Productive Entanglements: A Quantum-Agential Framework for Cognition, Agency, and Ethics in AI-Mediated Education

This paper introduces the Quantum-Agential framework to address the “foundational gap” in AI-mediated education, where classical dualisms fail to capture the entangled realities of human–machine learning. Drawing on Karen Barad’s Agential Realism and quantum-like models of cognition, the framework reconceptualizes cognition, agency, and ethics as emergent properties of material-discursive apparatuses rather than attributes of isolated individuals or tools. By dissolving binaries such as human/machine and subject/object, it provides a relational ontology and a formal language for modeling uncertainty, contextuality, and entanglement in learning processes. Case studies of co-writing with generative AI and adaptive tutoring systems illustrate how this approach reframes cognitive load, distributed agency, and systemic ethics. The framework advances post-humanist pedagogy, emphasizes educator roles as architects of learning apparatuses, and proposes diffractive methodologies for research. It offers a principled path toward ethically responsible, cognitively aware, and human-centered futures for AI in education.

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Publication Details

Journal
Artificial Intelligence Education Studies
Published
2025-09-10
DOI
https://doi.org/10.6914/aiese.010306
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
2.37
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article

Productive Entanglements: A Quantum-Agential Framework for Cognition, Agency, and Ethics in AI-Mediated Education

Gang Deng
1 citations
Artificial Intelligence Education Studies
Ethics and Social Impacts of AI
2.37
article

Productive Entanglements: A Quantum-Agential Framework for Cognition, Agency, and Ethics in AI-Mediated Education

Gang Deng
article en
1 citations

Abstract

This paper introduces the Quantum-Agential framework to address the “foundational gap” in AI-mediated education, where classical dualisms fail to capture the entangled realities of human–machine learning. Drawing on Karen Barad’s Agential Realism and quantum-like models of cognition, the framework reconceptualizes cognition, agency, and ethics as emergent properties of material-discursive apparatuses rather than attributes of isolated individuals or tools. By dissolving binaries such as human/machine and subject/object, it provides a relational ontology and a formal language for modeling uncertainty, contextuality, and entanglement in learning processes. Case studies of co-writing with generative AI and adaptive tutoring systems illustrate how this approach reframes cognitive load, distributed agency, and systemic ethics. The framework advances post-humanist pedagogy, emphasizes educator roles as architects of learning apparatuses, and proposes diffractive methodologies for research. It offers a principled path toward ethically responsible, cognitively aware, and human-centered futures for AI in education.

Artificial Intelligence Education StudiesVol. 1(3)
Qilu University of Technology (CN)
Openalex Percentile: Top 9%
Ethics and Social Impacts of AI
2.37
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