Enhancing Environmental Regulation Education With Generative AI and Reflective Learning
Presented by:
Jorge Arreola Vargas, Texas A&M University
This project shows AI can streamline environmental regulatory learning, boost engagement, and support research, while revealing broad outputs, occasional errors, and need for improved prompts.
Keywords:
AI‑Enhanced Learning, Environmental Regulation, Critical Thinking
Abstract:
This project explores the integration of generative AI into environmental regulation education, examining how tools like ChatGPT can support student learning, research, and engagement. AI helped students brainstorm hypothetical scenarios, organize complex regulatory information, and navigate frameworks such as the Clean Air Act and Clean Water Act. While AI improved efficiency and provided useful starting points, students also encountered limitations, including broad outputs, occasional inaccuracies, and weak citation practices. These challenges required verification with authoritative sources and highlighted the importance of critical thinking and prompt‑engineering skills. Overall, the project demonstrates AI’s potential when paired with thoughtful oversight and reflective learning.
Outcomes:
1. Identify effective strategies for integrating generative AI into environmental regulation coursework to enhance engagement, research efficiency, and conceptual understanding.
2. Evaluate common AI limitations and apply approaches that promote verification, critical thinking, and responsible use.
3. Refine instructional activities that strengthen students’ prompt‑engineering skills, improving the precision, relevance, and reliability of AI‑generated academic work.
Hear it from the author:
Transcript:
References:
Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., ... & Sandison, C. (2023). ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 48(4), 559-614.
Ruff, E. F., & Zemke, J. M. (2025). Discussing the Ethics of Professional AI Use in Undergraduate Chemistry Courses. Journal of Chemical Education, 102(4), 1457-1464.
Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in Education and Research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965-2971.