Medical education has ventured into a new arena of computer-assisted teaching powered by artificial intelligence (AI).In medical institutions, AI can serve as an intelligent tool facilitating the decision-making process effectively.AI can enhance teaching by assisting in developing new strategies for educators.Similarly, students also benefit from intelligent systems playing the role of competent teachers.Thus, AIintegrated medical education paves new opportunities for advanced teaching and learning experiences and improved outcomes.On the other hand, optical mark recognition and automated scoring are ways AI can also transform into a real-time assessor and evaluator in medical education.This review summarizes the AI tools and their application in medical teaching or learning, assessment, and administrative support.This article can aid medical institutes in planning and implementing AI according to the needs of the educators. 

Artificial Intelligence Revolutionizing the Field of Medical Education

With the increasing popularity of artificial intelligence (AI) applications in medical practices, the integration of AI technologies into medical education has garnered significant attention. However, there exists a noticeable research gap when it comes to providing comprehensive guidelines and recommendations for its successful integration into this domain. Addressing this research gap is crucial as the responsible and effective incorporation of AI in medical education not only ensures that current and future healthcare professionals are well-prepared for the demands of modern medicine but also upholds ethical standards, maximizes the potential benefits of AI, and minimizes potential risks. The objective of this chapter is to fill this gap by offering practical tips and actionable insights for incorporating AI into medical education, encompassing practical, ethical, pedagogical, and professional implications. Consequently, it equips medical educators and learners alike with the knowledge and tools necessary to navigate the evolving landscape of medical education in the age of AI. 

Effective Integration of Artificial Intelligence in Medical Education

Introduction Since its release by OpenAI in November 2022, numerous studies have subjected ChatGPT to various tests to evaluate its performance in medical exams. The objective of this study is to evaluate ChatGPT's accuracy and logical reasoning across all 10 subjects featured in Stage 1 of Senior Professional and Technical Examinations for Medical Doctors (SPTEMD) in Taiwan, with questions that encompass both Chinese and English. Methods In this study, we tested ChatGPT-4 to complete SPTEMD Stage 1. The model was presented with multiple-choice questions extracted from three separate tests conducted in February 2022, July 2022, and February 2023. These questions encompass 10 subjects, namely biochemistry and molecular biology, anatomy, embryology and developmental biology, histology, physiology, microbiology and immunology, parasitology, pharmacology, pathology, and public health. Subsequently, we analyzed the model's accuracy for each subject. Result In all three tests, ChatGPT achieved scores surpassing the 60% passing threshold, resulting in an overall average score of 87.8%. Notably, its best performance was in biochemistry, where it garnered an average score of 93.8%. Conversely, the performance of the generative pre-trained transformer (GPT)-4 assistant on anatomy, parasitology, and embryology was not as good. In addition, its scores were highly variable in embryology and parasitology. Conclusion ChatGPT has the potential to facilitate not only exam preparation but also improve the accessibility of medical education and support continuous education for medical professionals. In conclusion, this study has demonstrated ChatGPT's potential competence across various subjects within the SPTEMD Stage 1 and suggests that it could be a helpful tool for learning and exam preparation for medical students and professionals.

Performance of ChatGPT on Stage 1 of the Taiwanese medical licensing exam

With AI's advancing technology, pedagogical changes are occurring, and chatbots are becoming more intertwined in our daily lives. While these can be used in a variety of disciplines, they play a particularly significant role in the digital transformation of education. We present a scoping review of chatbots in higher education (HE), investigating where chatbots are being applied. The main benefits and challenges of chatbots were explored. The quality of the selected papers was assessed using the International Organization for Standards (ISO) 9241–11 guidelines. A total of 2692 citations were found while searching recognized digital databases. After reading the abstract and full text, 64 publications were considered. Backward and forward reference checking yielded four further studies. Altogether, 66 studies were included. This review provides a comprehensive overview of research on using chatbots in HE, including advantages, and challenges. Research demonstrates the versatility and the promising aspects of this type of support system for university education. 

Here's to the future: Conversational agents in higher education- a scoping review

Technologies like ChatGPT and other AI tools have impacted learning by giving students more chances to ask questions and explore knowledge. The inclusion of Non-Player Characters (NPCs) as scaffolding in game-based situated learning activities can have a positive impact on learning. The application of ChatGPT to role-playing has potential; therefore, this study designed a “ChatGPT-based NPCs scaffolding Workflow Framework” and used it as the basis for designing and evaluating an educational game for employee ethics training with empirical evidence. This study had 61 participants, divided into a document scaffolding group ( n = 32) and a ChatGPT-based NPC group ( n = 29) and examined the learning achievement, flow, motivation, and anxiety in the two groups. The results showed that the designs of both groups benefited learning achievement, and both groups could maintain a certain level of high motivation and engagement. Through qualitatively analyzing the content of students’ discussions with NPCs, it was found that there is potential for NPC-assisted learning through ChatGPT. Overall, this study explored the efficacy and limitations of using ChatGPT-based NPCs as scaffolding in game-based learning and found that it is extensible. We also present a framework for the design of ChatGPT-based NPCs scaffolding mechanism. 

A Mobile Contextualized Educational Game Framework With ChatGPT Interactive Scaffolding for Employee Ethics Training

Objectives Chatbots, conversational agents that walk medical students (MS) though a clinical case, are serious games that seem to be appreciated by MS. Their impact on MS’s performance in exams however was not yet evaluated. Chatprogress is a chatbot-based game developed at Paris Descartes University. It contains 8 pulmonology cases with step-by-step answers delivered with pedagogical comments. The CHATPROGRESS study aimed to evaluate the impact of Chatprogress on students’ success rate in their end-term exams. Methods We conducted a post-test randomized controlled trial held on all fourth-year MS at Paris Descartes University. All MS were asked to follow the University’s regular lectures, and half of them were randomly given access to Chatprogress. At the end of the term, medical students were evaluated on pulmonology, cardiology and critical care medicine. Main outcomes measures The primary aim was to evaluate an increase in scores in the pulmonology sub-test for students who had access to Chatprogress, compared to those who didn’t. Secondary aims were to evaluate an increase in scores in the overall test (Pulmonology, Cardiology and Critical care medicine test (PCC)) and to evaluate the correlation between access to Chatprogress and overall test score. Finally, students’ satisfaction was assessed using a survey. Results From 10/2018 to 06/2019, 171 students had access to Chatprogress (the Gamers) and among them, 104 ended up using it (the Users). Gamers and Users were compared to 255 Controls with no access to Chatprogress. Differences in scores on the pulmonology sub-test over the academic year were significantly higher among Gamers and Users vs Controls (mean score: 12.7/20 vs 12.0/20, p = 0.0104 and mean score: 12.7/20 vs 12.0/20, p = 0.0365 respectively). This significant difference was present as well in the overall PCC test scores: (mean score: 12.5/20 vs 12.1/20, p = 0.0285 and 12.6/20 vs 12.1/20, p = 0.0355 respectively). Although no significant correlation was found between the pulmonology sub-test’s scores and MS’s assiduity parameters (number of finished games among the 8 proposed to Users and number of times a User finished a game), there was a trend to a better correlation when users were evaluated on a subject covered by Chatprogress. MS were also found to be fans of this teaching tool, asking for more pedagogical comments even when they got the questions right. Conclusion This randomised controlled trial is the first to demonstrate a significant improvement in students’ results (in both the pulmonology subtest and the overall PCC exam) when they had access to Chatbots, and even more so when they actually used it.

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Chatbot-based serious games: A useful tool for training medical students? A randomized controlled trial

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Chatbot-based serious games: A useful tool for training medical students? A randomized controlled trial