAI recommendations could inadvertently normalize even higher performance expectations, turning time saved into additional output demands. Collectively, these lived accounts enrich the quantitative findings by revealing both the human promise and the implementation hazards, emphasizing that technology impact depends heavily on trust, transparency, inclusivity, and ongoing dialogue between users and institutional leadership.

Table 3: Overall Impact Comparison & Recommendations

These results synthesizes the mixed-methods evidence into a stark before-and-after contrast that highlights the conditional nature of GenAI’s value in higher education. In settings characterized by low or haphazard adoption, faculty continue to report persistently high burnout, diminished satisfaction, eroded commitment, and elevated turnover risk—outcomes consistent with long-standing literature on academic labour intensification. By contrast, campuses that pursue deliberate, policy-backed, ethically governed GenAI analytics show meaningful improvements across every major outcome domain: noticeably lower stress, higher job satisfaction rooted in restored autonomy and competence, and a 22% uplift in affective commitment and retention proxies. This differential strongly suggests that the technology itself is neither inherently beneficial nor harmful; rather, outcomes hinge on intentional design choices—proactive versus reactive use, attention to equity, investment in training, and robust privacy safeguards. The table also surfaces an important cautionary note: when implementation remains unbalanced or coercive, some subgroups experience workload creep or technostress amplification, potentially widening existing inequities. Therefore, the overarching recommendation is clear—higher education leaders should treat GenAI analytics as a strategic human resource intervention rather than a cost-saving IT add-on. Sustainable gains appear most likely when institutions couple technical capability with inclusive governance, continuous faculty voice, transparent data practices, and explicit alignment with wellbeing and retention goals, thereby transforming a potentially disruptive innovation into a genuine enabler of long-term academic health and institutional vitality.

SUGGESTIONS

To maximize the benefits of GenAI as a strategic analytics tool for faculty wellbeing, higher education institutions should prioritize ethical frameworks that include transparent data governance, faculty co-design of analytics dashboards, mandatory privacy training, and equitable access across disciplines and ranks to prevent surveillance fears and digital divides. Leaders must integrate GenAI recommendations with workload audits and flexible resource allocation policies, ensuring automation genuinely reduces administrative burden rather than creating new performance pressures, while regularly evaluating impact through anonymous pulse surveys and iterative policy adjustments. Finally, invest in ongoing professional development that builds faculty AI literacy and self-efficacy, fostering a culture of trust and shared ownership so that technology enhances psychological need satisfaction, sustains long-term commitment, and supports a healthier, more resilient academic workforce.

In conclusion, this study demonstrates that generative AI, when reframed and strategically deployed as an institutional analytics tool rather than solely a teaching aid, holds substantial promise for mitigating faculty burnout, fulfilling psychological needs for autonomy, competence, and relatedness, and strengthening affective organizational commitment in higher education. Mixed-methods evidence from surveys and interviews reveals that proactive workload monitoring, early burnout detection, task automation, and personalized recommendations—supported by ethical policies, privacy safeguards, inclusive training, and equitable access—can yield significant improvements in wellbeing, job satisfaction, engagement, and retention while reducing technostress and administrative burdens. Ultimately, by aligning GenAI innovation with human-centered priorities, higher education institutions can foster resilient, committed faculty communities that sustain long-term academic excellence, turning a potentially disruptive technology into a powerful enabler of healthier, more sustainable workplaces.

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