Rethinking assessment and teaching in response to generative artificial intelligence: Unpacking the impact of technology-mediated team-based learning
DOI:
https://doi.org/10.24200/jonus.vol10iss1pp67-93Abstract
Background and Purpose: This study explores the implementation of Team-Based Learning (TBL) within a clinical reasoning module at a medical school. The objective is to assess the impact of TBL on student learning experiences and address the potential to mitigate assessment challenges heightened by Generative Artificial Intelligence (GAI) advancements.
Methodology: The study employed a mixed-methods approach, combining qualitative and quantitative analyses. Data were collected through surveys from students (n=31) who took a Clinical Reasoning module at a medical school. The university where the study was conducted is an international institution, hosting students from diverse cultural and ethnic backgrounds, including representation from the Asian continent. The Learning Activity Management System (LAMS) was used for module delivery. A thematic analysis was performed on the qualitative data, and descriptive statistics were applied to the quantitative data.
Findings: Findings indicate that TBL significantly enhances the learning experience by promoting active engagement, collaborative learning, and the development of critical thinking skills among medical students. Students reported positive experiences with teamwork, peer evaluation, and the structured nature of TBL sessions. The study also highlighted the role of TBL in maintaining assessment integrity amidst the rise of GAI tools like ChatGPT.
Contributions: TBL presents a viable framework for enhancing student engagement and maintaining the authenticity of assessments in an era increasingly dominated by Generative AI. The study advocates for the thoughtful integration of TBL in education, emphasising its potential to foster a deeper understanding of the subject matter and address evolving challenges in academic assessments.
Keywords: Team-based learning, medical education, assessment integrity, generative AI, collaborative learning, higher education.
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