Self-assessment of active learning and critical thinking during problem-based learning: An exploratory study.

OBJECTIVE: To assess whether problem-based learning enhances students' perception of their active learning and critical thinking skills with time, and to validate the self-assessment scale regarding active learning and critical thinking in the local context. METHODS: The exploratory study was conducted at the Bahria University Dental College, Karachi, from February to September 2020, and comprised dental undergraduates in their first and second years of the academic programme. Data was collected using the self-assessment scale on active learning and critical thinking questionnaire which was administered twice after problem-based learning tutorials. Data was analysed using SPSS 23. RESULTS: There was a significant difference in the mean questionnaire scores of modules 1 and 3 of the first-year batch and modules 4 and 6 of the second-year batch (p=0.001). Cronbach's alpha value was 0.735 in the first-year cohort and 0.802 in the second-year cohort. CONCLUSIONS: Dental students' perception of active learning and critical thinking increased with time using problem-based learning as the tool. The self-assessment scale on active learning and critical thinking was also found validated in the local context.

The Revival of Essay-Type Questions in Medical Education: Harnessing Artificial Intelligence and Machine Learning.

OBJECTIVE: To analyse and compare the assessment and grading of human-written and machine-written formative essays. STUDY DESIGN: Quasi-experimental, qualitative cross-sectional study. Place and Duration of the Study: Department of Science of Dental Materials, Hamdard College of Medicine & Dentistry, Hamdard University, Karachi, from February to April 2023. METHODOLOGY: Ten short formative essays of final-year dental students were manually assessed and graded. These essays were then graded using ChatGPT version 3.5. The chatbot responses and prompts were recorded and matched with manually graded essays. Qualitative analysis of the chatbot responses was then performed. RESULTS: Four different prompts were given to the artificial intelligence (AI) driven platform of ChatGPT to grade the summative essays. These were the chatbot's initial responses without grading, the chatbot's response to grading against criteria, the chatbot's response to criteria-wise grading, and the chatbot's response to questions for the difference in grading. Based on the results, four innovative ways of using AI and machine learning (ML) have been proposed for medical educators: Automated grading, content analysis, plagiarism detection, and formative assessment. ChatGPT provided a comprehensive report with feedback on writing skills, as opposed to manual grading of essays. CONCLUSION: The chatbot's responses were fascinating and thought-provoking. AI and ML technologies can potentially supplement human grading in the assessment of essays. Medical educators need to embrace AI and ML technology to enhance the standards and quality of medical education, particularly when assessing long and short essay-type questions. Further empirical research and evaluation are needed to confirm their effectiveness. KEY WORDS: Machine learning, Artificial intelligence, Essays, ChatGPT, Formative assessment.