Investigating the factors of undergraduate students support for AI utilisation

Tan, Yen Yee and Lee, Hui Ni (2024) Investigating the factors of undergraduate students support for AI utilisation. Final Year Project, UTAR.

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Abstract

This study investigates the factors that influence undergraduate students' support for AI utilisation, focusing on personality traits, creativity, and information quality. As AI becomes increasingly integrated into educational practices, understanding the determinants of student support for AI is crucial for enhancing learning outcomes and guiding the effective implementation of AI tools in academic settings. The research employs a quantitative approach, utilising a structured questionnaire to gather data from 400 undergraduate students across various public universities and private universities in Selangor, Kuala Lumpur, and Perak. The study applies the Big Five Personality Traits, Divergent Thinking Theory, and Technology Acceptance Model (TAM) to explore the effects of the independent variables (personality traits, creativity, and information quality) on the dependent variable (support for AI utilisation). The findings reveal that all three independent variables significantly influence students' support for AI, with creativity having the strongest impact, followed by information quality and personality traits. These results suggest that fostering creativity and ensuring high-quality, relevant, and reliable AI-generated information are key to gaining student support for AI tools in education. The study concludes with recommendations for educators to integrate AI thoughtfully into curricula and for AI developers to focus on creating tools that meet the evolving needs of educational environments. The implications of this research are significant for the future of AI in education, as it provides insights into how students' support can be harnessed to improve educational outcome

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