Publication Date
Spring 2026
Presentation Length
Poster/Gallery presentation
College
College of Education
Department
Education, Department of
Student Level
Undergraduate
Faculty Mentor
Amanda Nelms
Metadata/Fulltext
Fulltext
Presentation Type
Poster
Summary
The rapid integration of generative artificial intelligence (GenAI) tools into academic settings has created significant challenges for maintaining academic integrity in higher education. Rather than relying on increasingly unreliable AI detection software or blanket prohibition policies, this project proposes a design-based approach to assignment construction that embeds transparency, critical engagement, and documented AI use into the learning process itself. Drawing on existing literature related to academic integrity, AI literacy, and instructional design, this study examines how faculty can redesign traditional assignments (including essays, research papers, reflective journals, and discussion posts) to require students to interact with AI critically and accountably. The proposed framework aligns with the International Society for Technology in Education (ISTE) Educator Standards, specifically the roles of Educator as Designer, Facilitator, and Digital Citizen. Findings from a review of institutional AI policies and scholarly literature suggest that proactive, design-forward approaches are more effective than reactive enforcement in preserving both academic rigor and student trust. This project offers a practical, transferable model for higher education instructors seeking to integrate GenAI ethically and strategically into their courses.
Recommended Citation
Phares, George R., "Designing AI-Integrated Assignments That Reduce Academic Dishonesty" (2026). SPARK Symposium Presentations. 1087.
https://repository.belmont.edu/spark_presentations/1087
Included in
Adult and Continuing Education Commons, Curriculum and Social Inquiry Commons, Educational Technology Commons
