Artificial intelligence in multimodal learning analytics: A systematic literature review
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 Analyt...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Computers and Education: Artificial Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X25000669 |
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| author | Mehrnoush Mohammadi Elham Tajik Roberto Martinez-Maldonado Shazia Sadiq Wojtek Tomaszewski Hassan Khosravi |
| author_facet | Mehrnoush Mohammadi Elham Tajik Roberto Martinez-Maldonado Shazia Sadiq Wojtek Tomaszewski Hassan Khosravi |
| author_sort | Mehrnoush Mohammadi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6f9b208d920e4e10b16d547cd6f29ca0 |
| institution | Kabale University |
| issn | 2666-920X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj-art-6f9b208d920e4e10b16d547cd6f29ca02025-08-20T03:47:10ZengElsevierComputers and Education: Artificial Intelligence2666-920X2025-06-01810042610.1016/j.caeai.2025.100426Artificial intelligence in multimodal learning analytics: A systematic literature reviewMehrnoush Mohammadi0Elham Tajik1Roberto Martinez-Maldonado2Shazia Sadiq3Wojtek Tomaszewski4Hassan Khosravi5School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, AustraliaDepartment of Educational Psychology and Learning Systems, Florida State University, Tallahassee, USACentre for Learning Analytics at Monash, Faculty of Information Technology, Monash University, Melbourne, AustraliaSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, AustraliaAustralian Research Council Centre of Excellence for Children and Families Over the Life Course, The University of Queensland, Brisbane, Australia; Institute for Social Science Research, The University of Queensland, Brisbane, AustraliaInstitute for Teaching and Learning Innovation, The University of Queensland, Brisbane, Australia; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2666920X25000669AIMultimodal dataLearning analyticsMultimodal learning analyticsSystematic review |
| spellingShingle | Mehrnoush Mohammadi Elham Tajik Roberto Martinez-Maldonado Shazia Sadiq Wojtek Tomaszewski Hassan Khosravi Artificial intelligence in multimodal learning analytics: A systematic literature review Computers and Education: Artificial Intelligence AI Multimodal data Learning analytics Multimodal learning analytics Systematic review |
| title | Artificial intelligence in multimodal learning analytics: A systematic literature review |
| title_full | Artificial intelligence in multimodal learning analytics: A systematic literature review |
| title_fullStr | Artificial intelligence in multimodal learning analytics: A systematic literature review |
| title_full_unstemmed | Artificial intelligence in multimodal learning analytics: A systematic literature review |
| title_short | Artificial intelligence in multimodal learning analytics: A systematic literature review |
| title_sort | artificial intelligence in multimodal learning analytics a systematic literature review |
| topic | AI Multimodal data Learning analytics Multimodal learning analytics Systematic review |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X25000669 |
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