  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.
