Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

TL;DR

This paper presents Tailor-Mind, an interactive visualization system powered by fine-tuned LLMs, designed to support self-regulated learning in AI education, demonstrating improved learning experiences through model tuning and user interaction.

Contribution

It introduces a framework for integrating fine-tuned LLMs into visualization systems for education, specifically tailored to enhance self-regulated learning for beginners.

Findings

Tailor-Mind improves self-regulated learning experiences.

Model tuning and interactive features enhance educational effectiveness.

User studies validate the framework's effectiveness.

Abstract

Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework.