AI-Driven Multimodal Framework for Student Engagement Detection in Sustainable Distance Learning: Models and Green AI Perspectives

DOI

10.2991/978-94-6463-948-3_59How to use a DOI?

Keywords

Artificial Intelligence; Student Engagement; Multimodal Learning Analytics; Distance Learning; Green AI

Abstract

The pandemic of COVID-19 fast-tracked online learning, with the challenge of maintaining student engagement in the process. This paper introduces a multimodal AI architecture that integrates visual, audio, and behavioral data with lightweight deep learning models for real-time energy-efficient student engagement detection. The system adopts fusion strategies and explainability tools (Grad-CAM, SHAP) to provide transparency, fairness, and scalability. Tests prove its ability to increase inclusivity, sustainability, and responsible AI uptake in online learning.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Parinita Chate
AU  - Kailas Patil
AU  - Nazish Ansari
AU  - Shardul Jagdhane
AU  - Sejal Bhole
PY  - 2026
DA  - 2026/01/06
TI  - AI-Driven Multimodal Framework for Student Engagement Detection in Sustainable Distance Learning: Models and Green AI Perspectives
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 853
EP  - 865
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_59
DO  - 10.2991/978-94-6463-948-3_59
ID  - Chate2026
ER  -