Understanding University Students' Intentions to Adopt AI Technology: Key Influencing Factors in the Use of ChatGPT

Abstract

Introduction This research investigates the variables influencing university students' willingness to use ChatGPT by employing the Technology-to-Performance Chain theory and the Technology Acceptance Model frameworks. Methods A quantitative research approach was used, with online questionnaires distributed to 209 university students. Structural Equation Modeling was employed to analyze the associations between task characteristics, technology characteristics, individual characteristics, task–technology fit, attitude, and adoption intention. Results The findings revealed that task characteristics, technology characteristics, individual characteristics, task–technology fit, and students’ attitudes toward ChatGPT all had significant positive effects on their intention to adopt the tool. These results confirm the strength of the integrated theoretical framework, demonstrating that both the Technology-to-Performance Chain and the Technology Acceptance Model effectively explain students’ adoption behavior in the context of AI-assisted learning. Discussion The findings provide actionable insights for educators, policymakers, and developers to design AI-based learning environments that align with students' academic tasks, enhance usability, and foster positive attitudes, thereby supporting effective technology integration in higher education. Conclusion The study’s focus on a single public university, with a sample primarily composed of undergraduate business students, limits the generalizability of the findings. Future research should include diverse institutions and examine additional mediating variables. This study contributes to technology adoption literature by applying established theories to AI education contexts and by incorporating Task–Technology Fit as an independent variable to deepen understanding of AI–learning alignment.

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