# Integrating large language models into medical undergraduate laboratory course to enhance bioethical competence: a quasi-experimental study

## TL;DR

This study explores how large language models can be used in medical courses to improve students' understanding of bioethics and laboratory skills.

## Contribution

The study introduces a novel approach of integrating large language models into undergraduate medical education to enhance bioethical competence.

## Key findings

- All four LLMs helped students consolidate knowledge and improve bioethics proficiency.
- Students expressed concerns about potential inaccuracies and bias in LLM use.
- KIMI and DeepSeek were rated highest for effectiveness in specific medical majors.

## Abstract

This study investigates the integration of different large language models (LLMs) into the Medical Cell Biology Laboratory Course (MCBLC) to enhance bioethics training for undergraduate medical students in China. It further compares the effectiveness of these LLMs in improving teaching outcomes and student learning performances. Key challenges encountered during implementation were identified, and potential strategies to address them were also explored.

First-year undergraduate medical students from three medical majors were assigned to five groups. The study involved three phases: instructor-led course introduction, LLM-assisted experimental practice addressing procedural, conceptual, and psychological challenges, and post-training evaluation via questionnaires and blind-graded laboratory reports. Four domestic robust LLMs (DeepSeek, Doubao, KIMI, ChatGLM) were compared to assess their impact on bioethics integration, instructional effectiveness, and student learning outcomes, while documenting students' perceptions and concerns regarding LLM use.

The study demonstrated that all four LLMs supported first-year undergraduate medical students in consolidating foundational knowledge, enhancing bioethics proficiency during laboratory practice, and developing critical competencies for future physicians. Questionnaires from 86 students across three majors indicated generally high satisfaction. For Medical Imaging Technology students, DeepSeek (mean 4.3, SD 0.7) and KIMI (mean 4.3, SD 0.8) were rated significantly higher than Doubao (mean 3.9, SD 0.7) and ChatGLM (mean 3.3, SD 0.6). KIMI was also preferred among Health Surveillance and Quarantine (mean 4.4, SD 0.5) and Medical Prevention (mean 4.5, SD 0.5) students. Nevertheless, students expressed concerns regarding potential academic inaccuracies, bias, and possible impact on independent thinking.

This study suggested that recent LLMs, particularly KIMI and DeepSeek, may support integrating bioethics into undergraduate medical laboratory courses in a university in China. By assisting students in accessing information, reflecting on ethical issues, and navigating practical challenges, these tools can facilitate learning and foster ethical awareness, competent future physicians. These findings, as an initial exploration and context-specific, indicate that LLMs may support bioethics learning in undergraduate medical laboratory courses and help foster ethically aware, competent future physicians.