Exploring Students’ Motivation and Self-Regulation in AI-Driven Feedback: A Qualitative Study of a Python Learning Chatbot

Abstract

As artificial intelligence (AI) becomes increasingly embedded in higher education, understanding how students perceive, interpret, and respond to AI-generated feedback is critical for designing pedagogically meaningful learning environments. This study investigates students’ experiences with TaskInsighter, a GenAI-supported feedback chatbot that combines a conversational interface, generative feedback, Socratic questioning, formative scoring, and code-specific prompts in a university-level Python programming course. Grounded in Self-Regulated Learning (SRL) theory and Sensemaking Theory, we conducted a qualitative analysis of 38 open-ended student reflections and 8 in-depth semi-structured interviews. Notably, the interviewed students had engaged with the chatbot continuously over a 16-week semester, allowing the study to examine how learners described their evolving interpretations, feedback engagement, and regulatory behaviors over time. The findings suggest that students perceived the chatbot as supporting metacognitive regulation, explanation-oriented learning, motivational engagement, and strategic learning behaviors, while also reporting usability challenges related to system constraints and feedback clarity. Rather than accepting AI feedback passively, students actively evaluated the chatbot’s responses and adjusted their learning strategies in response to feedback. These insights contribute to understanding how SRL operates in AI-mediated learning contexts and suggest implications for designing AI learning tools and instructional strategies to support autonomy, reflection, and student agency.