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Reconceptualizing of GenAI adoption in higher education: A task-based perspective

Marwa Chaieb et al.

The International Journal of Management Education2026https://doi.org/10.1016/j.ijme.2026.101365article
AJG 1ABDC B
Weight
0.50

What the paper says

The quick incorporation of generative AI (GenAI) in academia triggers essential discussions regarding its role in educational methodologies. Despite extensive research on student acceptance via models like Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), the educator's viewpoint is insufficiently examined, despite its significance in pedagogical development. This qualitative study aims to address this gap by examining GenAI adoption in relation to task characteristics, pedagogical identity, institutional support, and ethical concerns. By conducting semi-structured interviews with 20 educators from Europe and North Africa, the research leverages a conceptual framework integrating with the Job Characteristics Model (JCM), Task–Technology Fit (TTF), and Institutional Theory to comprehend the multifaceted nature of adoption. The data reveal that GenAI is mainly leveraged for standard tasks, but experiences challenges in critical or identity-associated functions. The research presents the notion of pedagogical role integrity and reconceptualizes TTF as a context-dependent process influenced by both values and functionality. These insights hold practical relevance for higher education institutions seeking to develop responsible AI policies, create supportive training initiatives, and AI literacy modules for future managers, thereby aligning management education with the requirements of the changing workforce. • Examines GenAI adoption from educators' perspectives using qualitative methods. • Integrates Job Characteristics, TTF, and UTAUT Models into a novel conceptual framework. • Highlights teachers' ethical awareness and policy adoptions within universities as critical enabling conditions. • Reveals that adoption is highest for low-skill tasks, lowest for identity-driven ones. • Identifies how GenAI impacts pedagogical approaches but also teachers' identity and jobs.

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https://doi.org/https://doi.org/10.1016/j.ijme.2026.101365

Or copy a formatted citation

@article{marwa2026,
  title        = {{Reconceptualizing of GenAI adoption in higher education: A task-based perspective}},
  author       = {Marwa Chaieb et al.},
  journal      = {The International Journal of Management Education},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ijme.2026.101365},
}

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Reconceptualizing of GenAI adoption in higher education: A task-based perspective

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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