Academic Misconduct
Improper use of LLM systems (i.e., abuse of LLM systems) will cause adverse social impacts, such as academic misconduct.
ENTITY
1 - Human
INTENT
1 - Intentional
TIMING
2 - Post-deployment
Risk ID
mit15
Domain lineage
4. Malicious Actors & Misuse
4.3 > Fraud, scams, and targeted manipulation
Mitigation strategy
1. Prioritize pedagogical redesign to diminish the utility of LLM misuse. This involves transitioning assessment methods from low-cognitive-load recall tasks to authentic, higher-order cognitive processing (e.g., comparison, analysis, evaluation) via non-reproducible or context-specific prompts and incorporating mandatory explanation components for student reasoning. 2. Establish and rigorously communicate a clear, updated academic integrity policy. This policy must explicitly define acceptable and prohibited uses of Generative AI/LLMs for each assignment, require students to formally declare any use of such tools, and ensure consistent enforcement by transparently publicizing the disciplinary consequences of confirmed misconduct. 3. Implement a hybrid, human-integrated approach to academic integrity assurance. This strategy involves utilizing AI-detection tools solely to flag potential concerns, with the final determination of misconduct always requiring comprehensive human review, contextual evidence, and an open dialogue with the student to ensure fairness and mitigate the risk of false positives.