Theories and Frameworks

Theories and frameworks — the working inventory of explanatory and organizational structures this knowledge base actually uses. Theories explain why learning happens (Learning Theories, Self-Determination Theory, Sociocultural Learning, activity theory); frameworks organize design, teaching, and adoption decisions (Technological Pedagogical Content Knowledge (TPACK), SAMR, technology adoption models, ICAP, universal design for learning); models make learning claims testable and computable (item response theory, Assessment Validity, Knowledge Tracing). Use this page to find the lens a study rests on, or to pick one for your own work.

Questions to Consider

• A theory predicts what happens when something changes; a framework tells you what to attend to. When a study reports a gain under a framework's banner but makes no prediction that could have failed, what has actually been learned?
• TPACK, SAMR, UDL, the adoption models and ICAP all predate large language models. Which parts of them still hold when the tool can write, explain, and adapt on its own, and how would you tell?
• Several of the theories catalogued below were coined for AI within the last few years. What would it take for one of them to be wrong, and has anyone tried to show that?
• Adoption is often reported as movement through stages. If a colleague reports that their course moved "from substitution to redefinition" this year, what independent evidence would you ask for before believing it?
• Much of this field applies borrowed theory rather than testing it. Pick a finding you rely on: could it be reframed as a test of a theory, and what measurement would that require?

Introduction

Every AI tutor, feedback system, and dashboard embeds assumptions about how people learn, whether or not the designers state them. This page collects the theories, frameworks, and models those assumptions come from, ordered loosely by how much of the current research literature leans on them, and says briefly what each one is good for. The distinctions among the three labels are real but minor, and they are summarized near the end rather than treated as the subject. What matters practically is which lens a claim rests on, and whether that lens can support the claim.

Learning theories the literature applies

These explain mechanisms: they name the parts that do the work and predict what follows when those parts change. In the AI literature they are mostly borrowed from psychology and the learning sciences, and used as interpretive frames around a new tool.

• Learning theories (umbrella) — the family as a whole, including the classical poles of Behaviorism and constructivism and the "constructivism in name, behaviorism in practice" gap that recurs in AI implementations. Connectivism and cognitive load theory are covered here too.
• Constructivism — learning as active knowledge construction. It underwrites project-based work, dialogue-based AI tutoring, and the critique that drill-and-feedback systems violate their own stated pedagogy.
• Sociocultural theory — learning as mediated by tools and social interaction, which is why AI gets theorized as a mediating artifact rather than a mere aid.
• Activity theory — takes the whole activity system, contradictions included, as the unit of analysis. Common in teacher-facing AI studies, where the interesting finding is usually the tension among tools, rules, and community.
• Situated learning and Embodied learning — learning as tied to context and to the body, the counterweight to treating AI as a purely linguistic medium.
• Distributed cognition — thinking spread across people and artifacts, which frames AI assistance as a redistribution of cognitive labor rather than a substitution for it.
• Community of inquiry — the social, cognitive, and teaching presences that make an online experience work; a theory of the experience and a design frame at once.
• Self-determination theory — autonomy, competence, and relatedness as motivational preconditions. Often the explanation offered when AI use raises engagement without raising learning.
• Cognitivism — information processing, working memory, and the divergence between the feeling of learning and actual learning.
• Social perception theory (warmth and competence) — imported from social cognition and service-robot research, it frames how users judge an AI agent's credibility: perceived competence predicted ChatGPT's feedback credibility directly while warmth worked only through human-like empathy (SEM, 230 students; Pérez-López et al. (2026)).
• A fourth affordance category was proposed, not tested. Coding 24 studies against a three-part affordance scheme, the reviewers found engagement, safety and self-efficacy did not fit the technological, pedagogical or social categories, and proposed emotional affordances grounded in Bandura's reciprocal determinism (Lopez & Arabadzhy (2026)).

Models of the learner and of the learning process

• Self-regulated learning — planning, monitoring, and evaluating one's own learning. The workhorse construct for asking whether AI support reaches the phases that matter or only execution.
• Metacognition — thinking about one's own thinking, and the construct most often named when AI is accused of doing the thinking for students.
• Motivation — why learners engage, and the construct that separates engagement from learning in AI studies.
• Self-efficacy — belief in one's capability, distinct from competence, and a frequent mediator between AI use and outcomes.
• Agency — who directs the work: the construct behind the field's control-versus-autonomy tension, and behind the delegated-agency mechanism in the newest theory.
• Desirable difficulties, retrieval, spacing, and interleaving, and refutation text — findings that make friction pedagogically valuable, applied to decisions about how much an AI should do.
• Transfer of learning — whether capability persists once support is withdrawn, which recent theory treats as the criterion separating learning from assistance rather than one outcome among several.

Teaching, integration, and adoption frameworks

• TPACK — the teacher knowledge that blends technology, pedagogy, and content; the default framework for teacher-facing AI studies.
• Adoption models — intention as a function of perceived usefulness and ease of use. Used widely enough that the field's reliance on intention rather than behavior is a standing critique.
• ICAP — ranks engagement as passive, active, constructive, interactive; useful for asking what mode an AI interaction actually affords.
• SAMR — substitution through redefinition. Popular as a maturity story, weak as a measurement instrument.
• Universal design for learning, inclusive learning, and Accessibility — design frames for who gets served, and the ones most often invoked in disability and equity work.
• Learning design, Curriculum design, and Design thinking — how tasks, sequences, and programs are structured before any tool is chosen.
• Change management — whether a system is taken up at all, which is usually an institutional question rather than a pedagogical one.

Measurement and computational models

• Item response theory — item and ability estimation, and the model that makes a test's numbers interpretable.
• Educational measurement — the instrument landscape, including the measures built specifically for AI literacy.
• Validity — whether an instrument measures the construct it claims, which decides whether a reported outcome supports the theoretical claim attached to it.
• Self-report measures — the dominant and most fragile source of evidence in this literature; several constructs above are known to diverge sharply between self-report and performance.
• Knowledge tracing and Student modeling — estimating a learner's state over time from interaction data.
• Cognitive diagnosis — attributing performance to specific skill components, the modeling counterpart to diagnostic teaching.
• Benchmarks, AI education evaluation, and psychometric awareness in AI design — how systems and interventions are scored, compared, and audited.
• Design-based research — the method that generates theory from designed interventions rather than testing it in advance.

Theories coined for the AI era

Four proposals in the knowledge base build theory rather than borrow it. They are article pages rather than concept nodes, because each one is a single argument:

• Generativism — a new learning theory arguing that the classical four show significant conceptual limitations as generative AI proliferates.
• Epistemic co-agency — a model of how learners and AI systems jointly produce knowledge and understanding.
• The absent cognitive baseline — theorizes why AI-native students overestimate their own learning when AI inflates performance.
• Agentivism — a mid-range theory naming four mechanisms (delegated agency, epistemic monitoring and verification, reconstructive internalization, and transfer under reduced support) and stating six testable propositions.

Telling the three labels apart

The words are used loosely in the literature, and the difference is worth one paragraph rather than a whole page:

• A theory explains a mechanism and predicts what happens when its parts change. It can fail, which is why studies that test one can report effect sizes.
• A framework organizes or prescribes. It names the components to attend to, usually without predicting magnitudes, and it cannot be falsified.
• A model is a formal representation, most often of measurement or of the learner's state. It can be fitted, compared, and shown to be wrong in a way a framework cannot.
• The labels overlap. Community of inquiry is a theory and a design frame at once; activity theory is explanatory and analytic. Treat the label as a clue about what a source claims, not as a filing category.

Related layers

Two neighbouring pages are deliberately about something else. Philosophy of AI in education asks normative and conceptual questions — what education is for, whether a machine can teach — and does not predict effect sizes; a study can be philosophically naive and theoretically sound, or the reverse. Theory development is the meta-activity of building, borrowing, and revising theory, including the standing critique that much AIED work applies existing theory rather than testing it. This page is the inventory; theory development is about producing and revising the inventory.

Choosing a lens when the question is practical

• Instructors starting from a problem can work backwards: if learners are not engaging, ICAP and SDT name different causes and suggest different fixes; if the question is whether to adopt a tool at all, adoption models and SAMR ask different things about it.
• Learning designers get the most from the design frames — UDL, Learning Design, and Scaffolding — paired with a theory that predicts what happens when the scaffolding is removed.
• Researchers should state which node a study claims and whether the design can actually test it; the measurement models (IRT, Assessment Validity, Self-Report Measures) decide whether a reported outcome supports that claim.
• Administrators meet these frameworks as adoption and change questions, where a stage model is often used as a maturity story rather than an instrument.

What frameworks cannot do

Frameworks are not evidence. They are usually borrowed from pre-LLM contexts and localized by whoever applies them, they can function as branding, and stage models invite checkbox adoption that reports movement through levels rather than learning. Claims resting on a framework should be read alongside the field's cross-cutting limitations, the validity of whatever measured the outcome, and the known limits of self-report.

Mechanism-to-theory alignment can nonetheless be decisive: a thematic synthesis of 55 AI-supported game-based learning studies found effectiveness turned on whether the AI mechanism enacted the pedagogy it was meant to serve rather than on algorithmic sophistication (Kaşarcı and Yurt (2026)).

Common Misconceptions

"Applying a borrowed theory to an AI tool is using theory." Much of this field applies borrowed theory rather than testing it, and the difference is whether the framework made a prediction that could have failed. When a study reports a gain under a framework's banner while committing to no falsifiable prediction, nothing has been learned about the framework — an outcome consistent with every theory in the catalog is evidence for none of them.

"A framework's value is how well it describes a system you have already built." That is post-hoc labeling. A taxonomy that accommodates any design has no discriminating power, which is why descriptive frameworks should be judged on what they exclude. The evaluation question is not "does my system fit this?" but "what kind of system would this framework say is wrong?"

"Learning theories, models of the learner, and adoption frameworks are interchangeable lenses." They answer different questions — how learning happens, what the learner's state is, and why a practice spreads — and their evidence types differ accordingly. Choosing the wrong layer produces a study that measures the right variable against the wrong prediction, which reads as a null result when it is a category error.

"More frameworks means a more mature field." Coined-for-the-AI-era frameworks accumulate faster than they are tested, and accumulation without falsification is a symptom rather than progress. Maturity shows up as competing predictions being resolved, not as vocabulary growth.

Connected Concepts

• Theory Development in AI in Education — building and revising theory in the field
• Philosophy of AI in Education — the normative and conceptual layer
• Learning Theories — the umbrella for learning theory
• Learning Sciences — the neighboring field most theory is borrowed from
• Limitations in AIEd Research — what the evidence base can and cannot support
• Technological Pedagogical Content Knowledge (TPACK) — teacher knowledge framework
• Technology Adoption Models — adoption frameworks
• Item Response Theory — a measurement model
• Universal Design for Learning — a design framework
• Research Methods in AIED — how theory gets tested

Connected Articles

• GenAI chatbots in language learning: A systematic review of emerging affordances — Emotional affordances proposed as a fourth category in an affordance taxonomy

• Effectiveness of AI-Supported Game-Based Learning: A Systematic Review of Outcomes, Challenges, and Future Directions — Mechanism-to-theory alignment, not algorithmic sophistication, drove effectiveness in 55 AI game-based learning studies

• The evolution of research on AI and education across four decades: Insights from the AIxEd framework — four decades of AIED through the AI×Ed framework

• Educating minds with generative AI — theory-heavy synthesis of generative AI and learning

• An activity-theoretical approach to teacher professional development in pedagogical AI agent design — activity theory applied to teacher professional development

• Activity theory as a lens on teachers' adoption of AI technologies: A structural equation modeling — activity theory and teacher adoption

• Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence — a learning theory proposed for the generative AI era

• Learning with machines: Toward a theory of epistemic co-agency — toward a theory of epistemic co-agency

• The Absent Cognitive Baseline: Theorizing a Structural Gap in AI-Native College Students' Academic Self-Assessment — theorizing a structural gap in AI-native students' self-assessment

• Agentivism: a learning theory for the age of artificial intelligence — a mid-range learning theory for human-AI interaction

• Measuring Artificial Intelligence Literacy: A Systematic Review of Instrument Development, Conceptual Foundations, and Psychometric Quality — instrument development across a young construct

• Control vs. Agency: Exploring the History of AI in Education — the field's foundational control-versus-agency tension