By Carolina Kuepper-Tetzel
In May I attended an all-day ‘AI in Assessment’ workshop with a focus on Psychology degrees in Higher Education in London. Researchers from the UK and Ireland shared insights on students’ attitudes on AI, rethinking assessment approaches, and general considerations on navigating technological progress. I left the workshop thinking: I need to write this up because these insights apply to the wider educational sector! In today’s post, I feature research from Dr Mark Carrigan (University of Manchester), Dr Patricia Gasalla Canto (Cardiff University), Dr Laura Contu (University of Bristol), Dr Michael Smyth (University of Bristol), Dr Kirsty Dunn (Lancaster University), Dr Lara Warmelink (Lancaster University), and Prof Oliver McGarr (University of Limerick).
Keynote speaker Mark Carrigan set the tone by highlighting that the crisis of AI in education is a crisis of trust. When you think about it, the introduction of AI led to an increase in mistrust among all parties involved: Students don’t trust each other, staff don’t trust each other, staff don’t trust students and vice versa. This mistrust has also been revealed on a greater institutional level in work by Patricia Gasalla Canto and colleagues with students revealing institutional trust issues.
The crisis of mistrust led to two things:
We immediately entered ‘cheater detection mode’.
We generated preventative solutions to AI.
However, both approaches fail to realistically address the problem and hinder meaningful learning about and with new technologies. Carrigan also urged educators to approach this crisis in a more diagnostic way and explore why students may be using AI in non-optimal ways by taking external factors into consideration (e.g., time/resources poverty, caring responsibilities). Oliver McGarr suggested instead that we need to move away from the cheater rhetoric and instead explore innovative ways to adapt to changes in education that are brought on by technological innovations. He outlined the four phases of technology governance that help explain our reactions to novel technologies in general, but that can specifically be applied to AI in education, too:
Phase 1: Restriction & bans
What it looks like: Prohibit the use of AI
Examples: Invigilated assessments; restrict access to websites
Phase 2: Detection & policing
What it looks like: Monitor student submissions and address AI issues
Examples: Apply criteria to detect inappropriate AI use (e.g., referencing, unusual content/analyses)
Phase 3: Retrofitting assessments
What it looks like: Modify traditional assessments to make them less prone to AI
Examples: Design assessments that require more creativity and critical thinking; increase difficulty of questions
Phase 4: Re-imagining assessments
What it looks like: Redesign assessment approaches that meaningfully integrate AI
Examples: Shift to real-world project-based, experiential learning and use AI as collaborator; assess the process not the outcome
