Dual effects of AI on academic work performance: A job demands-resources theory systematic review
Sheref Betabo Gogsido, Matewos Kebede Refera, Abdilkerim Seman Asrar, Baqer Khudair Al-HadrawiBackground
Artificial intelligence (AI) is rapidly reshaping academic work, yet its occupational health implications for academicians remain under-examined. The Job Demands-Resources (JD-R) framework offers a dual lens for understanding AI as both a performance-enhancing resource and a source of job demands, but no systematic review has applied this framework across the full spectrum of academic roles.
Objective
This systematic review examines how AI impacts academicians’ performance across four core domains—teaching, research, community engagement, and professional development—using the JD-R framework to understand AI's dual nature as both a resource and a demand.
Methods
Following PRISMA guidelines and the five-stage framework of Khan et al.,
Results
Teaching performance dominates the literature (70%), reflecting AI's immediate impact on instructional practices. AI functions as a job resource through enhanced personalized learning, assessment automation, and administrative efficiency when supported by self-efficacy, AI literacy, and institutional resources. However, AI creates significant job demands including academic integrity management, assessment redesign, continuous upskilling, and ethical dilemmas. Professional development (27%) exhibits the most severe demand-resource imbalance, with faculty experiencing skill obsolescence anxiety, technostress, and imposter syndrome. Research performance (20%) shows a relatively balanced profile, while community engagement (7%) remains critically understudied. Identity-threatening demands—academic integrity in teaching, authorship ambiguity in research, and skill obsolescence in professional development—generate disproportionate psychological strain requiring specialized buffers.
Conclusions
AI's impact on academic work is inherently dualistic, with outcomes determined by the balance between available resources and demand intensity. Psychological resources, particularly self-efficacy, emerge as the most powerful enablers mediating the effectiveness of institutional and knowledge resources. The review extends JD-R theory by identifying identity-threatening demands as a distinct demand category and proposing a Resource Cascade Model wherein distal organizational resources foster proximal psychological resources. Institutions must adopt systemic, autonomy-supportive interventions addressing the full spectrum of AI's impact on academic work.