# Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

arXiv: https://arxiv.org/abs/2005.02431 · HTML: https://mlsanity.com/paper/2005.02431/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

## Built on by (most cited first, 16 of 16)

- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning](https://mlsanity.com/paper/2308.03234/) (2023)
- [Improving the Validity of Automatically Generated Feedback via Reinforcement Learning](https://mlsanity.com/paper/2403.01304/) (2024)
- [Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology](https://mlsanity.com/paper/2411.03495/) (2024)
- [Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems](https://mlsanity.com/paper/2103.07785/) (2021)
- [Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions?](https://mlsanity.com/paper/2405.06414/) (2024)
- [WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation](https://mlsanity.com/paper/2412.01626/) (2024)
- [Generating Feedback-Ladders for Logical Errors in Programming using Large Language Models](https://mlsanity.com/paper/2405.00302/) (2024)
- [Comparative Study of Learning Outcomes for Online Learning Platforms](https://mlsanity.com/paper/2104.07763/) (2021)
- [When Verification Hurts: Asymmetric Effects of Multi-Agent Feedback in Logic Proof Tutoring](https://mlsanity.com/paper/2603.27076/) (2026)
- [Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most](https://mlsanity.com/paper/2605.16207/) (2026)
- [Learning Code-Edit Embedding to Model Student Debugging Behavior](https://mlsanity.com/paper/2502.19407/) (2025)
- [Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests](https://mlsanity.com/paper/2506.05990/) (2025)
- [Investigating Learner-Aware Design of LLM-Generated Educational Feedback](https://mlsanity.com/paper/2602.11650/) (2026)
- [Question Personalization in an Intelligent Tutoring System](https://mlsanity.com/paper/2206.14145/) (2022)
- [Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System](https://mlsanity.com/paper/2607.13370/) (2026)

## Abstract (arXiv)

We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit, a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.