Pedagogies and Teaching Strategies

Pedagogies and teaching strategies — the methods and approaches educators use to teach and facilitate learning, and the umbrella concept for the wiki's coverage of how teaching happens (in contrast to Learning Theories, which explains how learning happens). In AI in education, pedagogy is central because the choice of teaching strategy shapes how AI tools are deployed: the same generative-AI tool can be a scaffold under one pedagogy, a Socratic interlocutor under another, or an answer-generator under a third. The wiki documents individual pedagogies and treats them as the instructional lens through which AI's design and classroom use are evaluated.

Pedagogy and teaching strategy concern how educators teach — the activities, structures, and methods that organize learning — while Learning Theories explains the underlying mechanisms of how learning happens. The two are complementary: a pedagogy operationalizes one or more theories, and the wiki treats pedagogy as the bridge from theory to classroom practice. Every AI tool embeds pedagogical assumptions about the desired instructional interaction, whether the designer states them or not.

The pedagogy landscape

The wiki documents a rich set of individual teaching strategies and pedagogies, organized into families:

Student-centered and active approaches. Active Learning (students engaged in doing and thinking rather than passively receiving), Project Based Learning (learning through extended projects), Experiential Learning (learning through direct experience), and Learning By Teaching (learning by explaining to others).
Collaborative and social approaches. Collaborative Learning (learning through group work), Sociocultural Learning (learning through social participation and mediation), and Socratic questioning (learning through guided dialogue and questioning).
Experience-based approaches. Experiential Learning (learning through direct experience and reflection), Situated Learning (learning in authentic contexts), and Embodied Learning (learning through physical/embodied interaction).
Structured and guided approaches. Scaffolding (temporary, fading support), Instructional Design (systematic design of instruction), Self Regulated Learning (learners directing their own learning), and Sociocultural Learning (including structured, teacher-guided sociocultural support).
Online and distance pedagogies. Online teaching and learning is itself a pedagogical context, not just a delivery channel: the medium shapes which strategies are viable (Active Learning rethought for asynchronous forums, Collaborative Learning via digital discussion, tutoring agents replacing face-to-face interaction). In this medium, AI raises both new opportunities (scalable personalization, always-on support) and new risks (academic integrity, cognitive offloading), making pedagogical intent decisive.
Motivation and engagement approaches. Game Based Learning (learning through games), Self Determination Theory (supporting autonomy, competence, relatedness), and Motivation-oriented strategies.
equity-conscious pedagogies. Culturally relevant pedagogy, Universal Design for Learning, Critical Pedagogy, and Accessible Learning ensure strategies serve diverse learners.

How pedagogy appears in AI in education

The wiki's research examines pedagogy at the intersection of AI and teaching in several ways:

AI as a pedagogical agent. AI tools embody pedagogies — a tutor built on Socratic questioning prompts learners to reason, while an answer-generating chatbot may default to direct provision (see Reducing AI Misuse on why the pedagogical stance matters). The agentic AI literature shows that grounding agents in instructional-design theory outperforms raw prompting.
Pedagogy determines AI's effect. A recurring finding is that how AI is used matters as much as whether it is used. Instructional-guidance research and guardrailed-tutor RCTs show the same AI can harm or help depending on the pedagogical wrapper (hints vs. answers, structured vs. open use).
Teaching strategies for AI literacy. Teaching students to use AI well is itself a pedagogical task — AI Literacy and Reducing AI Misuse research develops strategies (think-first/AI-second/reflect, AI-declaration, calibration training) that belong to this umbrella.
Pedagogy in teacher practice. Teacher Role and Teacher AI Competency examine how teachers adopt AI within their existing pedagogical repertoire, and Pedagogical LLM Training / Pedagogical Agent study AI tools trained to follow pedagogical principles.

Relationship to learning theories

Pedagogies and learning theories are closely linked: each pedagogy operationalizes one or more theories. For example, Project Based Learning operationalizes Constructivist and experiential theories; Socratic Method draws on Sociocultural Learning and Metacognition; Scaffolding stems from the Zone of Proximal Development. The wiki treats Learning Theories as the conceptual foundation and this page as the instructional-practice umbrella — see also Instructional Design, which concerns the systematic process of selecting and sequencing strategies.

Learning gains across pedagogical strategies

Different pedagogical strategies produce different kinds and sizes of learning gains, and the wiki's evidence lets us compare them:

Active and experiential strategies generally produce stronger durable learning than passive reception, though they feel more effortful — Active Learning, Experiential Learning, Project Based Learning, and Learning By Teaching build understanding through doing. Research shows that strategies preserving effortful practice (rather than AI shortcutting it) protect Learning Gains.
Structured, guided strategies (Scaffolding, Self Regulated Learning, Instructional Design) produce reliable but more modest gains — the guardrail evidence (PNAS 2025) shows hint-not-answer scaffolding preserves learning that unguarded answer-giving destroys.
Game-based learning produces engagement and skill gains that are real but often modest and context-dependent — meta-analytic evidence finds game-assisted GenAI shows no significant added benefit over other formats, so games are best used for motivation and practice, not as a shortcut to gains.
Collaborative and sociocultural strategies (Collaborative Learning, Sociocultural Learning) show gains mediated by interaction quality, increasingly studied with AI as a partner or peer.
Socratic and dialogue-based strategies (Socratic Method) target higher-order thinking and reasoning — gains that are harder to measure than skill gains but central to Critical Thinking.

The key cross-cutting finding, consistent with the wiki's Learning Gains research, is that the strategy's effect on learning depends more on how it preserves learner effort and productive struggle than on which label it carries — any pedagogy, even a "good" one, fails if AI is configured to bypass the cognitive work it was meant to elicit (see Cognitive Offloading, Desirable Difficulties).

Implications for AI in education

Select pedagogy deliberately with AI: the teaching strategy determines whether an AI tool supports or undermines learning, so pedagogical intent should drive AI tool selection and configuration.
Keep learner agency central: active, Socratic, and scaffolding pedagogies preserve the productive struggle and Agency that AI can otherwise erode (see Cognitive Offloading, Desirable Difficulties).
Design AI to enact good pedagogy: AI agents and tutors should be grounded in established instructional frameworks, not default answer-generation.
Teach with and about AI: pedagogies should both use AI to teach and teach learners how to use AI responsibly.

Connected Concepts

Connected Articles

AI Communities Of Inquiry 2026
AI Distance Education Systematic Review 2026
Instructional Guidance GenAI Learning — How instructional guidance shapes GenAI learning effects

Generative AI Guardrails Harm Learning — Guardrailed (hint-not-answer) tutoring eliminates the exam penalty
Agentic AI Pedagogical Best Practice 2026 — The automation-vs-learning tension in agentic AI
Jeon Isd Agent Bench 2026 — Grounding agents in instructional-design theory
Pedagogical LLM Training — AI tools trained to follow pedagogical principles
AI TPACK Teacher Multi Agent Workflow — Teacher TPACK and multi-agent workflows
Edurev 100741 TPACK GenAI Review — Systematic review of GenAI in student learning from a TPACK perspective
AI Learning Tools Engineering Education Needs — AI learning tools in engineering education
Fowlin Operationalizing Learning Principles AI — Operationalizing learning principles with AI
Learnlm Improving Gemini Learning — LearnLM: pedagogical instruction following
AI Video Dual Gatekeeping 2026 — When Saying No Makes Better Videos: Dual Gatekeeping for Pedagogically Grounded AI Content Creation
Zuo Instructor Power GenAI Writing 2026 — Power relations perceived by college instructors grappling with GenAI in writing (Zuo, Xu & Dunning 2026)