📝 SummarXiv

Abstract

Reflection is widely recognized as a cornerstone of student development, fostering critical thinking, self-regulation, and deep conceptual understanding. Traditionally, reflective skills have been cultivated through structured feedback, mentorship, and guided self-assessment. However, these approaches often face challenges such as limited scalability, difficulties in delivering individualized feedback, and a shortage of instructors proficient in facilitating meaningful reflection. This study pioneers the use of generative AI, specifically large language models (LLMs), as an innovative solution to these limitations. By leveraging the capacity of LLMs to deliver personalized, context-sensitive feedback at scale, this research investigates their potential to serve as effective facilitators of reflective exercises, sustaining deep engagement and promoting critical thinking. Through in-depth analyses of prompt engineering strategies and simulated multi-turn dialogues grounded in a project-based learning (PBL) context, the study demonstrates that, with pedagogically aligned prompts, LLMs can serve as accessible and adaptive tools for scalable reflective guidance. Furthermore, LLM-assisted evaluation is employed to objectively assess the performance of both tutors and students across multiple dimensions of reflective learning. The findings contribute to the evolving understanding of AI's role in reflective pedagogy and point to new opportunities for advancing AI-driven intelligent tutoring systems.