Vol. 2026 No. 1 (2026): 2026 Continuous Issue
Articles

Critical Trust in Generative AI: Suspending Disbelief and Developing Critical Friend Groups Among University Students

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  • Timothy Mattison,
  • Elizabeth Wilkins,
  • Julie Conrad
Timothy Mattison
University of Southern Indiana
Elizabeth Wilkins
University of Southern Indiana
Julie Conrad
University of Southern Indiana

Published 2026-03-10

Keywords

  • generative artificial intelligence,
  • AI,
  • GenAI,
  • trust,
  • critical hope,
  • critical friend groups
    • ...More
    Less

How to Cite

Mattison, T., Wilkins, E., & Conrad, J. (2026). Critical Trust in Generative AI: Suspending Disbelief and Developing Critical Friend Groups Among University Students. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 2026(1). Retrieved from https://journals.calstate.edu/ijaipil/article/view/6967

Copyright (c) 2026 Timothy Mattison, Elizabeth Wilkins, Julie Conrad

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

In a 2025 international study involving 11,706 undergraduate students, 29% overall (67% in the U.S.) reported regular use of generative artificial intelligence (GenAI) programs. This could be problematic, since GenAI outputs often contain randomly parroted falsehoods, and students could overuse the technology. This manuscript proposes a new framework, critical trust, to help university students balance trust and mistrust in the validity of information in GenAI outputs. A critical trust framework could also help students make ethical decisions about how and when to use GenAI applications in their academic work. University students can employ critical trust in critical friend groups, which could be especially beneficial for students from historically marginalized communities without college-educated mentors, helping them avoid accusations of misuse of GenAI and the loss of credibility that can result from plagiarism. The critical trust framework could also provide education researchers with a pathway to develop and test a scale that measures critical trust, ultimately determining the optimal levels to minimize harms from GenAI hallucinations and inappropriate overuse.

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