Reflective Dialogue or Prompt Refinement? Effects of Tutor Scaffolding on Students' Independent LLM Use for Programming

A study in a graduate-level mobile robotics course found that a Socratic-Guidance (SG) tutor, which uses dialogic questioning, led to higher learning gains and more understanding-driven prompting strategies compared to a Prompt-Refinement (PR) tutor when students later used an unconstrained LLM. The SG tutor was perceived as less efficient but better supported students' capacity to learn with LLMs over time.

arXiv:2607.03303v1 Announce Type: new Abstract: While Large Language Models LLMs can provide personalized support in learning, several studies have raised concerns regarding their use in education. Importantly, learning depends on how students engage with LLMs. This study examined how two types of LLM-based tutors shape students' prompting practices, learning, and subsequent LLM-use: a Socratic-Guidance SG tutor, which structures interaction through dialogic questioning, and a Prompt-Refinement PR tutor that guides the formulation of effective prompts. We conducted a two-phase study in a graduate-level mobile robotics course: 66 students used either the SG or PR tutor during a 6-week intervention, followed by 52 students using an unconstrained LLM during a 3-week course project. Results show that while the SG- and PR tutors led to similar task performance and prompting patterns during guided use, they differ in learning outcomes and later LLM-use. SG-students, relative to PR-student, achieved higher learning gains in later sessions, and were more likely to adopt understanding-driven prompting strategies, which are predictive of higher understanding, when using an unconstrained LLM. Although learners perceived the SG tutor as less efficient, the findings suggest that Socratic guidance supports the development of students' capacity to learn with LLMs over time, highlighting its importance for LLM tutor design.