Synthesis — Vargas, Chiappe & Durand (2024) conduct a PRISMA systematic review of 60 peer-reviewed articles spanning three decades to map how Situated Learning has evolved and how AI can reshape education around it. Situated learning — learning through authentic, real-world contexts, communities of practice, legitimate peripheral participation, and contextual knowledge — confronts the traditional school's vertical, one-way passive instruction, its fixation on predefined outcomes, and teachers' limited contextual knowledge. The authors argue AI offers concrete solutions: adaptive systems tuned to learners' evolving needs, intelligent tutoring embedded in authentic scenarios, automation of administrative tasks, and data-driven teacher support. Framed through Education 4.0, AI and situated learning are cast as complementary — AI providing adaptive scaffolding and analytics to scale situated learning's benefits while situated learning grounds AI-driven education in real-world practice and complexity. Human guidance remains essential for ethical grounding.

Reshaping Education in the Era of Artificial Intelligence: Insights from Situated Learning Related Literature

Vargas, Chiappe & Durand (2024) offer a systematic synthesis of how situated learning — a theory rooted in the early 1990s work of Lave and Wenger — can be reinvigorated by artificial intelligence. Using the PRISMA method, the review analyzed 60 peer-reviewed journal articles from Scopus over three decades (1990s to 2022), extracting and analyzing the concepts associated with situated learning both qualitatively and quantitatively. The paper positions this conversation within Education 4.0, the educational face of the fourth industrial revolution in which IoT, robotics, immersive environments, and especially AI reconfigure the learning experience.

Key Findings

• Situated learning is a dynamic, evolving concept organized around the durable core idea of "learning in context," with emphasis shifting across eras: workplace learning in the 1990s, ICT-mediated communities of practice in the 2000s, mobile and gaming immersion in the 2010s, and Education 4.0 technologies (augmented reality, immersive environments, AI, IoT, educational robotics) in 2020–2023.
• Three obstacles block situated learning. (1) The traditional school system is hegemonic, centralizing knowledge and relying on vertical, one-way passive transmission with little connection to real contexts. (2) The predominant educational approach fixates on predefined outcomes, competencies, and content, excluding learners' and teachers' knowledge, interests, and needs. (3) Teachers often lack contextual knowledge and sensitivity to the cultural diversity and everyday realities of their students.
• Six challenges to implementation: linking art and creativity into teaching; shaping critical, context-aware individuals with ethical capacity; recognizing students and teachers as active agents; understanding situated learning beyond mere practice; rethinking linear, static teaching toward dynamic horizontal models; and treating context as a facilitator of expression.
• AI offers concrete solutions to these obstacles: adaptive systems tailored to students' evolving needs, intelligent tutoring situated in authentic scenarios, automation of administrative tasks, and data-driven teacher support.
• AI enables personalization and student agency, letting learners co-direct their pathways in collaboration with intelligent agents — a shift away from the industrial, mass-production model of schooling.
• Human guidance remains essential: instructors must instill ethical reasoning to question AI biases, so that AI augments rather than replaces the human, ethical dimension of education.
• AI and situated learning are complementary. AI provides adaptive scaffolding and analytics to scale situated learning's advantages; situated learning grounds AI-driven education in real-world practices and complexity — together demanding a reinvention of learning ecosystems.

Educational Significance

The review is significant because it bridges a classical learning theory and contemporary AI research, showing how an Constructivist, context-anchored view of learning can steer AI deployment rather than merely reacting to it. For educators, it reframes the Teacher Role as designer of flexible, context-connected learning spaces supported — not supplanted — by AI. For instructional designers and policymakers, it argues that effective Adaptive Learning and Personalized Learning depend on grounding AI in authentic contexts and communities of practice, and that scaling such experiences via AI can make engaged, situated learning accessible to far more students. Its insistence on human ethical guidance connects directly to ongoing debates about AI Education and the responsible integration of AI in Higher Ed and schooling.

Connected Concepts

• Situated Learning
• Learning Theories
• Constructivist
• Experiential Learning
• Embodied Learning
• Collaborative Learning
• Adaptive Learning
• Personalized Learning
• Teacher Role
• Instructional Design
• AI Education
• Higher Ed

Connected Articles

• Learning Theories — umbrella concept; situated learning is documented as one of its activity-and-context learning theories
• GenAI Educational Outcomes Meta Analysis — broader synthesis of AI's effects on learning outcomes
• AI Vocational Education Training Review — documents the constructivism/behaviorism gap in AI for education, complementing this review's constructivist framing
• Self Directed Growth Generative AI Learning Analytics — related framing of AI as a scaffold for self-directed, context-aware learning
• Gerlich AI Tools Cognitive Offloading Critical Thinking — the role of human guidance and critical engagement with AI

Citation

Vargas, E. G., Chiappe, A., & Durand, J. (2024). Reshaping education in the era of artificial intelligence: insights from Situated Learning related literature. Journal of Social Studies Education Research, 15(2), 1–28.