This article explores the evolution of constructionism as an educational framework, tracing its relevance and transformation across three pivotal eras: the advent of personal computing, the networked society, and the current era of generative AI. Rooted in Seymour Papert’s constructionist philosophy, this study examines how constructionist principles align with the expanding role of digital technology in personal and collective learning. We discuss the transformation of educational environments from hierarchical instructionism to constructionist models that emphasize learner autonomy and interactive, creative engagement. Central to this analysis is the concept of an “expanded personality”, wherein digital tools and AI integration fundamentally reshape individual self-perception and social interactions. By integrating constructionism into the paradigm of smart education, we propose it as a foundational approach to personalized and democratized learning. Our findings underscore constructionism’s enduring relevance in navigating the complexities of technology-driven education, providing insights for educators and policymakers seeking to harness digital innovations to foster adaptive, student-centered learning experiences.

Business schools teach students to converse with generative AI, while the work those graduates will do is being reshaped by intentional autonomous agents that execute multi-step tasks across files and applications and return finished deliverables. This paper argues that the resulting gap is wide enough to require a change at the level of the curriculum, and that fluency in directing such agents should become a graded competency in the MBA core rather than an optional elective. The argument draws on the authors' experience teaching the adjacent discipline to software engineers, adapting a discipline-based curriculum for agentic software engineering to the deliverable-centred work of the business graduate. The competency is defined not as a checklist of equal parts but as a single analytical judgment, the direction of an agent, with oversight, verification, and the safe handling of data and access as dimensions of doing that one thing well. Empirical work on knowledge-worker productivity, on the failure modes of agentic systems, and on the shallow ways students currently use these tools supports both the diagnosis and the design. The paper offers a concrete teaching pattern built around a planted error, in which an agent produces a result that is plausible and wrong and the student's task is to catch it, because the value a graduate adds lives in the check rather than in the generation. It closes with two models for placing the competency in a programme, the obstacle of faculty readiness, and an honest admission that the framework awaits empirical validation.

Artificial intelligence (AI) is rapidly reshaping educational practice, challenging long held assumptions about teaching and learning. This article integrates conceptual perspectives from recent books (Genesis by Eric Schmidt, Henry Kissinger and Craig Mundie, CoIntelligence by Ethan Mollick, and The Inevitable by Kevin Kelly) with empirical insights from popular AI podcasts and Anthropic public releases. We examine seven key domains: emotional support, creativity, contextual understanding, student engagement, problem solving, ethics and morality, and collaboration. For each domain, we explore AI capabilities, opportunities for transformative change, and emerging best practices, drawing equally from theoretical analysis and real world observations. Overall, we find that AI, when used thoughtfully, can complement and enhance human educators in fostering richer learning experiences across cognitive, social, and emotional dimensions. We emphasize an optimistic yet responsible outlook: educators and students should actively shape AI integration to amplify human potential in creativity, ethical reasoning, collaboration, and beyond, while maintaining a focus on human centric values.