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Education·P Space·Evidence-backed problem·Published 2026-07-13

Manipulation and Deception in Generative AI-Mediated Education: Preserving Epistemic Agency, Critical Thinking, and Creativity

Abstract Generative AI now mediates core parts of learning, yet we lack criteria to tell its legitimate pedagogical uses from manipulative and deceptive ones. We also know too little about how AI reshapes the growth of critical thinking and creativity, or about whether it accelerates drift from educational goods to evaluative metrics. Using a postdigital, pragmatist lens that treats classrooms as sociomaterial assemblages of people, platforms, and institutions, we propose three orienting principles: moral legiti…

TRV-2026-0190Peer-reviewedPermanent record — cite & verify
Manipulation and Deception in Generative AI-Mediated Education: Preserving Epistemic Agency, Critical Thinking, and Creativity

"Classroom students collage element, drawing" is marked with CC0 1.0. To view the terms, visit https://creativecommons.org/publicdomain/zero/1.0/.

The quick read

As of May 2026, generative AI mediates core parts of learning, prompting a peer-reviewed conceptual paper to propose criteria for distinguishing legitimate pedagogical uses from manipulative and deceptive ones. It introduces three principles — moral legitimacy, developmental integrity, and value preservation — to assess influence and protect reflective judgement.

The distinction matters because without such criteria, AI tools may substitute for imaginative work and erode intellectual virtues while pushing education toward evaluative metrics. The paper does not report measured classroom outcomes by its publication date; it offers expected outputs like a typology of nudging, persuasion, manipulation, and deception and design constraints, leaving empirical validation and threshold testing as unresolved work.

Main points
  • Paper treats classrooms as sociomaterial assemblages of people, platforms, and institutions to analyze AI mediation.
  • Proposes three orienting principles: moral legitimacy, developmental integrity, and value preservation.
  • Expected outputs include a typology of influence covering nudging, persuasion, manipulation, and deception and a legitimacy audit.
Problem

Generative AI now mediating core parts of learning risks crossing into manipulation and deception and accelerating drift from educational goods to metrics, reshaping development of critical thinking and creativity.

The rundown

The authors frame classrooms as sociomaterial assemblages and propose adjudicating hard cases across autonomy-, virtue-, and outcome-based views to define thresholds for legitimate influence.

They outline translation into design and policy via a legitimacy audit and constraints like contestability, reason-giving, and calibrated confidence intended to embrace edtech with humanistic aims.

Sources

Reader signal

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The debate