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

AI-First Critique Learning (AFCL): A Framework for Restoring Assessment Integrity in the Age of Generative AI

PDF
  • Nabeel Alzahrani
Nabeel Alzahrani
CSUSB

Published 2026-02-21

Keywords

  • AI-integrated assessment,
  • critical thinking,
  • metacognition,
  • authentic assessment,
  • distributed cognition,
  • higher education
    • ...More
    Less

How to Cite

Alzahrani, N. (2026). AI-First Critique Learning (AFCL): A Framework for Restoring Assessment Integrity in the Age of Generative AI. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 2026(1). Retrieved from https://journals.calstate.edu/ijaipil/article/view/6945

Copyright (c) 2026 Nabeel Alzahrani

Creative Commons License

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

Abstract

The pervasive adoption of generative artificial intelligence by students has precipitated a widespread challenge to traditional assessment validity in higher education. This article introduces the AI-First Critique Learning (AFCL) framework as a conceptual framework with practical implementation guidance and a proposed research agenda. AFCL is a pedagogical innovation that transforms AI from an assessment threat into a catalyst for developing critical thinking, metacognition, and ethical judgment. Grounded in distributed cognition and supported by meta-analytic evidence showing AI's positive impact on higher-order thinking, AFCL operates through three interconnected elements: Classroom-Locked Prompts that ensure contextual specificity, Thinking Lenses that scaffold analytical rigor, and Standardized AI Interaction Environments that generate verifiable reasoning traces. By shifting assessment from products to documented critique processes, AFCL aims to restore evaluative validity while cultivating "critique literacy" as an essential digital-age competency. The framework includes a rapid four-month implementation pathway, enabling institutions to respond effectively to contemporary assessment challenges while preparing learners for AI-augmented professional futures. This work contributes a theoretically grounded framework and actionable guidance to the domains of AI pedagogy and assessment innovation, concluding with a defined agenda for necessary empirical research.

PDF

References

  1. Al Mashagbeh, M., Alsharqawi, M., Tudevdagva, U., & Khasawneh, H. J. (2025). Student engagement with artificial intelligence tools in academia: A survey of Jordanian universities. Frontiers in Education, 10, 1550147. https://doi.org/10.3389/feduc.2025.1550147
  2. Csikszentmihalyi, M. (2014). Flow and the foundations of positive psychology: The collected works of Mihaly Csikszentmihalyi. Springer. https://doi.org/10.1007/978-94-017-9088-8
  3. Dawson, P. (2020). Defending assessment security in a digital world: Preventing e-cheating and supporting academic integrity in higher education. Routledge. https://doi.org/10.4324/9780429324178
  4. Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press.
  5. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906
  6. Greene, M. (1995). Releasing the imagination: Essays on education, the arts, and social change. Jossey-Bass.
  7. Hennessey, B. A. (2019). Motivation and creativity. In J. C. Kaufman & R. J. Sternberg (Eds.), The Cambridge handbook of creativity (2nd ed., pp. 374–395). Cambridge University Press. https://doi.org/10.1017/9781316979839.020
  8. Lee, J., Hung, J.-T., Soylu, M. Y., Popescu, D., Cui, C. Z., Grigoryan, G., Joyner, D. A., & Harmon, S. W. (2025). Socratic Mind: Impact of a novel GenAI-powered assessment tool on student learning and higher-order thinking. Computers & Education, 218, 105100. https://doi.org/10.48550/arXiv.2509.16262
  9. Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779
  10. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
  11. Palmer, P. J., Zajonc, A., & Scribner, M. (2010). The heart of higher education: A call to renewal. Jossey-Bass.
  12. Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 47–87). Cambridge University Press.
  13. Pearson, H. (2025). Universities are embracing AI: Will students get smarter or stop thinking? Nature, 646(8086), 788–791. https://doi.org/10.1038/d41586-025-03340-w
  14. Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books.
  15. Selwyn, N. (2024). On the limits of artificial intelligence (AI) in education. Nordisk Tidsskrift for Pedagogikk og Kritikk, 10(1), 3–14. https://doi.org/10.23865/ntpk.v10.6062
  16. Sibanda, N., & Busby, S. (2024). Standing on the shoulders of Giants (Part 1). Research in Drama Education: The Journal of Applied Theatre and Performance, 29(4), 511–517. https://doi.org/10.1080/13569783.2024.2425122
  17. Sidra, S., & Mason, C. (2025). Generative AI in human–AI collaboration: Validation of the collaborative AI literacy and collaborative AI metacognition scales for effective use. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2025.2543997
  18. Stewart, O. G., & Rodgers, D. J. (2025). A critical AI media literacy framework: Understanding layered bias and empowerment in artificial intelligence. Learning, Media and Technology, 1–13. https://doi.org/10.1080/17439884.2025.2527179
  19. Veletsianos, G. (2020). Learning online: The student experience. Johns Hopkins University Press. https://doi.org/10.1353/book.73824
  20. Wiggins, G. (1990). The case for authentic assessment. Practical Assessment, Research, and Evaluation, 2(1), Article 2. https://doi.org/10.7275/ffb1-mm19
  21. Wolf, M. (2018). Reader, come home: The reading brain in a digital world. Harper.
  22. World Economic Forum. (2025, January). The future of jobs report 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/
  23. Zhang, Y., Tian, H., & Lu, J. (2025). The relationship between higher order thinking and problem solving skills development among pre-service teachers using generative AI: An analysis based on moderated mediation. BMC Psychology, 13(1), 1094. https://doi.org/10.1186/s40359-025-03404-6
  24. Zhao, Y., Yue, Y., Sun, Z., Jiang, Q., & Li, G. (2025). Does generative artificial intelligence improve students’ higher-order thinking? A meta-analysis based on 29 experiments and quasi-experiments. Journal of Intelligence, 13(12), 160. https://doi.org/10.3390/jintelligence13120160