Reshaping education in the era of artificial intelligence: insights from Situated Learning related literature

Abstract

This review explores how artificial intelligence (AI henceforth) can reshape education through insights from situated learning literature. The objective was to critically examine opportunities and challenges of situated learning, and how AI could augment strengths while overcoming obstacles. A systematic review using the PRISMA method analyzed 60 articles from peer-reviewed journals over three decades. Key concepts associated with situated learning were extracted and analyzed qualitatively and quantitatively. Findings identified major obstacles: the traditional school system's one-way passive learning; the predominant educational approach fixated on predefined outcomes; and teachers' lack of contextual knowledge. AI presents solutions including adaptive systems tailored to students' evolving needs; intelligent tutoring situated in authentic scenarios; automation of administrative tasks; and data-driven teacher support. When implemented thoughtfully, AI has the potential to enhance situated learning through increased personalization, interactivity, and real-world connections. This promises a better effective, adaptive education - but human guidance remains essential for ethical grounding. This review offers teachers, researchers, and policymakers valuable insights on integrating both AI and situated learning to keep education relevant in an interconnected world.