Possibilities and Limitations of Generative AI in the Classroom: UTA Guidelines for Using Generative AI in Instruction to Achieve Student Learning Outcomes (SLOs)
Welcome to the UTA guidelines for the considered use of generative AI (Gen AI) in instruction. This resource is crafted to foster informed decisions about leveraging GenAI in teaching and learning within UTA’s diverse academic landscape. Our aim is to illuminate the spectrum of possibilities—from cautious restraint to enthusiastic adoption—always with a clear eye on how these technologies can serve or, at times, detract from achieving the specific Student Learning Outcomes (SLOs) in your courses.
By embracing a balanced perspective, this document endeavors to support instructors in making judicious choices about when and how to integrate GenAI into their pedagogy, as well as when to exclude it, in favor of methods that better align with their educational objectives. Whether considering a nuanced incorporation of GenAI tools or contemplating a comprehensive application, the guidelines within will help faculty navigate these decisions in alignment with UTA’s standards of academic integrity.
We encourage faculty to consult the Table of Contents to find discussions and recommendations most pertinent to their discipline and teaching goals. It is our hope that this guide will serve as a dynamic resource in their instructional toolkit, enabling them to tailor use of GenAI in ways that are most conducive to fulfilling the SLOs of each course taught at UTA.
This Generative AI Guidelines for Instruction document was developed by a sub-committee put in place by our Chief Information Officer at UTA, and as requested by the UTA leadership. The sub-committee members and authorship of this document consists of the following UTA faculty and staff:
Ann Cavallo, CRTLE/COED – Chair
Andrew Clark, COLA
Karen Magruder, SSW
Tim Ponce, COLA
Corey Forbes, COED
Morteza Khaledi, COS
Michael Schmid, University Analytics
This document was also reviewed and vetted by the full Generative AI committee, which has wide representation of faculty, staff, and administrators from across the UTA campus.
My deepest appreciation is extended to all who contributed to this work.
Ann Cavallo, Ph.D.
Assistant Vice Provost and Director
Center for Research on Teaching and Learning Excellence
Distinguished University Professor of Science Education
The University of Texas at Arlington
cavallo@uta.edu