Artificial Intelligence in Chemical Engineering Education Opportunities, Challenges, and Talent Cultivation

Abstract

The rapid advancement of artificial intelligence (AI), especially large language models (LLMs), has revolutionized chemical engineering research through innovations in process optimization, sustainable resource management, big data integration, automated simulation, and predictive modeling. These developments are particularly pertinent to disciplines such as Chemical Engineering and Technology, which emphasize reaction engineering and process design, and Resource Recycling Science and Engineering, focused on circular economy and resource recovery. In education, AI facilitates enhanced learning via virtual simulations, adaptive platforms for individualized instruction, and collaborative tools that boost efficiency and student engagement. Nonetheless, obstacles arise from computational inaccuracies, conceptual errors, data biases, and fabricated outputs, potentially compromising academic integrity. Excessive dependence on AI risks eroding students' critical thinking, independent problem-solving, and innovative capabilities. To counter these, curricula must reinforce foundational theory while cultivating observational acuity, logical reasoning, ethical discernment, and exploratory curiosity. This integrated strategy aims to develop versatile professionals capable of addressing global sustainability imperatives, including low-carbon processes and resource efficiency.