TY - GEN
T1 - Exploring GenAI Chatbots in MOOCs: Analyzing Student Interactions and Self-regulated Learning Behaviors
AU - Brünner, Benedikt
AU - Ebner, Martin
AU - Schön, Sandra
PY - 2025/10/1
Y1 - 2025/10/1
N2 - The integration of generative AI (genAI) chatbots into Massive Open Online Courses (MOOCs) presents new opportunities for supporting self-regulated learning (SRL). This study examines 1,302 chatbot interactions from two Austrian blended MOOCs, where a retrieval-augmented generation (RAG) chatbot based on GPT 4o-mini was deployed to assist students. Using the process-action framework by Lai (2024), we categorize chatbot interactions into key SRL processes: defining, seeking, engaging, and reflecting. Results show that students predominantly use the chatbot for information retrieval, content summarization, and quiz-based reinforcement, with 41% of interactions classified as information search queries and 17% as rehearsal. However, engagement with metacognitive SRL strategies, such as goal setting and self-evaluation, remains low. Additionally, non-learning interactions, including humor-driven conversations, functional queries, and prompt injection attempts, showcase ways students interact with AI in educational settings. Based on our findings, we propose refinements to the existing SRL process-action framework, incorporating new categories to better account for genAI chatbot-specific interactions, such as Evaluation of Information Quality and Reformatting. We discuss implications for chatbot integration in MOOCs, emphasizing AI-generated quizzes, structured feedback, and safeguards against misuse.
AB - The integration of generative AI (genAI) chatbots into Massive Open Online Courses (MOOCs) presents new opportunities for supporting self-regulated learning (SRL). This study examines 1,302 chatbot interactions from two Austrian blended MOOCs, where a retrieval-augmented generation (RAG) chatbot based on GPT 4o-mini was deployed to assist students. Using the process-action framework by Lai (2024), we categorize chatbot interactions into key SRL processes: defining, seeking, engaging, and reflecting. Results show that students predominantly use the chatbot for information retrieval, content summarization, and quiz-based reinforcement, with 41% of interactions classified as information search queries and 17% as rehearsal. However, engagement with metacognitive SRL strategies, such as goal setting and self-evaluation, remains low. Additionally, non-learning interactions, including humor-driven conversations, functional queries, and prompt injection attempts, showcase ways students interact with AI in educational settings. Based on our findings, we propose refinements to the existing SRL process-action framework, incorporating new categories to better account for genAI chatbot-specific interactions, such as Evaluation of Information Quality and Reformatting. We discuss implications for chatbot integration in MOOCs, emphasizing AI-generated quizzes, structured feedback, and safeguards against misuse.
KW - AI Chatbots
KW - MOOCs
KW - Self-Regulated Learning
KW - Generative AI
KW - Student Interactions
KW - Blended Learning
KW - Educational Technology
UR - https://www.scopus.com/pages/publications/105019185252
U2 - 10.1007/978-3-032-00056-9_2
DO - 10.1007/978-3-032-00056-9_2
M3 - Conference paper
SN - 978-3-032-00055-2
T3 - Lecture Notes in Computer Science
SP - 14
EP - 24
BT - Digital Education
A2 - Hamonic, Ella
A2 - Sharrock, Rémi
PB - Springer, Cham
ER -
