Scholarlink

Background <p>Medical biochemistry, a fundamental course in medical education, has a complex and expanding knowledge base. Traditional teaching methods often fail to meet students’ needs for in-depth understanding and personalized learning. Students can become overwhelmed by the vast array of biochemical concepts, reactions, and molecular structures.</p> Objective <p>This study aims to explore the potential of generative AI tools as teaching assistants in medical biochemistry, particularly in CBL (Case-Based Learning) settings where their application is currently limited.</p> Methods <p>We conducted a comparative study involving a control group (<i>N</i> = 40) and an experimental group (<i>N</i> = 39) to assess the impact of AI tools on CBL learning. We analyzed students’ performance and compared evaluations of their work by both teachers and AI tools. Additionally, a questionnaire was used to gauge the effects of AI tools on case study learning.</p> Results <p>The experimental group using AI tools showed significantly better performance than the control group. The former completed case assignments faster (2.6&#xa0;h vs. 5.5&#xa0;h, <i>P</i> &lt; 0.05) and achieved higher exam scores (77.3 ± 4.3 vs. 66.5 ± 5.4, <i>P</i> &lt; 0.05). AI-based grading on students’ assignments closely matched teachers’ evaluations on them (<i>P</i> &gt; 0.05), demonstrating reliability in assessment. Students rated AI highly for basic knowledge acquisition (Q4M = 9.18) but noted limitations in complex clinical reasoning (Q11M = 4.20) and innovative thinking (Q12M = 3.90). Key concerns of using AI included that AI reduced interaction between teachers and students (Q1M = 7.17) and standardized AI outputs led to homogenized learning (Q6M = 6.56). Despite these drawbacks, students’ acceptance of AI increased significantly after the trial (5.5 to 7.6, <i>P</i> &lt; 0.05).</p> Conclusion <p>Generative AI tools have significantly enhanced learning efficiency and performance in CBL teaching of medical biochemistry, shortened task completion time and improved examination scores. Although there are limitations in the cultivation of innovative thinking and interaction between teachers and students, students’ acceptance of AI has increased. Therefore, AI should serve as a supplement to traditional teaching to balance the learning efficiency and creative thinking of students.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The role of generative AI tools in case-based learning and teaching evaluation of medical biochemistry

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月22日

查看全文
  • Liang Li,
  • Weiwei Zhang,
  • Kun Zhang,
  • Yuhan Yang,
  • Lan Wang,
  • Luo Zuo,
  • Yiran Sun,
  • Quekun Peng

摘要

Background

Medical biochemistry, a fundamental course in medical education, has a complex and expanding knowledge base. Traditional teaching methods often fail to meet students’ needs for in-depth understanding and personalized learning. Students can become overwhelmed by the vast array of biochemical concepts, reactions, and molecular structures.

Objective

This study aims to explore the potential of generative AI tools as teaching assistants in medical biochemistry, particularly in CBL (Case-Based Learning) settings where their application is currently limited.

Methods

We conducted a comparative study involving a control group (N = 40) and an experimental group (N = 39) to assess the impact of AI tools on CBL learning. We analyzed students’ performance and compared evaluations of their work by both teachers and AI tools. Additionally, a questionnaire was used to gauge the effects of AI tools on case study learning.

Results

The experimental group using AI tools showed significantly better performance than the control group. The former completed case assignments faster (2.6 h vs. 5.5 h, P < 0.05) and achieved higher exam scores (77.3 ± 4.3 vs. 66.5 ± 5.4, P < 0.05). AI-based grading on students’ assignments closely matched teachers’ evaluations on them (P > 0.05), demonstrating reliability in assessment. Students rated AI highly for basic knowledge acquisition (Q4M = 9.18) but noted limitations in complex clinical reasoning (Q11M = 4.20) and innovative thinking (Q12M = 3.90). Key concerns of using AI included that AI reduced interaction between teachers and students (Q1M = 7.17) and standardized AI outputs led to homogenized learning (Q6M = 6.56). Despite these drawbacks, students’ acceptance of AI increased significantly after the trial (5.5 to 7.6, P < 0.05).

Conclusion

Generative AI tools have significantly enhanced learning efficiency and performance in CBL teaching of medical biochemistry, shortened task completion time and improved examination scores. Although there are limitations in the cultivation of innovative thinking and interaction between teachers and students, students’ acceptance of AI has increased. Therefore, AI should serve as a supplement to traditional teaching to balance the learning efficiency and creative thinking of students.