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THE FACTORS DRIVING CHATGPT ADOPTION AMONG UNIVERSITY STUDENTS IN MALAYSIA: A CONCEPTUAL ANALYSIS

2026-06-30 · Zenodo (CERN European Organization for Nuclear Research)

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An AI research paper on THE FACTORS DRIVING CHATGPT ADOPTION AMONG UNIVERSITY STUDENTS IN MALAYSIA: A CONCEPTUAL ANALYSIS.

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Chinese explanation / 中文解读

中文解读待补充:本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

As generative AI technology, especially large language models such as ChatGPT, rapidly come into common use, they are changing the way that learning occurs in higher education. Research across the globe has begun exploring how students are adopting generative AI technology; however, there is still a limited understanding of what drives students’ adoption of generative AI technology in lower resource developing higher education environments. Currently, most research on students’ adoption of generative AI technology relies on traditional technology acceptance models and empirical correlation studies. While these studies do provide some insight into students’ adoption of generative AI technology, there is a significant lack of integration of the psychological capabilities of the learner and the social learning environment in which students learn. In response to this gap, the purpose of this conceptual paper is to develop an integrative framework to explain how university students in Malaysia will adopt ChatGPT. Building on the Technology Acceptance Model and Social Cognitive Theory, the proposed model includes perceived usefulness, self-efficacy and social influence as important antecedents of ChatGPT adoption. The proposed model fills a critical gap in the literature by contextualising technology acceptance regarding the unique characteristics of conversational AI tools. The proposed model has practical applications in helping universities, educators and policymakers to effect ethical, effective and meaningful pedagogical integration of AI technology in Malaysian higher education. Finally, the paper describes the research relevance of the proposed model and offers suggestions and future directions for empirical validation.

5.0Engineering value
7.0Research novelty
4.0Business relevance

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