Large Language Models (LLMs) are increasingly embedded in everyday study practices, assisting with content generation, explanations, and exam preparation. LLMs are now deeply embedded in classroom instruction, facilitating content generation and explanations. However, the traditional LLM model is prone to exposing the model’s internal chain-of-thought. In this paper, a Socratic Agent is presented as auditable tutoring model that foregrounds the learner’s reasoning rather than exposing its internal chain of thought. An evaluation plan is outlined across numeric and unit-conversion tasks, diagram reading, rubric-graded responses, and conceptual probes; outcome measures cover learning and retention, metacognitive coverage, trace quality, deference compliance, and cost/latency. Limitations, ablations, and a practical path from design to evidence are also detailed.