Can I get a second opinion? GenAI in Sports Medicine
In Neuromusculoskeletal Radiology, this redesigned assessment sees students turn a clinical case into a concise CARE-style manuscript and weigh GenAI produced second opinions against their own judgement. The focus is on evaluative judgement, clinical reasoning and ethical awareness, including the role and limits of GenAI in contemporary practice.
Summary
As part of an assessment redesign, GenAI was incorporated to provide a second-opinion diagnosis. Students analysed anonymised cases using platforms like ChatGPT or Copilot, critically comparing GenAI advice with their own diagnosis. Students reflected on GenAI's impact in clinical decision-making, and addressing ethical and practical implications.
The task aimed to foster evaluative judgement, ethical awareness, and clarity of expression. Alternative options were provided for students who wished to opt out of using GenAI.
Subject information
Subject
Year and Semester used
Cohort
Activity/assessment overview
- Assessment 4: Written report
- Weight: 40 %
- Length: 1500 words (+/- 10%)
The redesigned assessment 4 integrated Generative AI, moving away from a traditional, static report format. Previously, students worked through a single case study, culminating in an infographic (Assessment 3) and a lengthy written report (Assessment 4). The new approach aimed to foster deeper engagement and higher-order thinking by having students use Generative AI as a “second opinion” on anonymized radiology cases from their own practice.
Learning outcomes
Students will develop and demonstrate the ability to integrate all learning outcomes for this subject into a case example from their practice
GenAI usage
GenAI tool(s) used
Students could select any Generative AI platform of their choice (for example, ChatGPT, Copilot, Claude, Gemini).
How GenAI was integrated
Clear, step-by-step instructions were provided to guide students in using GenAI as part of the assessment workflow, including prompt design, image upload, critical evaluation of AI-generated outputs, referencing and rubric.
Level of student engagement
Individual assessment.
Pedagogical approach
Teaching strategy
Summative assessment.
Type of activity
Assessment.
Scaffolding and support provided
Multiple brainstorming sessions to:
- Clarify context and purpose
- Test and refine GenAI prompts
- Develop student instructions and implementation strategy
- Prioritising inclusion
- Identify evaluation methods.
Ethics
Students were given an alternative assessment format if they chose to opt out of using GenAI
Outcomes and reflections
This pilot project was a valuable learning journey for all of us (the Subject Coordinator and the Learning design and assessment team) as we learned together, driven by the same passion and curiosity. It is always encouraging when academics are equally eager to explore and engage in trialling new ideas that align with sound pedagogical approaches.
Through multiple brainstorming sessions, we engaged in in-depth discussions to identify key points within the subject where students could meaningfully apply this technology to enhance their critical thinking, achieve the intended learning outcomes, and adopt a forward-looking, future-oriented approach to learning.
In collaboration, we co-developed clear student instructions, a detailed assessment rubric, and an alternative assessment format designed to promote inclusiveness and ensure equitable learning opportunities for all students. Student feedback is forthcoming.
Contributors
- Sonja Moore, senior lecturer (Department of Physiotherapy)
- Timsy Gupta, learning designer
- Reuben Fry, learning designer
- Kate Batten, student GenAI innovator