⚡ Quick Summary
A recent study explored the perceptions of medical students regarding the use of large language models (LLMs) in their education. The findings suggest that while LLMs can enhance learning, there are significant concerns about their reliability and privacy implications.
🔍 Key Details
- 👩🎓 Participants: 25 medical students from eight Indian states
- 🗣️ Methodology: In-depth interviews with open-ended questions
- ⏱️ Average Interview Duration: 55.28±18.04 minutes
- 🔍 Analysis Tool: QDA Miner Lite v.2.0.8
🔑 Key Takeaways
- 📚 Usage Scenarios: Students utilized LLMs for clarifying complex topics and solving MCQs.
- 💡 Augmented Learning: LLMs provided customized answers and simplified notes.
- ⏳ Time-Saving: Students appreciated the efficiency in completing assignments.
- ⚠️ Concerns: Issues regarding erroneous results and overreliance on chatbots were highlighted.
- 🔒 Privacy Issues: Students expressed concerns about the reliability and privacy of LLMs.
- 🛠️ Need for Training: Emphasis on the necessity for training to integrate LLMs effectively in education.
- 🌟 Potential Benefits: Students believe LLMs can significantly enhance medical education.
📚 Background
The integration of artificial intelligence (AI) in education has gained traction, particularly in medical fields. Large language models represent a breakthrough in generative AI, capable of understanding and generating human-like text. Their potential to assist in medical education is promising, yet understanding student perceptions is crucial for effective implementation.
🗒️ Study
This qualitative study involved conducting in-depth interviews with medical students across India. The aim was to gather insights on their experiences and perceptions regarding the use of LLMs in their educational journey. The thematic analysis revealed three major themes: usage scenarios, augmented learning, and limitations of LLMs.
📈 Results
The analysis identified that students frequently used LLMs for various educational tasks, appreciating their ease of access and clarity in explanations. However, concerns about erroneous outputs and the potential for overreliance were significant. The need for structured training to integrate LLMs into the curriculum was also emphasized.
🌍 Impact and Implications
The findings of this study highlight the transformative potential of LLMs in medical education. By addressing the challenges and leveraging the strengths of these technologies, educators can enhance learning experiences. This could lead to a more efficient and effective educational environment, ultimately benefiting future healthcare professionals.
🔮 Conclusion
The study underscores the potential of large language models to enrich medical education while also pointing out the need for caution regarding their limitations. As medical education evolves, integrating LLMs thoughtfully and training students to use them effectively will be essential for maximizing their benefits.
💬 Your comments
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A qualitative survey on perception of medical students on the use of large language models for educational purposes.
Abstract
Large language models (LLMs)-based chatbots use natural language processing and are a type of generative artificial intelligence (AI) that are capable of comprehending user input and generating output in various formats. They offer potential benefits in medical education. This study explored the student’s feedback on the utilization of LLMs in medical education. We conducted an in-depth interview with open-ended questions with Indian medical students via telephone conversation. The recording (average time 55.28±18.04 min) was transcribed and thematically analyzed to find major themes and sub-themes. We used QDA Miner Lite v.2.0.8 (Provalis Research: Montreal, Canada) for the thematic analysis of the text. A total of 25 students from eight Indian states studying from the first to final year of studies participated in this study. Three major themes were identified about usage scenario, augmented learning, and limitation of LLMs. Students use LLMs for clarifying complex topics, searching for customized answers, solving MCQs, making simplified notes, and streamlining assignments. While they appreciated the ease of access, ready reference for getting clarity on doubts, lucid explanation of questions, and time-saving aspects of LLMs, concerns were raised regarding erroneous results, limited usage due to reliability and privacy issues, and the overreliance on chatbots for educational needs. Hence, they emphasized the need for training for the integration of LLM in medical education. In conclusion, according to students’ perception, LLMs have the potential to enhance medical education. However, addressing challenges and leveraging the strengths of LLMs are crucial for optimizing their integration into medical education.
Author: [‘Mondal H’, ‘Karri JKK’, ‘Ramasubramanian S’, ‘Mondal S’, ‘Juhi A’, ‘Gupta P’]
Journal: Adv Physiol Educ
Citation: Mondal H, et al. A qualitative survey on perception of medical students on the use of large language models for educational purposes. A qualitative survey on perception of medical students on the use of large language models for educational purposes. 2024; (unknown volume):(unknown pages). doi: 10.1152/advan.00088.2024