International Educational Review

Algorithmic Authority Bias in AI-Assisted Learning: An Integrative Cognitive Framework

Sara D Sony 1 * ,
Koksal Mus 2 *
1 School of Mathematics and Statistics, Northwest Missouri State University2 Department of Electrical and Computer Engineering, Worcester polytechnic Institute* Corresponding Author
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International Educational Review, Volume 4, Issue 1, 2026, pp. 75-93
OPEN ACCESS VIEWS: 16 DOWNLOADS: 5 Publication date: 15 Apr 2026
ABSTRACT
The increasing reliance on generative artificial intelligence (AI) in education is reshaping how learners’ access, evaluate, and internalize knowledge. While AI tools enhance accessibility, they also introduce cognitive risks by encouraging learners to bypass critical thinking. This paper introduces Algorithmic Authority Bias (AAB), a conceptual framework that explains how learners assign unwarranted epistemic authority to AI-generated responses on the basis of their fluency, immediacy, and coherence rather than their accuracy or underlying expertise. Building on established theories of authority bias, automation bias, and fluency-based judgment, and integrating recent empirical work on metacognitive laziness, cognitive offloading, and the illusion of explanatory depth in AI-assisted learning, the paper foregrounds the role of cognitive offloading, whereby learners delegate cognitive tasks to AI systems, reducing their active engagement and metacognitive awareness. This over-reliance can produce “synthetic mastery” — a false sense of understanding in which learners accept plausible-sounding answers without engaging in conceptual reasoning. By developing a model that links specific AI features to cognitive mechanisms, the paper articulates a set of testable propositions describing how fluency, perceived authority, and offloading jointly contribute to the acceptance of responses that appear correct yet lack conceptual depth. It then considers implications for instructional design, AI system development, and assessment strategies, and outlines directions for empirical research that build on, rather than duplicate, recent experimental findings. Ultimately, the paper contributes to understanding how AI is reshaping epistemic processes in education and calls for a more critical approach to AI-assisted learning.
KEYWORDS
Algorithmic Authority Bias, Artificial Intelligence, False Understanding, AI-Assisted Learning, Cognitive Bias, Educational Assessment
CITATION (APA)
Sony, S. D., & Mus, K. (2026). Algorithmic Authority Bias in AI-Assisted Learning: An Integrative Cognitive Framework. International Educational Review, 4(1), 75-93. https://doi.org/10.58693/ier.415
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