Artificial intelligence and energy education: synergizing technological innovation with human capital development for a sustainable future - for the applied energy 50th anniversary special issue

As Applied Energy commemorates five decades of publishing pioneering research at the nexus of energy systems and sustainability, this perspective article examines an emerging frontier that will shape the next twenty years: the synergistic integration of artificial intelligence (AI) with energy education. The global energy transition demands not only technological innovation but also a workforce equipped with new competencies to understand, design, manage, and govern increasingly complex, data-driven smart energy systems characterized by intermittent renewables and decentralized grids. This paper highlights the dual role of AI in this context, both as a transformative tool for energy research and innovation and as an enabler and catalyst for reimagining energy education. Through a targeted literature review, we identify a critical gap: the absence of domain-specific pedagogical frameworks. While the energy sector urgently needs AI-fluent talent, generic educational AI lacks the physical rigor required for complex power engineering, risking “black-box” overreliance rather than acting as a trustworthy tutor that develops independent critical thinking. To address this, we propose a conceptual framework for AI-enhanced energy education that encompasses three core dimensions: (1) AI as a subject of study within energy curricula, (2) AI as an instructional medium for energy knowledge and skills, and (3) AI as an analytical engine for understanding learning processes and outcomes in energy education. We critically examine the “black-box” risks of AI overreliance, advocating for “Socratic” AI designs that compel students to verify outputs against physical laws, thereby developing independent, trustworthy professional judgment. This framework is empirically grounded through a real-world case study of a power systems course at Tianjin University, demonstrating how a domain-specific Large Language Model (LLM) tackles concrete engineering learning barriers. We conclude with a research agenda for the next decade, identifying priority areas where the Applied Energy community can lead in shaping both the technological and human dimensions of the energy future.