Teaching Students to Identify Ethical Risks and Blind Spots in Academic AI Use

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Journal of Technology-Integrated Lessons and Teaching Cover. Volume 6, Issue 1, 2026.
Lesson PDF Lesson DOCX Presentation Quick Reference Ethics Checklist Assistance Log Scenario Cards
Published: Jun 19, 2026
DOI: https://doi.org/10.13001/jtilt.v5i1.10297
Keywords:
Artificial Intelligence, Situated Learning, Ethics, Metacognitive Reflection, Personas, Generative AI Tools

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Sara McNeil
University of Houston
https://orcid.org/0000-0002-7295-1660

Abstract

This lesson equips graduate students across disciplines to navigate ethical challenges when using generative AI tools in their research and writing. Through guided discussion, students learn five "AI Personas" (e.g., Research Assistant, Writing Coach, Data Detective, Idea Generator, Academic Concierge) representing common AI use cases. For each persona, students identify specific ethical blind spots and develop mitigation strategies. The technology-rich experience centers on real-time polling, enabling anonymous sharing of concerns and collaborative scenario analysis. Students receive an AI Ethics Checklist and an AI Assistance Log Template. The assessment includes a pre-/post-poll comparison to demonstrate any changes in ethical reasoning.

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How to Cite
McNeil, S. (2026). Teaching Students to Identify Ethical Risks and Blind Spots in Academic AI Use. Journal of Technology-Integrated Lessons and Teaching, 5(1), 64–77. https://doi.org/10.13001/jtilt.v5i1.10297
Issue
Vol. 5 No. 1 (2026): Journal of Technology-Integrated Lessons and Teaching
Section
Article
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biography

Sara McNeil, University of Houston

Sara McNeil, Ed.D., is an Associate Professor in the Learning, Design, and Technology program at the University of Houston College of Education. Her background spans instructional design, educational graphics, and online learning, work that has shaped her current research and teaching on the responsible and ethical integration of generative AI in higher education. She focuses particular attention on how graduate students develop critical judgment about AI tools in research and academic writing. One of her current studies examines AI literacy not as a standalone subject but as an integrative framework, positioning future educational leaders to ask, across every tool and every decision: “What can AI contribute here, and what is yours to decide?”