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Predicting higher education students' continued intention and self-reported use of generative AI tools in academic
Lawrence Jinming Du1, Zhi Liu2, Ning Liao3
1No.95 Albany Street, Department of Languages and Cultures, University of Otago, New Zealand.
Acta Psychologica
|July 17, 2026
Summary
Generative artificial intelligence (GenAI) use in Chinese higher education is driven by expected usefulness, social influence, and perceived knowledge. Ease of use alone is insufficient for continued adoption of these academic tools.
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Human-Computer Interaction
Background:
- Generative artificial intelligence (GenAI) tools are increasingly prevalent in higher education.
- Understanding factors influencing student adoption and continued use of GenAI for academic work is crucial.
- Existing technology acceptance models require adaptation to the unique context of GenAI in education.
Purpose of the Study:
- To investigate the key determinants of Chinese higher education students' continued intention and self-reported use of GenAI tools.
- To extend the Unified Theory of Acceptance and Use of Technology (UTAUT) model for GenAI adoption.
- To explore students' motivations, perceived benefits, and concerns regarding GenAI in academic settings.
Main Methods:
- An explanatory sequential mixed-methods design combining quantitative surveys and qualitative interviews.
- Quantitative phase: Structural equation modeling analysis of survey data from 580 university students.
- Qualitative phase: Semi-structured interviews to elaborate on survey findings and provide deeper insights.
Main Results:
- Performance expectancy, social influence, and perceived knowledge significantly predicted behavioral intention to use GenAI.
- Facilitating conditions and behavioral intention were associated with self-reported use behavior.
- Effort expectancy did not significantly predict behavioral intention, indicating ease of use is not the primary driver for already accessible tools.
Conclusions:
- Student adoption of GenAI in academic contexts is primarily shaped by perceived usefulness, social influence, and knowledge of AI outputs.
- While students utilize GenAI for tasks like brainstorming and proofreading, concerns about over-reliance, academic integrity, and content reliability persist.
- Findings offer practical guidance for developing AI literacy training, classroom policies, and institutional guidelines for responsible GenAI integration in higher education.