Semantic Scholar Open Access 2023 195 sitasi

Prompt Engineering in Medical Education

Thomas F. Heston Charya Khun

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Abstrak

Artificial intelligence-powered generative language models (GLMs), such as ChatGPT, Perplexity AI, and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate feedback. However, to fully utilize GLMs, properly formulated instructions are essential. Prompt engineering is a systematic approach to effectively communicating with GLMs to achieve the desired results. Well-crafted prompts yield good responses from the GLM, while poorly constructed prompts will lead to unsatisfactory responses. Besides the challenges of prompt engineering, significant concerns are associated with using GLMs in medical education, including ensuring accuracy, mitigating bias, maintaining privacy, and avoiding excessive reliance on technology. Future directions involve developing more sophisticated prompt engineering techniques, integrating GLMs with other technologies, creating personalized learning pathways, and researching the effectiveness of GLMs in medical education.

Penulis (2)

Thomas F. Heston

Charya Khun

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Heston, T.F., Khun, C. (2023). Prompt Engineering in Medical Education. https://doi.org/10.3390/ime2030019

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Lihat di Sumber doi.org/10.3390/ime2030019

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 195×
Sumber Database: Semantic Scholar
DOI: 10.3390/ime2030019
Akses: Open Access ✓