ChatGPT in the Malaysian Classroom: Assessing Student Acceptance and Effectiveness Through UTAUT2 and IS Success Models
ChatGPT, an advanced artificial intelligence (AI) tool, is gaining prominence in higher education for its ability to provide timely academic support to students, enhancing their learning experiences. By generating original content based on user prompts, ChatGPT has the potential to transform academic assistance. This study investigates the key factors influencing user satisfaction (effectiveness) and behavioral intention (acceptance) toward ChatGPT. We propose an integrated research model that combines the “Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)” and the “Information System Success (ISS) model”, incorporating personal innovativeness as an additional factor. Using PLS-SEM, data from 202 students at a leading Malaysian research university were analyzed. Our findings reveal that social influence is the most significant predictor of behavioral intention, while system quality is the dominant driver of user satisfaction. Furthermore, satisfaction was found to significantly enhance students' behavioral intention to continue using ChatGPT. This highlights the role of user satisfaction in promoting acceptance of AI-driven educational tools. Our results provide recommendations for future research on the acceptance of AI tools such as ChatGPT in university education, suggesting the need for further exploration from both educators' and students' perspectives.
Authors
- Daisy Mui Hung Kee (ORCID: https://orcid.org/0000-0002-7748-8230)
- Safa Anmar Albarwary (ORCID: https://orcid.org/0000-0002-4165-1657)
- Savriddin Khalilov
- Rand B. Mohammed (ORCID: https://orcid.org/0000-0003-2885-326X)
- Hala Najwan Sabeh (ORCID: https://orcid.org/0000-0003-0420-6033)
Institutions
- Universiti Sains Malaysia (MY)
- Tishk International University (IQ)
Publication Details
- Published
- 2025-02-17
- DOI
- https://doi.org/10.1109/ssd64182.2025.10989952
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.69