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Certainty-based marking in multiple-choice assessments in physiology: a web-based implementation using an AI
Chinmay Suryavanshi1, Kirtana Raghurama Nayak1,2
1Department of Physiology, Kasturba Medical College Manipal, Manipal Academy of Higher Education, Manipal, India.
Advances in Physiology Education
|October 17, 2025
Summary
Certainty-based marking (CBM) in physiology education improved student performance and metacognitive skills. This AI-assisted assessment tool enhanced self-assessment and learning awareness in medical students.
Area of Science:
- Medical Education
- Educational Technology
Background:
- Certainty-based marking (CBM) enhances self-assessment and metacognitive awareness.
- Implementing CBM in physiology assessments can improve learning outcomes.
Purpose of the Study:
- To explore the implementation of CBM in multiple-choice assessments for first-year medical students in physiology.
- To evaluate the impact of CBM on student performance, self-assessment, and metacognitive awareness.
Main Methods:
- A web-based CBM assessment tool was developed with AI assistance (Claude 3.5).
- 15 MCQs were administered as pretest and posttest to 195 first-year medical students.
- Student performance, certainty indices, and perceptions were analyzed via surveys.
Main Results:
- Significant improvements in performance and certainty indices were observed from pretest to posttest.
- Most students found the certainty scale beneficial, leading to answer revisions and better knowledge gap recognition.
- Students reported increased metacognitive awareness, self-monitoring skills, and preferred CBM over traditional MCQs.
Conclusions:
- AI-assisted CBM functions as both an assessment and instructional tool, enhancing metacognitive awareness and self-monitoring in physiology education.
- CBM shows promise for improving knowledge retention and certainty calibration, but longitudinal studies are needed.
- AI integration in assessment design offers potential for improving educational strategies and accessibility.
