# Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation

arXiv: https://arxiv.org/abs/2312.11274 · HTML: https://mlsanity.com/paper/2312.11274/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2023.

## Built on by (most cited first, 8 of 8)

- [Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support](https://mlsanity.com/paper/2412.11995/) (2024)
- [Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students](https://mlsanity.com/paper/2411.18708/) (2024)
- [Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding](https://mlsanity.com/paper/2507.11198/) (2025)
- [AI-Mediated Feedback Improves Student Revisions: A Randomized Trial with FeedbackWriter in a Large Undergraduate Course](https://mlsanity.com/paper/2602.16820/) (2026)
- [Predicting Long Term Sequential Policy Value Using Softer Surrogates](https://mlsanity.com/paper/2412.20638/) (2024)
- [Rethinking AI Evaluation in Education: The TEACH-AI Framework and Benchmark for Generative AI Assistants](https://mlsanity.com/paper/2512.04107/) (2025)
- [Towards Breaking the Learning System Wall Using Multimodal Tutoring Transcriptions](https://mlsanity.com/paper/2609.36502/) (2026)
- [Examining Variation in How Guided AI Tutors Resolve Student Impasses](https://mlsanity.com/paper/2609.38346/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) applications to support human tutoring have potential to significantly improve learning outcomes, but engagement issues persist, especially among students from low-income backgrounds. We introduce an AI-assisted tutoring model that combines human and AI tutoring and hypothesize that this synergy will have positive impacts on learning processes. To investigate this hypothesis, we conduct a three-study quasi-experiment across three urban and low-income middle schools: 1) 125 students in a Pennsylvania school; 2) 385 students (50% Latinx) in a California school; and 3) 75 students (100% Black) in a Pennsylvania charter school, all implementing analogous tutoring models. We compare learning analytics of students engaged in human-AI tutoring compared to students using math software only. We find human-AI tutoring has positive effects, particularly in student's proficiency and usage, with evidence suggesting lower achieving students may benefit more compared to higher achieving students. We illustrate the use of quasi-experimental methods adapted to the particulars of different schools and data-availability contexts so as to achieve the rapid data-driven iteration needed to guide an inspired creation into effective innovation. Future work focuses on improving the tutor dashboard and optimizing tutor-student ratios, while maintaining annual costs per students of approximately 700 annually.