IPHS 300: AI for Humanity
Document Type
Poster
Publication Date
Spring 2026
Abstract
Preparing for the Medical College Admission Test (MCAT) is among the most demanding academic challenges facing pre-medical students, yet access to professional tutoring remains prohibitively expensive for many. This paper describes the design, implementation, and iterative evaluation of an AI-powered tutoring system built using Claude Code, Anthropic’s command-line interface for the Claude large language model, intended to serve pre-medical students directly, whether or not they have access to a professional tutor. The system integrates three components: a personalized multi-phase study schedule generator, a daily check-in assistant, and an AI-driven instructional slideshow generator for targeted content review. Each component was designed by a professional MCAT tutor and refined across multiple cycles of expert review. Evaluation assessed the system’s capacity to guide a student through the full arc of MCAT preparation from initial onboarding through content review and into the final practice phase with attention to legibility, pedagogical correctness, and practical usefulness. Results demonstrate that the system can meaningfully replicate or extend the work of a human tutor, making structured, expert-validated MCAT preparation accessible to a broader population of pre-medical students.
Recommended Citation
Latterman, Luke, "AI MCAT Tutor: Personalized Study Planning, Daily Check-Ins, & Slideshow Generator for Pre-Medical Students" (2026). IPHS 300: AI for Humanity. Paper 63.
https://digital.kenyon.edu/dh_iphs_ai/63
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.