# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 6 Conclusions  
This research investigates the impact of multimodal interaction technology on remote education, focusing on its benefits for enriching learning experiences, tailoring instruction, and integrating affective computing. The results suggest that the fusion of multimodal data can significantly enhance interaction and teaching efficacy in remote learning settings. \begin{table}
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**Study** & **Modality Types Used** & **Education Level** & **Target Group** & **Environment** & **Limitations** \\ \hline [PERSON] et al., 2023 [64] & Posture and gesture recognition, facial analysis, eye-tracking, verbal recognition & Higher Education & General students & Controlled laboratory setting & Limited real-world classroom deployment \\ \hline [PERSON] et al., 2023 [79] & Mixed reality gestural guidance & Higher Education & Physiotherapy students & Simulated classroom environment & Small sample size \\ \hline [PERSON] et al., 2024 [80] & LLM-empowered agents simulating teacher-student interactions & Higher Education & General students & Virtual classroom simulation & Lack of real-world validation \\ \hline [PERSON] et al., 2021 [81] & Pre-trained language models for dialog instruction detection & Higher Education & Online learners & Online educational platform & Focused on text-based interactions only \\ \hline [PERSON] et al., 2020 [82] & Machine learning for identifying at-risk students & K-12 Education & K-12 students & Multimodal online environments & Data imbalance, limited offline factors \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative summary of selected studies on multimodal interaction in remote education. Nonetheless, challenges persist, including issues with data synchronization, the demand for computational resources, user adaptability, and concerns regarding privacy and security. One significant challenge is synchronizing multimodal data from various sources, as differences in sampling rates and temporal misalignment can compromise real-time accuracy. In addition, high computational demands often hinder the practical deployment of these systems on low-resource devices commonly used in educational settings. Moreover, the complexity of human-computer interaction and the varying acceptance of personalized learning pathways can pose usability issues. Finally, privacy protection and algorithmic fairness remain critical concerns, as the extensive use of multimodal data increases the risk of misuse and discrimination. While the potential of this technology is substantial, existing research is constrained. A majority of studies are conducted in controlled laboratory environments, which limits the availability of large-scale, real-world classroom data and diminishes their relevance in practical teaching contexts. Furthermore, there is a notable deficiency in assessing the long-term effects of multimodal interaction technology, highlighting the need for additional research into its enduring influence on students' cognitive and behavioral growth. Future investigations should prioritize the optimization of personalized learning pathways, the enhancement of adaptability in resource-limited settings, and the improvement of cross-cultural applicability to promote the widespread adoption of this technology in remote education. As advancements in artificial intelligence and sensor technologies progress, multimodal interaction technology is poised to transform remote education into a more intelligent and personalized learning experience. Writing--original draft preparation, Y.X., L.Y., M.Z., S.C. and J.L. ; writing--review and editing, Y.X., L.Y., M.Z., S.C. and J.L. ; visualization, Y.X. ; supervision, S.C. and J.L. ; project administration, S.C. and J.L. All authors have read and agreed to the published version of the manuscript. This research was partially supported by Kobe University CMDS Joint Project Promoting DX Inside & Outside the University. Grant Number PJ2024-03. Not applicable.