Artificial intelligence in multimodal learning analytics: A systematic literature review

被引：0
作者：
Mohammadi, Mehrnoush [1 ]
Tajik, Elham [2 ]
Martinez-Maldonado, Roberto [3 ]
Sadiq, Shazia [1 ]
Tomaszewski, Wojtek [4 ,5 ]
Khosravi, Hassan [6 ]
机构：
[1] School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane
[2] Department of Educational Psychology and Learning Systems, Florida State University, Tallahassee
[3] Centre for Learning Analytics at Monash, Faculty of Information Technology, Monash University, Melbourne
[4] Australian Research Council Centre of Excellence for Children and Families Over the Life Course, The University of Queensland, Brisbane
[5] Institute for Social Science Research, The University of Queensland, Brisbane
[6] Institute for Teaching and Learning Innovation, The University of Queensland, Brisbane
来源：
Computers and Education: Artificial Intelligence | 2025年 / 8卷
基金：
澳大利亚研究理事会;
关键词：
AI; Learning analytics; Multimodal data; Multimodal learning analytics; Systematic review;
D O I：
10.1016/j.caeai.2025.100426
中图分类号：
学科分类号：
摘要：
The proliferation of educational technologies has generated unprecedented volumes of diverse, multimodal learner data, offering rich insights into learning processes and outcomes. However, leveraging this complex, multimodal data requires advanced analytical methods. While Multimodal Learning Analytics (MMLA) offers promise for exploring this data, the potential of Artificial Intelligence (AI) to enhance MMLA remains largely unexplored. This paper bridges these two evolving domains by conducting the first systematic literature review at the intersection of AI and MMLA, analyzing 43 peer-reviewed papers from 11 reputable databases published between 2019 and 2024. The findings indicate a growing trend in AI-enhanced MMLA studies published predominantly in high-quality venues, led by education researchers with a predominant focus on tertiary education targeting diverse stakeholders. Guided by a novel conceptual framework, our analysis highlights the transformative role of AI across the MMLA process, particularly in model learning and feature engineering. However, it also uncovers significant gaps, including limited AI implementation in components requiring deep integration with learning theories, insufficient application of advanced AI techniques, and lack of large-scale studies in authentic learning environments. The review identifies key benefits, such as enhanced personalization and real-time feedback, while also addressing challenges related to ethical considerations, data integration, and scalability. Our study contributes by offering comprehensive recommendations for future research, emphasizing international collaboration, multi-level studies, and ethical AI implementation. These findings advance the theoretical understanding of AI's role in education, providing a foundation for developing sophisticated, interpretable, and scalable AI-enhanced MMLA approaches, potentially revolutionizing personalized learning across diverse educational settings. © 2025
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