Heliyon. 2024 May 31;10(11):e32220. doi: 10.1016/j.heliyon.2024.e32220. eCollection 2024 Jun 15.

ABSTRACT

This study investigates the determinants of ChatGPT adoption among university students and its impact on learning satisfaction. Utilizing the Technology Acceptance Model (TAM) and incorporating insights from interaction learning, collaborative learning, and information quality, a structural equation modeling approach was employed. This research collected valuable responses from 262 students at King Faisal University in Saudi Arabia through the use of self-report questionnaires. The data’s reliability and validity were assessed using confirmation factor analysis, followed by path analysis to explore the hypotheses in the proposed model. The results indicate the pivotal roles of interaction learning and collaborative learning in fostering ChatGPT adoption. Social interaction played a significant role, as researchers engaging in conversations and knowledge-sharing expressed increased comfort with ChatGPT. Information quality was found to substantially influence researchers’ decisions to continue using ChatGPT, emphasizing the need for ongoing improvement in the accuracy and relevance of content provided. Perceived ease of use and perceived usefulness played intermediary roles in linking ChatGPT engagement to learning satisfaction. User-friendly interfaces and perceived utility were identified as crucial factors affecting overall satisfaction levels. Notably, ChatGPT positively impacted learning motivation, indicating its potential to enhance student engagement and interest in learning. The study’s findings have implications for educational practitioners seeking to improve the implementation of AI technologies in university students, emphasizing user-friendly design, collaborative learning, and factors influencing satisfaction. The study concludes with insights into the complex interplay between AI-powered tools, learning objectives, and motivation, highlighting the need for continued research to comprehensively understand these dynamics.

PMID:38933954 | PMC:PMC11200296 | DOI:10.1016/j.heliyon.2024.e32220