What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI
Griffin Pitts, Sanaz Motamedi

TL;DR
This study investigates the factors influencing students' adoption of conversational AI tools, emphasizing perceived usefulness, trust, enjoyment, and social norms as key drivers over ease of use.
Contribution
It extends the Technology Acceptance Model by incorporating trust, enjoyment, and social influences to better understand AI adoption in educational settings.
Findings
Perceived usefulness is the strongest predictor of usage intention.
Trust and subjective norms significantly affect perceived usefulness.
Perceived enjoyment influences adoption both directly and indirectly.
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…
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Taxonomy
TopicsAI in Service Interactions · Technology Adoption and User Behaviour · Artificial Intelligence in Healthcare and Education
