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

TL;DR

This paper explores integrating large language models with tutoring systems to support caregivers in homework assistance, demonstrating effective conversational recommendations that enhance caregiver involvement and student learning outcomes.

Contribution

It introduces a novel method combining LLMs with tutoring system intelligence to generate effective conversational guidance for caregivers supporting student homework.

Findings

Caregivers valued recommendations that support content understanding and metacognition.

Few-shot prompting with real-time context improves LLM-generated tutoring messages.

Caregivers found the system helpful for providing targeted homework support.

Abstract

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.