Factors Influencing University Students' Intention to Use and Reliance on Generative AI: An Extended Technology Acceptance Model with Critical Use

Trang H. Nguyen, Long T. Truong, & Nhu H.T. Nguyen (2026) investigated factors influencing university students' intention to use and reliance on generative AI among Engineering and Computer Science/IT (CS/IT) students. Drawing on an extended Technology Acceptance Model (TAM), the study integrates the construct of critical use and conceptualizes reliance across four functional domains relevant to engineering and CS/IT education.

Key Findings

• Method: Anonymous survey at an Australian university (n=126), analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM).
• Attitudes and critical use drive intention: Attitudes towards GenAI and critical use directly and positively influence intention to use GenAI, while perceived ease of use and perceived usefulness have positive indirect effects. Notably, ease of use did not directly shape attitudes, and usefulness did not directly predict intention.
• Intention predicts reliance across four domains: Understanding, assessment, programming, and engineering projects. Students demonstrated moderate reliance overall, with greatest use for understanding-related tasks (clarifying concepts, worked examples) and limited utilization for full assessment writing.
• Appropriate, not excessive, reliance: Consistent with Schemmer et al.'s framework, students showed moderate, appropriate reliance — recognizing GenAI's limits for precision-demanding engineering and CS tasks while drawing on it for debugging, formula generation, and design ideation.
• Critical use as a core AI-literacy dimension: Students who actively evaluate, question, and cross-check GenAI outputs are more motivated to integrate the tools, and this practice safeguards against over-reliance.

Implications for AI in Education

The study highlights the importance of moving beyond initial technology acceptance to examine post-adoption behaviors — critical use and reliance — particularly in technically demanding disciplines. It proposes a three-tiered AI-literacy training approach for engineering and CS/IT programs: (1) evidence-based AI literacy for all students, (2) targeted mentoring for those needing additional support, and (3) intensive one-to-one support for students experiencing persistent difficulties or over-dependence. Because reliance varies by task, pedagogical approaches should be task-specific rather than uniform.

Connected Concepts

• Technology Acceptance Model
• Generative AI
• AI Literacy
• Higher Ed
• Student Experience
• CS Education
• Engineering Education
• Critical Thinking
• Ethics
• Academic Integrity
• Cognitive Offloading
• Reducing AI Misuse

Connected Articles

• GenAI Reliance Types Scale — GenAI reliance types scale
• LLM Reliance Types Undergrad — LLM reliance types among undergraduates
• Trust Reliance AI Education 2026 — Trust and reliance in AI education
• Efficiency Gain Illusion AI Overreliance — The efficiency gain illusion: AI overreliance

Citation

Nguyen, T. H., Truong, L. T., & Nguyen, N. H. T. (2026). Factors influencing university students' intention to use and reliance on generative artificial intelligence: An extended technology acceptance model with critical use. Computers and Education: Artificial Intelligence, 10, 100618.