# Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support

arXiv: https://arxiv.org/abs/2412.11995 · HTML: https://mlsanity.com/paper/2412.11995/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails](https://mlsanity.com/paper/2402.09216/) (2024)
- [How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging](https://mlsanity.com/paper/2310.05292/) (2023)
- [Improving Student Learning with Hybrid Human-AI Tutoring: A Three-Study Quasi-Experimental Investigation](https://mlsanity.com/paper/2312.11274/) (2023)
- [Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle](https://mlsanity.com/paper/2312.00087/) (2023)

## Built on by (most cited first, 11 of 11)

- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Problems With Large Language Models for Learner Modelling: Why LLMs Alone Fall Short for Responsible Tutoring in K--12 Education](https://mlsanity.com/paper/2512.23036/) (2025)
- [Large Language Models as Students Who Think Aloud: Overly Coherent, Verbose, and Confident](https://mlsanity.com/paper/2602.01015/) (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)
- [PAPPL: Personalized AI-Powered Progressive Learning Platform](https://mlsanity.com/paper/2508.14109/) (2025)
- [From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education](https://mlsanity.com/paper/2512.20714/) (2025)
- [LLM-based Multimodal Feedback Produces Equivalent Learning and Better Student Perceptions than Educator Feedback](https://mlsanity.com/paper/2601.15280/) (2026)
- [Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactions](https://mlsanity.com/paper/2602.20486/) (2026)
- [Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding](https://mlsanity.com/paper/2603.24535/) (2026)

## Abstract (arXiv)

Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning analytics is hybrid tutoring, which includes instructional and motivational support. Caregivers assert similar roles in homework, yet it is unknown how learning analytics can support them. Our past work with caregivers suggested that conversational support is a promising method of providing caregivers with the guidance needed to effectively support student learning. We developed a system that provides instructional support to caregivers through conversational recommendations generated by a Large Language Model (LLM). Addressing known instructional limitations of LLMs, we use instructional intelligence from tutoring systems while conducting prompt engineering experiments with the open-source Llama 3 LLM. This LLM generated message recommendations for caregivers supporting their child's math practice via chat. Few-shot prompting and combining real-time problem-solving context from tutoring systems with examples of tutoring practices yielded desirable message recommendations. These recommendations were evaluated with ten middle school caregivers, who valued recommendations facilitating content-level support and student metacognition through self-explanation. We contribute insights into how tutoring systems can best be merged with LLMs to support hybrid tutoring settings through conversational assistance, facilitating effective caregiver involvement in tutoring systems.