![[2602.20547] What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20547] What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI
Summary
This article explores the factors influencing students' adoption of AI chatbots for learning, utilizing the Technology Acceptance Model to identify key predictors of usage intention.
Why It Matters
Understanding the drivers behind students' use of AI chatbots is crucial for educators and developers. Insights from this research can inform the design of more effective AI tools that enhance learning experiences, addressing both technological and social factors that impact acceptance.
Key Takeaways
- Perceived usefulness is the strongest predictor of students' intention to use AI chatbots.
- Perceived ease of use has an indirect effect on usage intention through perceived usefulness.
- Trust and subjective norms significantly influence perceptions of usefulness.
- Perceived enjoyment affects usage intentions both directly and indirectly.
- Adoption decisions are more influenced by confidence in AI outputs than by effort-related considerations.
Computer Science > Human-Computer Interaction arXiv:2602.20547 (cs) [Submitted on 24 Feb 2026] Title:What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI Authors:Griffin Pitts, Sanaz Motamedi View a PDF of the paper titled What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI, by Griffin Pitts and 1 other authors View PDF HTML (experimental) Abstract:Conversational AI tools have been rapidly adopted by students and are becoming part of their learning routines. To understand what drives this adoption, we draw on the Technology Acceptance Model (TAM) and examine how perceived usefulness and perceived ease of use relate to students' behavioral intention to use conversational AI that generates responses for learning tasks. We extend TAM by incorporating trust, perceived enjoyment, and subjective norms as additional factors that capture social and affective influences and uncertainty around AI outputs. Using partial least squares structural equation modeling, we find perceived usefulness remains the strongest predictor of students' intention to use conversational AI. However, perceived ease of use does not exert a significant direct effect on behavioral intention once other factors are considered, operating instead indirectly through perceived usefulness. Trust and subjective norms significantly influence perceptions of usefulness, while perceived enjoyment exerts both a direct and indirect effect on usage intention...