Evaluating a GPT-4 and Retrieval-Augmented Generation-Based Conversational Agent to Enhance Learning Experience in a MOOC [r-libre/3602]

Miladi, Fatma; Psyché, Valéry; Diattara, Awa; El Mawas, Nour et Lemire, Daniel (sous presse). Evaluating a GPT-4 and Retrieval-Augmented Generation-Based Conversational Agent to Enhance Learning Experience in a MOOC. Dans 17th International Conference on Computer Supported Education.

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Catégorie de document :	Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture :	Oui
Étape de publication :	Accepté (sous presse)
Résumé :	Massive Open Online Courses (MOOCs) face significant challenges due to low completion rates, primarily caused by insufficient personalized support for learners. To address this, we developed a pedagogical AI-powered conversational agent enhanced with Retrieval-Augmented Generation (RAG) to provide real-time, contextually relevant support. Our evaluation with 25 learners demonstrated a statistically significant knowledge gain in the experimental group compared to the control group. Additionally, the agent achieved a high System Usability Scale (SUS) score. These findings highlight the potential of AI technologies to enhance online learning environments and inform future research on their role as learning companions in distance education.
Déposant:	Lemire, Daniel
Responsable :	Daniel Lemire
Dépôt :	11 févr. 2025 15:39
Dernière modification :	11 févr. 2025 15:39

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