Citation

Mohktaza Zain, Suraya and Raman, Kumutha and Abd Rahman, Norariffuddin and Abd Rahman, Alia Amira and Dzulkepli, Nor Syahidatul Amira An AI-Driven Adaptive Interactive Multimedia Learning System to Enhance Student Engagement and Learning Performance in Malaysian Higher Education. International Journal of Creative Multimedia, 7 (2). ISSN 2716-6333

Text Article+#3829+-+IJCM+Vol+7(2)+Sept+2026.pdf - Published Version Restricted to Repository staff only Download (438kB)

Abstract

Artificial intelligence (AI) has created new opportunities for the development of intelligent and personalised learning environments in higher education. The increasing use of digital and multimedia resources in education has also created new opportunities for learning. However, most conventional multimedia learning environments are static and do not adapt to the differing needs of the individual learners. Although there has been increasing research activity in artificial intelligence, adaptive learning, learning analytics and multimedia-based education, these areas have mostly been investigated separately. There is therefore a need for a framework that integrates these different areas of study, particularly in the higher education setting. Hence, this paper proposes an AI-driven adaptive interactive multimedia learning framework for Malaysia’s higher education institutions. The conceptual framework for the development of the proposed model is based on the synthesis of existing literature on areas such as AI in education, adaptive learning, interactive multimedia learning, learning analytics, personalised learning, and intelligent educational technologies. The framework incorporates five major components: interactions with multimedia content, interactive multimedia content itself, learning analytics, an AI analytics engine, and an adaptive learning module. The interaction with the multimedia content allows for the collection of data about the learners, which is analysed by the learning analytics and AI analytics engine to recognise patterns in the learners’ interactions with the multimedia content. These analyses enable the adaptive learning module to provide recommendations for the personalised learning of each of those learners. While the proposed framework provides a structure for integrating AI, learning analytics, adaptive learning, and multimedia content within a Malaysian higher education learning environment, the framework itself has yet to be empirically implemented. Thus, while the potential for the framework to provide benefits to learners within higher education is present, such potential is presented as a proposition for future investigation. As such, future research will focus upon the development of the proposed framework and its implementation as a functional prototype according to an appropriate research design.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence, Adaptive Learning, Interactive Multimedia, Learning Analytics, Higher Education, Student Engagement
Subjects:	L Education > LB Theory and practice of education > LB2300-2430 Higher education
Divisions:	Others
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	12 Oct 2026 04:44
Last Modified:	12 Oct 2026 04:44
URII:	http://shdl.mmu.edu.my/id/eprint/16920

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