Impact on education: plagiarism
Easy access to high-quality generative models might result in students that use AI models to plagiarize existing work intentionally or unintentionally.
ENTITY
1 - Human
INTENT
3 - Other
TIMING
2 - Post-deployment
Risk ID
mit1329
Domain lineage
4. Malicious Actors & Misuse
4.3 > Fraud, scams, and targeted manipulation
Mitigation strategy
1. Redesign Assessment Methods for Authenticity and Process Transparency Revise assignments from generic, text-based responses to authentic, human-centered tasks (e.g., in-class debates, oral thesis defenses, or context-specific case studies) that leverage student-specific knowledge and real-time critical thinking. Furthermore, implement multi-stage, scaffolded assignment structures that require documented evidence of the writing process (e.g., outlines, drafts, and revision history) to ensure original student engagement and inhibit the submission of wholly AI-generated content. 2. Institute and Enforce Explicit AI Usage Policies Academic institutions must develop and clearly communicate comprehensive policies detailing the acceptable use, citation requirements, and attribution protocols for generative AI within each course's curriculum and syllabus. This transforms the use of these tools from a clandestine threat into an ethically-governed, transparent practice. 3. Leverage Technology for Insight and Deterrence, Supplemented by Educator Intervention Employ advanced AI writing detection and plagiarism tools as a component of an overall academic integrity system, focusing on their function to gain insight into a student's writing journey and to act as a deterrent. Crucially, any suspicious technological findings must be corroborated by direct educator intervention, such as reviewing platform revision histories, comparing against previous student work, and conducting student interviews to discuss the research and writing process before imposing disciplinary action.