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Theory Development in AI in Education

◐ medium · created 2026-08-22 · updated 2026-08-22 · theory, learning-theories, theory-building, conceptual-framework, ai-education, limitations-in-aied-research, research-methods-aied

Theory development in AI in education — the scholarly work of creating, advancing, and critically examining the theories and conceptual frameworks that explain how learners, teachers, and AI systems interact. As generative AI reshapes education, the field is both proposing new theories of learning-with-AI and reworking established learning theories — while a documented weakness in theory use remains a cross-cutting limitation of AIEd research.

Theory development in AIEd sits at the boundary between the applied learning theories the field draws on and the novel constructs the AI era is producing. Where Learning Theories catalogs the established theories applied to AI (behaviorism, constructivism, cognitive load, self-determination, etc.), this concept tracks the process and product of theorizing itself: which new theories and frameworks are being born, how established theories are being advanced, and the methodological question of whether AIEd research theorizes well.

New theories of learning with AI

A growing cluster of articles explicitly creates new theory for the AI era rather than applying existing frames:

  • Generativism. Generativism is proposed as a new learning theory, arguing that behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as generative AI proliferates — a direct bid to name a distinct theoretical paradigm for AI-mediated learning.
  • Epistemic co-agency. Learning with Machines builds "toward a theory of epistemic co-agency," a theory-informed model of how learners and GenAI systems jointly produce knowledge and understanding.
  • The absent cognitive baseline (ACB). The Absent Cognitive Baseline theorizes a structural gap in AI-native students' academic self-assessment — a three-dimension framework explaining why students overestimate their learning when AI inflates performance.
  • The cognitive commons. Cognitive commons and expertise regeneration draws on common-pool-resource theory and distributed cognition to explain how rational AI adoption decisions can deplete the shared expertise pool professions require for renewal.
  • Performance vs. learning. Performance-vs-learning theorizes the sharp divergence between AI-inflated task performance and durable learning, a distinction that recurs across the wiki's Learning Gains evidence.
  • Epistemic AI literacy (EAIL). Epistemic AI Literacy reframes AI literacy as a process-oriented epistemic competence centered on how knowledge is constructed and justified when students co-program with generative AI.
  • Cognitive stewardship. Credential and cognitive stewardship theorizes the institutional responsibility for protecting knowledge and learning in AI-pervasive assessment contexts.
  • Co-regulation and epistemic proactivity. AI as a cognitive partner in co-regulation integrates executive function, Metacognition, distributed cognition, and sociocultural development into a developmental model; epistemic proactivity theorizes students' agentic stance toward AI in mathematics.
  • Human-GAI engagement paradigms. Strydom (2026) addresses the field's "theory deficit" by grounding seven enacted human-GAI engagement paradigms (guarded, possibility-focused, augmented, pioneering, symbiotic, values-based, equity) in Schommer's multidimensional model of personal epistemological beliefs — an epistemological, rather than tool-focused, theory of how individuals differently position themselves relative to AI.

Advancing established theory

Other work extends existing theory into the AI context rather than founding new paradigms: critical-thinking-paradox work integrates cognitive-load theory with load-reduction instruction into a three-level framework; complex-thinking scaffolding extends IBL theory with an AI-scaffolding layer; and equitable assessment re-theorizes Assessment under GenAI disruption. Much of this is conceptual-framework work (OEP/RS, business-school frameworks, valid simulation) that operationalizes theory for practice.

The theory-use problem in AIEd

The field's theorizing is uneven. Limitations in AIEd research documents that AIEd studies frequently use theory weakly or uncritically — a recurring methodological weakness alongside reproducibility and measurement gaps. Reviews find many AIEd papers apply theory superficially or not at all (e.g., literature reviews noting few studies ground interventions in learning theory). This makes theory development — and theory use — a quality concern as much as a scholarly output, connecting to research methods and AI Ed Evaluation.

Relationship to the philosophy of AI in education

Theory development and the philosophy of AI in education are complementary but distinct strands of the wiki's foundational work. Theory development produces and empirically tests the mechanisms of learning-with-AI — named theories and frameworks such as generativism, epistemic co-agency, and the absent cognitive baseline that explain and predict how learners and AI interact. Philosophy interrogates the presuppositions those mechanisms rest on: what counts as knowledge, who counts as a knower, and what the learner fundamentally is. A theory like epistemic co-agency proposes a mechanism while implicitly adopting philosophical commitments about distributed cognition and Agency; philosophy makes those commitments explicit and contestable. Where theory development asks whether a framework explains learning well, philosophy asks whether it captures what learning and mind really are — the two strands meet in the field's most foundational articles, which often do both at once.

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