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Assessing Malaysia higher education students' perceptions of artificial intelligence personalized learning tools

Aug 2026 · Edelweiss Applied Science and Technology · 0 citations · 44 references

TL;DR

Students' perceptions of AI-based personalised learning tools in Malaysian higher education institutions are examined to provide insights for developers, academic institutions, and policymakers regarding AI personalised learning tool selection, syllabus development, and integration strategies.

Abstract

Artificial Intelligence (AI) is a transformative technology reshaping teaching and learning practices. By enabling personalized, cost-effective, and adaptive learning experiences, AI has become increasingly integrated into educational environments. However, its adoption also raises concerns related to credibility, trustworthiness, accuracy, privacy, and security. As part of a larger research project, this paper examines students' perceptions of AI-based personalised learning tools in Malaysian higher education institutions. Student perceptions were investigated using three variables derived from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2): Performance Expectancy (PE), Effort Expectancy (EE), and Social Influence (SI), with an additional variable, Reliability and Trust (RT). A qualitative study involving in-depth interviews with thirteen participants was conducted to explore their experiences with an AI-based personalised learning tool, Brainly. Using thematic analysis, five themes were identified: support and customization, learning efficiency, accessibility and interactivity, peer encouragement and social comparison, and credibility and dependability. Learning efficiency emerged as the most prominent theme, with 92% of participants reporting enhanced understanding of complex topics, improved learning effectiveness through accessibility, immediate feedback, and interactive learning, and better efficiency in revision and practice. The findings provide insights for developers, academic institutions, and policymakers regarding AI personalised learning tool selection, syllabus development, and integration strategies.

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