Swansea’s 5P approach recognises a growing consensus across the sector on how to make assignments more robust in the age of Gen AI. The teaching and assessment model provides ethical and creative uses of GenAI while minimising the risks of academic misconduct. It can be used across disciplines and as a catalyst for cross-disciplinary collaboration. Read the 5P Model below, or download the PDF version.
The 5P Approach - NAVIGATING ASSESSMENT DESIGN AND ACADEMIC INTEGRITY IN THE AGE OF GENERATIVE AI
The Approach
The approach works by preventing students from getting into situations where they might do something with GenAI that counts as academic misconduct.
It does that by moving students away from their PCs where they are often alone and working invisibly.
Instead students are in shared spaces, visibly collaborating and interacting with experts - and writing about what they do, the opportunities they encounter and the problems they solve.
The 5P Approach is made up of 5 steps listed below.
The 5P Approach
Process and Skills
People
Places
Perspectives
Presentations
Shift focus from evaluating the final output or “answer” to a question, value the diverse processes learners use. Include methodology used to develop and present the final output, add expectations to a rubric/marking scheme for the assessment. Encourage learners to draw on deeper analytical thinking, removing the opportunity to add an AI response to an assignment title. This supports transparency and cultivates awareness of methodological choices.
By interacting with practitioners and academics in a range of disciplines, learners can discover new insights about their own discipline. Connections and contacts that are forged during in-person encounters can also transfer into when they go into the workplace.
Encourage research in accessible, real-world environments eg. stated locality/environment, archives, schools, or libraries - they can explore and report how their research questions and disciplinary knowledge intersect with real world local contexts. Place-based learning approach supports multi-disciplinary inquiry, prompting learners to connect their knowledge and theoretical frameworks with practical, situated experiences.
Position AI as a catalyst for interdisciplinary thinking- pairing learners from different disciplines and inviting them to use GenAI to simulate dramatised conversations between their disciplines. Learners gain epistemic insight and particularly, interdisciplinary epistemic insight by prompting edgy debates about truth and trust within and across their disciplines. This strategy can also encourage learners to think critically about ways we can use GenAI.
Includes opportunities for learners to present their work to authentic audiences, eg. faculty research conferences, in-class or poster presentations. Through peer and tutor feedback, this supports the development of dialogue and scholarly identity, and the experiences help learners develop communication skills and confidence in articulating ideas across disciplinary boundaries. It is imperative to follow the University Reasonable Adjustment Policy.