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Situated Learning

● high · created 2026-08-16 · updated 2026-08-19 · situated-learning, experiential-learning, constructivist, learning-theories, communities-of-practice, contextual-knowledge, instructional-design, ai-education

Situated Learning — the theory, rooted in the early-1990s work of Lave and Wenger (1991), that learning is not an isolated, decontextualized act but occurs through participation in authentic activities, contexts, and cultures. Knowledge is co-constructed by learners and peers within communities of practice, and novices learn through legitimate peripheral participation — absorbing the culture, language, and practices of expert members as they move from the periphery to the center of a community. Emphasis falls on learning by doing in real-world situations, where Assessment emerges from the task itself rather than being separated from it.

Situated learning is one of the activity-and-context theories within the wiki's Learning Theories strand. It takes up Vygotskian themes of social construction but adds a strong emphasis on the intimate integration of "doing" and "learning" and on the importance of communities of practice. As an educational stance it confronts traditional, standardized schooling by foregrounding the learner's sociocultural context as a key element for acquiring skills and appropriating knowledge relevant to their reality.

In the AI-in-education literature, situated learning matters because it provides a design lens for AI: adaptive systems, intelligent tutoring in authentic scenarios, and immersive simulations can ground AI-driven education in real-world contexts, while situated learning in turn offers AI a meaningful anchor in authentic practice and complexity. The two are widely treated as complementary, with human guidance remaining essential for ethical grounding.

Situated learning as a design lens for AI

The wiki's research treats situated learning not merely as an abstract theory but as a concrete design and evaluation framework for AI in education:

  • Opportunities and obstacles. A PRISMA systematic review of 60 articles (three decades) finds that AI can augment situated learning — through adaptive systems tailored to students' evolving needs, intelligent tutoring situated in authentic scenarios, automation of administrative tasks, and data-driven teacher support — while the main obstacles are the traditional school's one-way passive learning, an over-emphasis on predefined outcomes, and teachers' limited contextual knowledge. Human guidance remains essential for ethical grounding.^Vargas Situated Learning AI Review 2024
  • AI as a catalyst connecting education to reality. AI can act as a catalyst for situated learning by connecting education with reality and authentic contexts, enabling learning grounded in real-world scenarios.^Vargas AI Catalyst Situated Learning 2026
  • Mediational artifacts in authentic inquiry. In science learning, AI tools (virtual labs, simulations, intelligent tutoring) function as "mediational artifacts" that extend situated learning by enabling digital communities of practice and boundary-crossing between school, real-world, and interdisciplinary contexts — transforming students from "knowledge learners" into "scientific practitioners."^Li AI Science Situated Learning Teachers 2025
  • Situated curriculum devices. AI literacy can be developed through Episodes of Situated Learning — active teaching instruments (anticipate, produce, reflect) that build AI competencies through real-world problem-solving rather than abstract instruction.^Panciroli AI Literacy Episodes Situated Learning
  • Situated AI ethics. Ethical reasoning about AI is itself best treated as situated — grounded in cultural-historical and ecological context rather than abstract principles.^Raffaghelli Situated AI Ethics 2026

Situated learning connects closely to Embodied Learning (both stress the grounding of cognition in context and action), Distributed Cognition (learning distributed across people, tools, and contexts), Experiential Learning, and Constructivist theory. In AI education it grounds the critique of decontextualized, disembodied learning: AI design that keeps learners anchored in authentic practice preserves the situatedness that durable learning requires.

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