Evidence-Based Strategies for Addressing AI-Related Academic Misconduct

Wednesday, October 29, 2025 | 3:15PM–4:00PM CT | EDUCAUSE Commons, Enterprise Central, Poster Area
Session Type: Poster Session
Delivery Format: Poster
This session explains how large language models generate text, identifies key linguistic indicators of AI writing, and presents an evidence-based approach to addressing concerns of academic misconduct which involve AI-generated text. By integrating human expertise with an automated detection tool, educators can develop confidence in their ability to differentiate AI-generated text from student-written text.

Presenters

  • Marilyn Derby

    Associate Director, Office of Student Support & Judicial Affairs, University of California, Davis

  • Bradley Emi

    CTO, Pangram Labs

  • Max Spero

    CEO, Pangram Labs

Resources & Downloads

  • Draft poster

    Updated on 10/22/2025