Presentation Description
Maxim Morin1
Kim Ashwin2, Paul Glover3, Jon Dupre3 and Jen Desrosiers2
1 Medical Council of Canada
2 Australian Medical Council
3 risr/
Kim Ashwin2, Paul Glover3, Jon Dupre3 and Jen Desrosiers2
1 Medical Council of Canada
2 Australian Medical Council
3 risr/
Background.
Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in various fields, including high-stakes certification exams (Nie et al., 2023). However, research and practice in this field must actively explore and evaluate the role of these technologies to ensure fair, valid, and efficient examination processes.
Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in various fields, including high-stakes certification exams (Nie et al., 2023). However, research and practice in this field must actively explore and evaluate the role of these technologies to ensure fair, valid, and efficient examination processes.
Summary of work.
This oral presentation explores the current and potential uses of AI and ML, including in areas such as preparatory materials, exam content development, exam delivery, exam scoring, and exam analytics. It will examine the potential benefits, challenges, and implications of leveraging these technologies in high-stakes certification exams (Alrassi et al., 2021).
This oral presentation explores the current and potential uses of AI and ML, including in areas such as preparatory materials, exam content development, exam delivery, exam scoring, and exam analytics. It will examine the potential benefits, challenges, and implications of leveraging these technologies in high-stakes certification exams (Alrassi et al., 2021).
Importance for research and practice.
It aims to inform the design and implementation of AI and ML technologies in high-stakes certification exams. They will also guide future research, policy development, and practice improvement in this evolving field.
It aims to inform the design and implementation of AI and ML technologies in high-stakes certification exams. They will also guide future research, policy development, and practice improvement in this evolving field.
Take-home messages, outcomes, and implications for further research and practice:
- Enhanced understanding of the role of AI and ML in high-stakes certification exams.
- Identification of the use cases for AI and ML technologies and their potential benefits.
- Exploration of best practices and guidelines for incorporating AI and ML into exam design, development, and evaluation processes.
- Identification of research gaps and the need for further studies to evaluate the validity, reliability, and fairness of AI and ML-powered certification exams.
References (maximum three)
Alrassi, J., Katsufrakis, P. J., & Chandran, L. (2021). Technology Can Augment, but Not Replace, Critical Human Skills Needed for Patient Care. Academic Medicine, 96(1). https://journals.lww.com/academicmedicine/Fulltext/2021/01000/Technology_Can_Augment ,_but_Not_Replace,_Critical.33.aspx
Alrassi, J., Katsufrakis, P. J., & Chandran, L. (2021). Technology Can Augment, but Not Replace, Critical Human Skills Needed for Patient Care. Academic Medicine, 96(1). https://journals.lww.com/academicmedicine/Fulltext/2021/01000/Technology_Can_Augment ,_but_Not_Replace,_Critical.33.aspx
Masters, K. (2023). Ethical use of Artificial Intelligence in Health Professions Education: AMEE Guide No. 158. Medical Teacher, 45(6), 574–584. https://doi.org/10.1080/0142159X.2023.2186203
Nie, R., Guo, Q., & Morin, M. (2023). Machine Learning Literacy for Measurement Professionals: A Practical Tutorial. Educational Measurement: Issues and Practice, 42(1), 9– 23. https://doi.org/https://doi.org/10.1111/emip.12539