JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is increasingly integrated into medical education, offering new ways for students to acquire knowledge and support clinical reasoning. However, the extent, patterns, and implications of AI use among medical students remain incompletely understood.

Objective:

This study aimed to quantify real-time artificial intelligence use among medical students, including the proportion of study time devoted to AI, and to examine how use varies by training stage and engagement style (active vs passive).

Methods:

This longitudinal study recruited medical students from two osteopathic medical schools to complete a baseline survey and seven digital diary entries over a 21-day period. The diary method was designed to capture real-time AI use and reduce recall bias. Data were analyzed using Stata 19. Multiple linear regression models examined associations between AI use (total minutes and percentage of study time) and key variables, including year in training and type of use (active vs passive), adjusting for age, gender, and campus.

Results:

A total of 71 students completed the baseline survey (mean age 26.6 years, SD 2.8; 54.9% male; 77.4% pre-clinical). On average, students reported using AI tools during 19.4% of their total study time. Clinical-phase students (MS3–MS4) used AI significantly more than pre-clinical students (MS1–MS2), with an adjusted increase of 19.0 percentage points (p=0.003). Students classified as active users spent significantly more total time using AI than passive users (p=0.002). Across groups, AI use was primarily passive, including simplifying complex concepts, answering practice questions, and generating summaries. Clinical-phase students were more likely to use AI for practice questions.

Conclusions:

Medical students are incorporating AI into a substantial proportion of their study time, with greater use among clinical trainees and those engaging actively with these tools. Despite this, most use remains passive. Given mixed evidence regarding the impact of AI on deep learning and potential risks related to uncritical acceptance of AI-generated content, these findings highlight the need for further research on learning outcomes. Medical schools may benefit from providing guidance on responsible AI use, including critical evaluation, verification of outputs, and integration into evidence-based study strategies. The digital diary methodology offers a novel and practical approach for capturing real-time AI use and may inform future educational research and intervention design.