Abstract

Generative AI tutors have become a common tool for independent learning, yet their capacity to support self-regulated learning (SRL) is poorly understood. This simulation-based textual analysis of prompt design evaluates a frontier large language model (Claude Sonnet 4.6) as a tutor across 60 scripted sessions on a single topic (density), crossing three levels of SRL-informed system prompting (Minimal, Moderate, Extensive) with four learner-behavior variants (Standard, Misconception, Disengagement, Overconfidence). Tutoring transcripts were scored on a 14-dimension framework spanning SRL phases, SRL developmental stages, self-determination theory principles, and Merrill’s First Principles of Instruction, applied via an LLM judge. Adding SRL context to the system prompt raised total tutoring scores, but only at the Extensive SRL support level. Minimal and Moderate prompting produced the same performance, near 36 on a 70-point scale, and Extensive prompting raised it to 40, a statistically significant effect (partial η2 = 0.24). The learner’s behavior in the session had a larger effect than the prompt did (partial η2 = 0.37), with disengaged learners scoring lowest. The threshold pattern held under an independent judge from a different developer than the tutor model. The findings support a method for evaluating GenAI tutors empirically and point to dynamic, dialogue-aware prompting alongside explicit SRL scaffolding.

Keywords