October 2025

Resources and conversations around GenAI and academic integrity

The emergence of Generative Artificial Intelligence (GenAI) has fundamentally reshaped the academic landscape across higher education, prompting a candid discussion among instructors about its pedagogical and ethical implications. One particular area of concern centers on maintaining academic integrity in an environment where AI tools are increasingly available for use in students’ work processes.

In last spring’s UMN Instructor GenAI Focus Group, instructors shared this concern for students to outsource their learning to this technology. Instructors, and even students in the UMN Student GenAI Focus groups, are concerned that the use of GenAI will undermine the development of essential foundational academic skills like critical thinking, independent reasoning, and original idea generation. 

A significant challenge identified in both the student and instructor focus groups is the inconsistency and ambiguity surrounding appropriate use of GenAI in learning. When instructors fail to provide clear guidelines, students are left to make their own assumptions about what constitutes acceptable use, leading to potential academic dishonesty issues.

The concerns are further complicated when instructors try to use GenAI detection tools to vet student work for originality. The University does not centrally support or recommend these tools due to their documented risks, including unreliability, the potential for false positives, and bias against non-native English writers. There are also significant data privacy concerns when submitting student work to unvetted third-party services, which infringes on student rights and potentially uses students’ work to train future AI models. This combination of risks and data concerns means instructors cannot easily rely on GenAI detection tools to monitor use, thus forcing an alternate approach.

All of the changes and new considerations precipitated by GenAI can feel overwhelming, but no one needs to face these challenges alone. Experts and professionals in all areas from around the University have created resources and are facilitating ongoing conversations for instructors to foster a culture of intellectual honesty. 

Teaching Support, a collaborative network connecting instructors to resources across the University, offers specific guidance for instructors navigating the evolving GenAI context with clarity and purpose. These resources provide some basic principles for turning course policy into action:

• Transparency is key: The foundational strategy is for instructors to establish clear expectations by crafting a concise GenAI syllabus statement that explicitly defines allowed and prohibited uses, serving as a starting point for ongoing conversations.
• Focus on process over product: Instructors can break down large assignments and require the submission of "artifacts of learning," such as outlines, drafts, reflections, and in-class writing, which makes the student’s work process more visible and verifiable.
• Design for originality and context: Assignments should be made personal and context-specific by requiring students to use unique project data, local case studies, community-engaged work, or interviews. This demands application and synthesis that GenAI tools struggle to replicate convincingly.
• Teach AI literacy as a skill: UMN faculty focus groups highlighted the growing importance of prompt engineering and planning along with verifying and critiquing AI output as key skills for today’s learners. Instructors can integrate responsible GenAI use by allowing and encouraging it for planning tasks, like outlining an assignment, while maintaining that the final product is solely the student's own work.

This conversation is ongoing and still in early stages of application and discovery. Experts from around the University continue to collaborate to keep instructors informed about ways to foster a community of academic excellence and integrity. 

One such conversation is coming up next week. Register for AI and Academic Integrity: Issues, Opportunities, and Resources, taking place Monday, October 13, 2025 from 11 a.m-noon. This workshop (hosted via Zoom) will address core topics related to academic integrity in the era of GenAI. Facilitators from across the University in student conduct, academic technology, and teaching and learning roles will share approaches to and resources for academic dishonesty, attribution and documentation, as well as discuss AI and its application to student academic integrity.