Challenges in Adopting AI-Proof Assessments for Academic Evaluation
Explore the concerns and responses related to implementing AI-proof assessments for academic evaluations, including issues with malpractice, student feedback, scalability, and the quest for reliability and fairness amid technological advancements.
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Presentation Transcript
Issues with the move to AI- proof assessments Neil McGregor University of Manchester, Centre for Academic English
Departure context of response Via - Current trial Destination issues & next steps
Context of response Lots of activity Generally positive Greater interest in alternative assessments Some general guidance AI-assisted malpractice treated same as other malpractice No definitive decisions
Current trial Critical reviews Used across the university Easy target for AI malpractice + Understanding of material + Ability to think critically ILO 1:Assess and evaluate psychological theories and practices as they relate to organisational problems and decision-making ILO2: Critically evaluate published international business research. Time consuming Only 1 or 2 texts per course
Task 1. Generate reviews 2. Critically evaluate the reviews 3. Report evaluation
Issues and responses Feedback from students but writingis my problem Scalability Automate marking as much as possible Return to sit down exams Reliability Contextual factors affect test-retest Fairness equal access for all or for none
Issues and responses Validity & Constructive alignment ILO3:To be able to critically evaluate published research reports. ILO4:Participants should be able to show enhanced skills in written, visual and oral presentation but ILO5: use communications and information technology in acquiring, analysing, and communicating information X
