Designing Assignments That AI Can' t Solve: Strategies for Educators

Alternative Title

Designing Assignments That Artificial Intelligence (AI) Can' t Solve: Strategies for Educators

Contributor

University of Central Florida. Faculty Center for Teaching and Learning; University of Central Florida. Division of Digital Learning; Teaching and Learning with AI Conference (2025 : Orlando, Fla.)

Location

Seminole A

Start Date

29-5-2025 10:45 AM

End Date

29-5-2025 11:10 AM

Publisher

University of Central Florida Libraries

Keywords:

AI-resilient assignments; Academic integrity; Educational strategies; Student engagement; Assignment design

Subjects

Artificial intelligence--Study and teaching; Cheating (Education)--Prevention; Artificial intelligence--Educational applications; Problem solving--Computer-assisted instruction; Lesson planning--Computer-assisted instruction

Description

In this session, participants will explore strategies for designing AI-resilient assignments. These assignments are carefully crafted to significantly reduce, and in some cases entirely prevent, students from using AI tools to earn high grades without genuine effort. By minimizing the potential for AI to complete tasks on behalf of students, these approaches ensure that academic integrity is upheld. Furthermore, they alleviate the need for instructors to act as "AI investigators," removing the burden of determining whether AI was involved in the completion of student work.

Language

eng

Type

Presentation

Rights Statement

All Rights Reserved

Audience

Faculty; Students

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May 29th, 10:45 AM May 29th, 11:10 AM

Designing Assignments That AI Can' t Solve: Strategies for Educators

Seminole A

In this session, participants will explore strategies for designing AI-resilient assignments. These assignments are carefully crafted to significantly reduce, and in some cases entirely prevent, students from using AI tools to earn high grades without genuine effort. By minimizing the potential for AI to complete tasks on behalf of students, these approaches ensure that academic integrity is upheld. Furthermore, they alleviate the need for instructors to act as "AI investigators," removing the burden of determining whether AI was involved in the completion of student work.