Vol. 2 No. 1 (2027): Volume 2, Issue 1 (2027)
Articles

Test Anxiety in the Age of AI: Revisiting Mulvenon’s Multi-Stakeholder Framework Under Algorithmic Accountability

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  • Professor Sean W. Mulvenon
Professor Sean W. Mulvenon
University of Nebraska, Las Vegas
DOI: https://doi.org/10.46787/ijaipil.v2i1.7290

Published 2026-05-04

Keywords

  • AI-mediated assessment, test anxiety, algorithmic accountability, learning analytics, continuous evaluation, educational stakeholders

How to Cite

Mulvenon, S. (2026). Test Anxiety in the Age of AI: Revisiting Mulvenon’s Multi-Stakeholder Framework Under Algorithmic Accountability. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 2(1). https://doi.org/10.46787/ijaipil.v2i1.7290

Copyright (c) 2026 Sean W. Mulvenon

Creative Commons License

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

Abstract

This article revisits Mulvenon et al.’s (2005) multi-stakeholder framework of test anxiety in light of AI-mediated assessment systems. It argues that while the original framework remains relevant, the nature of assessment-related pressure has fundamentally shifted from episodic, high-stakes testing to continuous, data-driven evaluation. Drawing on recent research in learning analytics and AI in education, the article examines how adaptive systems, real-time feedback, and predictive analytics reshape experiences of anxiety across students, teachers, parents, and administrators (Gašević et al., 2023; Holmes et al., 2023). It introduces the concept of algorithmic accountability as a new pressure system and proposes an AI-mediated multi-stakeholder anxiety model. The central claim is that AI does not eliminate test anxiety but redistributes and reconfigures it into more diffuse, persistent, and system-driven forms, with implications for instructional design, policy, and future research.

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