To examine the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on university students’ behavioural intention to use and actual use of ChatGPT in higher education, while assessing the moderating role of ethical awareness. Methods: The proposed study adopts a quantitative cross-sectional survey design. Data will be collected from university students and analysed using structural equation modelling. Guided by the Unified Theory of Acceptance and Use of Technology, the model evaluates the technological, social, institutional, and ethical factors influencing students’ adoption and use of ChatGPT. Results: The proposed model suggests that students’ behavioural intention and actual use of ChatGPT are influenced by its perceived usefulness, ease of use, social influence, and the availability of institutional and technological support. Ethical awareness is expected to moderate these relationships because ChatGPT use may raise concerns related to academic integrity, plagiarism, overreliance, privacy, fairness, and reduced critical thinking. Conclusion: The study contributes to the literature by integrating technology acceptance with responsible AI use in higher education. It provides a framework for examining ChatGPT adoption patterns, students’ motivations for using the technology, and its implications for academic integrity, while offering practical guidance for universities, educators, and policymakers in developing AI literacy programmes and responsible-use policies.
Naga Thevan Mano Karan, Muhammad Hassan Arshad, Saralah Devi Mariamdaran Chethiyar et al.· Journal of Psychology &...· 0 citations
Objective: To develop a conceptual model examining the relationships between AI learning support, digital competence, teacher AI guidance, and learning performance among students in Malaysian higher education institutions. Methods: The proposed study adopts a quantitative cross-sectional design. Data will be collected through a survey of Malaysian university students, and the hypothesised relationships will be tested using structural equation modelling. The model includes academic engagement and self-regulated learning as sequential mediators and academic integrity concern as a moderator of the relationship between academic engagement and learning performance. Results: Drawing on self-regulated learning theory, student engagement theory, social cognitive theory, AI literacy, digital competence, and academic integrity literature, the proposed model suggests that AI learning support does not automatically improve students’ learning performance. Its effectiveness depends on students’ ability to use AI strategically, critically, and ethically, together with appropriate guidance from lecturers. Conclusion: The proposed model contributes to the AI education literature by shifting attention from students’ intention to adopt AI towards the learning mechanisms through which AI use may influence academic outcomes. It also provides a framework for understanding the roles of academic engagement, self-regulated learning, teacher guidance, digital competence, and academic integrity in AI-supported learning.
Raden Azamry Bin Raden Perhan, Rajoo Ramanchandram, Saralah Devi Mariamdaran Chethiyar et al.· Journal of Psychology &...· 0 citations
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