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Communication Papers of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 49

Acceptance of Generative AI in Higher Education in Software Engineering Education—A Systematic Literature Review

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DOI: http://dx.doi.org/10.15439/2026F3410

Citation: Veronika Vasileva, ,

Full text

Abstract. Objective. This Systematic Literature Review (SLR) examines how predictors from established technology acceptance models influence the adoption of Generative AI (GenAI) in higher education among students and lecturers. It aims to identify key acceptance factors, with a particular focus on Software Engineering education. Methods. A rigorous literature review process was applied, including systematic search, selection, and quality assessment of studies. Clearly defined inclusion, exclusion, and quality criteria ensured the use of validated findings. Acceptance factors were synthesized and compared for both students and lecturers. Results. 31 studies (2020--2025) were analyzed. Performance Expectancy (PE), Perceived Trust (PT), Habit (HT), and Attitude Toward Using (ATU) show strong positive effects on Behavioral Intention (BI), while BI and HT are the strongest predictors of Use Behavior (UB). Effects of Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Hedonic Motivation (HM) vary across contexts. Conclusion. Tailored implementation strategies for GenAI in higher eductation are essential. Contextual and individual factors, such as digital competence and perceived ethics, should be emphasized. Research on acceptance of GenAI in Software Engineering education remains limited and requires more specific future studies.

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