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Accepted for/Published in: JMIR Medical Education

Date Submitted: May 29, 2026
Date Accepted: Oct 13, 2026
Date Submitted to PubMed: Oct 14, 2026

The final, peer-reviewed published version of this preprint can be found here:

Curriculum-Aligned Conversational AI to Support Pathology Self-Directed Learning in Preclinical Medical Students: Crossover Randomized Pilot Study

Roark K, Choi Y, Hoy E, Tufts N, Amevor R, Ghosal R, Keisler B, Bonilha L, Chatzistamou I

Curriculum-Aligned Conversational AI to Support Pathology Self-Directed Learning in Preclinical Medical Students: Crossover Randomized Pilot Study

JMIR Med Educ 2026;12:e102908

DOI: 10.2196/102908

PMID: 42873617

Curriculum-Aligned Conversational AI to Support Pathology Self-Directed Learning in Preclinical Medical Students: A Crossover Randomized Pilot Study

  • Kasie Roark; 
  • Yumin Choi; 
  • Elizabeth Hoy; 
  • Noel Tufts; 
  • Robert Amevor; 
  • Rahul Ghosal; 
  • Brian Keisler; 
  • Leonardo Bonilha; 
  • Ioulia Chatzistamou

ABSTRACT

Background:

Artificial intelligence (AI) is reshaping the landscape of medical education, yet the widespread adoption of general-purpose AI tools in student learning raises substantial concerns about curricular alignment, information accuracy, and the absence of pedagogical scaffolding.

Objective:

This crossover pilot study evaluated a curriculum-aligned custom GPT tool that was developed to support Socratic, case-based, self-directed learning (SDL) against traditional preparation methods in second-year medical students at the University of South Carolina, Floyd School of Medicine (USCFSOM).

Methods:

Forty-four voluntary participants participated in two faculty-led pathology small group discussions (on the topics of Cushing's disease and prostate cancer) using the custom GPT or conventional resources, then switched methods for the second case. Faculty assessed SDL documentation forms and small group discussion performance using validated rubrics.

Results:

Students using the curriculum-aligned custom GPT tool achieved significantly higher SDL rubric scores (10.27 vs. 9.36 out of 12; p < 0.0001, Cohen's d = 0.71) and small group discussion scores (14.89 vs. 13.68 out of 16; p < 0.0001, Cohen's d = 0.78), with large effect sizes. They also demonstrated a borderline-significant advantage in delayed NBME-style retention (Wilcoxon signed-rank p = 0.052; paired t-test p = 0.048) and significantly greater self-reported gains in understanding and pathophysiological reasoning. Seventy-seven percent reported improved reasoning clarity, and 70% improved conceptual understanding. Safety analysis revealed minimal risk across five domains. Low performers showed the greatest retention benefit.

Conclusions:

These findings support the integration of curriculum-aligned custom GPTs into second-year pathology SDL curricula and provide evidence that curriculum-aligned AI tools can safely and effectively enhance self-directed learning, clinical reasoning, and learner engagement in undergraduate medical education. Clinical Trial: NA


 Citation

Please cite as:

Roark K, Choi Y, Hoy E, Tufts N, Amevor R, Ghosal R, Keisler B, Bonilha L, Chatzistamou I

Curriculum-Aligned Conversational AI to Support Pathology Self-Directed Learning in Preclinical Medical Students: Crossover Randomized Pilot Study

JMIR Med Educ 2026;12:e102908

DOI: 10.2196/102908

PMID: 42873617

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