Challenges Faced by Malaysian Educators in Integrating Artificial Intelligence into Teaching and Learning: A Systematic Review

Abstract

The rapid integration of Artificial Intelligence (AI) into education has generated considerable scholarly attention worldwide, yet systematic documentation of the challenges encountered specifically by Malaysian educators remains limited. This article presents a systematic review of empirical studies published between 2020 and 2025 that examine the experiences and obstacles faced by Malaysian teachers and lecturers when adopting AI tools in their teaching and learning practices. Drawing from eight peer-reviewed sources grounded in Malaysian higher education and secondary school contexts, four principal challenge domains were identified: (1) concerns over AI-generated information accuracy and academic integrity, (2) the absence of clear institutional policies and governance frameworks, (3) insufficient AI literacy, training exposure, and professional development, and (4) role adaptation pressures accompanied by socio-psychological barriers. The findings confirm that while Malaysian educators generally recognise the pedagogical potential of AI, its meaningful integration is significantly impeded by structural, ethical, and human-capacity deficits. The study recommends that institutions prioritise evidence-based AI governance frameworks, invest in structured and career-stage-differentiated professional development, and cultivate collaborative professional learning communities to sustain long-term AI adoption in Malaysian education.

人工智能（AI）在教育领域的快速融合已引起全球关注，但对马来西亚教育者所面临挑战的系统性认识仍较有限。本研究对2020年至2025年间发表的相关实证研究进行探索性系统综述，采用叙事综合方法分析8篇同行评审文献，聚焦教育者在人工智能应用中的核心困境。研究发现四个相互关联的挑战领域：人工智能生成内容的准确性及其对学术诚信的影响、制度治理框架的缺失、教育者人工智能素养与专业发展不足，以及与角色适应、身份冲突和技术焦虑相关的社会心理障碍。这些挑战表明，人工智能整合不仅是技术问题，更是一个复杂的组织与人本过程，且各领域之间相互强化，如培训不足削弱批判性评估能力，政策缺失加剧学术诚信管理不确定性，心理顾虑则降低技术使用意愿。基于此，本研究强调需采取整体性与协同化路径推进人工智能整合，包括构建情境化治理框架、优化持续性与差异化的专业发展机制，以及通过协作与导师支持缓解社会心理压力。本研究为理解马来西亚教育情境下教育者面临的人工智能应用挑战提供了综合性视角，并为实现有意义且可持续的教学转型提供实践启示。