AI + Policy Simulation: Teaching Reform and Practice in Intelligent Decision-Making for Sustainable Energy and Environmental Economics

This project aims to redesign the teaching and learning of Environmental & Energy Policy (SEE6201/8219) and Energy & Environmental Economics (SEE3002) by shifting the traditional “theory–case–report” paradigm toward a “data–simulation–decision” model that equips students to conduct forward-looking, evidence-based policy analysis. Building on recent advances in accessible AI tools, the project will integrate AI into the curriculum as both a research assistant (supporting literature review, data acquisition/cleaning, and visualization) and a policy laboratory (enabling dynamic scenario testing through AI-supported policy simulators).The reform will implement three interconnected innovations. First, a new module—“Foundations of AI Applications in Energy & Environmental Policy Analysis”—will be embedded into the course to introduce core concepts such as prompt design for policy questions, real-time data workflows, fundamentals of simulation modeling, and critical discussion of ethics and bias. Second, classic teaching cases (e.g., carbon border adjustment, electricity market reform, eco-compensation) will be upgraded into AI-assisted case studies, requiring students to source the latest public data and model policy outcomes under alternative tax rates, subsidy schemes, or regulatory designs. Third, active-learning formats—including policy hackathon workshops, role-playing within multi-agent simulation games, and a flipped classroom supported by an AI tutor—will be introduced to deepen systems thinking and strategic reasoning under uncertainty.Assessment will be restructured to emphasize both process and outcomes: (i) process-oriented evaluation of students’ documented AI analysis workflow (e.g., prompt iteration logs and modeling decisions), and (ii) outcome-oriented evaluation via “dynamic policy briefs” that combine written analysis with interactive visualizations and/or a simple simulation prototype. A dedicated criterion will assess students’ ability to critically interrogate AI-generated results, including identifying data bias, model assumptions, and limitations.Deliverables include a replicable syllabus and teaching package, a repository of AI-assisted cases, and a set of teaching-ready micro-simulators (e.g., carbon price and renewable subsidy impact simulators). The project will enhance students’ employability and research readiness by cultivating human–AI collaboration competency for responsible policy decision-making, while offering a scalable model for interdisciplinary teaching innovation aligned with “dual-carbon” and SDG-oriented talent development.