Quantum Dialogism: A Quantum-Like Modelling Proposal for AI-Mediated Dialogic Learning

Dialogic pedagogy holds that learning is the interanimation of multiple voices and that meaning lives in the gap between voices, not inside any single one. Learner-modelling formalisms in Computer-Supported Collaborative Learning (CSCL), by contrast, still predominantly represent each learner as a scalar mastery variable per skill. This paper argues that this mismatch is not incidental: classical Kolmogorov probability cannot express a learner who coherently holds two incompatible interpretations of a concept, nor can it express the productive tension between voices that dialogic theory treats as constitutive of understanding. Quantum-inspired cognitive modelling—the use of Hilbert-space formalism including superposition, interference, non-commutativity, and entanglement on classical hardware—provides, we contend, the first learner-modelling substrate whose native structure matches the pedagogical commitments of dialogism. Concurrently, the rapid advancement of generative artificial intelligence (AI), particularly large language models (LLMs), has created unprecedented opportunities and challenges for enhancing online learning experiences. This paper proposes a novel conceptual bridge: applying the analogies and mathematics of quantum cognition to Bakhtinian dialogism. Moving beyond classical, transmission-based models of education, this paper conceptualises the LLM as a dialogic actor existing in a state of 'superposition', embodying multiple pedagogical voices and perspectives—polyphony—simultaneously. Through the lens of the uncertainty principle, the paper explores how productive ambiguity in AI-mediated scaffolding fosters deeper cognitive engagement, as measured by the Structure of Observed Learning Outcome (SOLO) taxonomy. The paper addresses the critical issue of machine consciousness, drawing on neuroscientific theories of consciousness—including Global Workspace Theory and Higher-Order Theories—and on the Orchestrated Objective Reduction (Orch-OR) theory of quantum consciousness, to delineate the 'dialogic gap' between human learners and non-conscious AI. Crucially, this expanded framework integrates an analysis of the ecosystem of cognitive bias in educational AI prompting, demonstrating how human biases—including confirmation bias, expediency bias, and automation bias—interact with AI outputs to either enrich or undermine the dialogic space. Drawing on empirical evidence from AI-mediated online learning environments, this paper articulates four conceptual bridges between the traditions, proposes a four-layer framework (Superposed Voices) for dialogic learner modelling, states five falsifiable design principles, and outlines a comprehensive research agenda. The contribution is a theoretical realignment for CSCL: a formal vocabulary that lets dialogic learner models say what they have always meant, shifting the focus from imitating human conversation to authentic participation in dialogic spaces.

dialogic pedagogy, learner modelling, quantum cognition, quantum-like AI, generative AI, large language models, open learner models, interthinking, Bakhtin,

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