Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciences

This paper advocates for theoretically-grounded approaches to designing LLM-generated feedback in Intelligent Tutoring Systems by reviewing decades of ITS research on feedback generation methods (expert-created models, data-driven models, and LLMs) and proposing how learning science principles should guide modern LLM-based feedback design and evaluation.

The field of Artificial Intelligence in Education (AIED) focuses on the intersection of technology, education, and psychology, placing a strong emphasis on supporting learners' needs with compassion and understanding. The growing prominence of Large Language Models (LLMs) has led to the development of scalable solutions within educational settings, including generating different types of feedback in Intelligent Tutoring Systems. However, the approach to utilizing these models often involves dire

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