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

arXiv: https://arxiv.org/abs/2405.04645 · HTML: https://mlsanity.com/paper/2405.04645/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2024.

## Built on by (most cited first, 30 of 37)

- [Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues](https://mlsanity.com/paper/2503.06424/) (2025)
- [Sotopia-RL: Reward Design for Social Intelligence](https://mlsanity.com/paper/2508.03905/) (2025)
- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation](https://mlsanity.com/paper/2503.12600/) (2025)
- [TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students](https://mlsanity.com/paper/2505.01563/) (2025)
- [SPL: A Socratic Playground for Learning Powered by Large Language Model](https://mlsanity.com/paper/2406.13919/) (2024)
- [A Systematic Review on Prompt Engineering in Large Language Models for K-12 STEM Education](https://mlsanity.com/paper/2410.11123/) (2024)
- [The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models](https://mlsanity.com/paper/2410.09576/) (2024)
- [A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents](https://mlsanity.com/paper/2508.01503/) (2025)
- [Generating AI Literacy MCQs: A Multi-Agent LLM Approach](https://mlsanity.com/paper/2412.00970/) (2024)
- [Position: LLMs Can be Good Tutors in English Education](https://mlsanity.com/paper/2502.05467/) (2025)
- [Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)](https://mlsanity.com/paper/2505.17238/) (2025)
- [ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations](https://mlsanity.com/paper/2506.14200/) (2025)
- [LLM-Generated Feedback Supports Learning If Learners Choose to Use It](https://mlsanity.com/paper/2506.17006/) (2025)
- [Revolutionizing Bridge Operation and Maintenance with LLM-based Agents: An Overview of Applications and Insights](https://mlsanity.com/paper/2407.10064/) (2024)
- [Human-Centric eXplainable AI in Education](https://mlsanity.com/paper/2410.19822/) (2024)
- [Evidence-Decision-Feedback: Theory-Driven Adaptive Scaffolding for LLM Agents](https://mlsanity.com/paper/2602.01415/) (2026)
- [SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs](https://mlsanity.com/paper/2604.22134/) (2026)
- ["From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-based Learning Toolkit in K-12 Education](https://mlsanity.com/paper/2412.17243/) (2024)
- [VTutor: An Animated Pedagogical Agent SDK that Provide Real Time Multi-Model Feedback](https://mlsanity.com/paper/2505.06676/) (2025)
- [Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education](https://mlsanity.com/paper/2506.19107/) (2025)
- [Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback](https://mlsanity.com/paper/2603.12471/) (2026)
- [Small, Private Language Models as Teammates for Educational Assessment Design](https://mlsanity.com/paper/2605.15015/) (2026)
- [Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most](https://mlsanity.com/paper/2605.16207/) (2026)
- [The promise and limits of LLMs in constructing proofs and hints for logic problems in intelligent tutoring systems](https://mlsanity.com/paper/2505.04736/) (2025)
- [SID: Benchmarking Guided Instruction Capabilities in STEM Education with a Socratic Interdisciplinary Dialogues Dataset](https://mlsanity.com/paper/2508.04563/) (2025)
- [How well can LLMs provide planning feedback in grounded environments?](https://mlsanity.com/paper/2509.09790/) (2025)
- [Investigating Learner-Aware Design of LLM-Generated Educational Feedback](https://mlsanity.com/paper/2602.11650/) (2026)
- [Beyond Accuracy: Evaluating Strategy Diversity in LLM Mathematical Reasoning](https://mlsanity.com/paper/2605.09292/) (2026)
- [The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory](https://mlsanity.com/paper/2503.10533/) (2025)

## Abstract (arXiv)

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 directly formulating prompts to solicit specific information, lacking a solid theoretical foundation for prompt construction and empirical assessments of their impact on learning. This work advocates careful and caring AIED research by going through previous research on feedback generation in ITS, with emphasis on the theoretical frameworks they utilized and the efficacy of the corresponding design in empirical evaluations, and then suggesting opportunities to apply these evidence-based principles to the design, experiment, and evaluation phases of LLM-based feedback generation. The main contributions of this paper include: an avocation of applying more cautious, theoretically grounded methods in feedback generation in the era of generative AI; and practical suggestions on theory and evidence-based feedback design for LLM-powered ITS.