2025 ASEE Annual Conference & Exposition

Artificial intelligence (AI) has the potential to transform how core engineering courses are designed and delivered. This study explores the complete redesign of three chemical engineering courses—Thermodynamics, Material and Energy Balances and Kinetics—using AI to generate the syllabus, course content, homework, quizzes, and case studies. The central objective is to assess how AI can be leveraged to create educational materials while also investigating students' evolving perceptions of AI's reliability and their confidence in using AI for problem-solving in engineering contexts.
In the Thermodynamics course, students will be surveyed at the start to assess their current use of AI tools, including the types of AI they rely on, how reliable they find AI-generated answers, and how confident they are in the information they receive from AI. These pre-course perceptions will be captured using a combination of Likert-scale and open-ended questions. Throughout the course, students will engage with AI-generated homework and quiz questions, critically analyzing AI-produced solutions in comparison to their own problem-solving methods. After this experience, a post-course survey will be conducted to measure any shifts in students’ confidence in AI, their perceptions of its reliability, and their understanding of AI’s role in solving complex thermodynamics problems.
The guiding research questions are: How effective is AI in designing Chemical Engineering courses? How does the integration of AI in the design of course content and assessments in Thermodynamics influence students’ perceptions of AI’s reliability and their confidence in using AI as an engineering tool?

The study adopts a survey-based approach to capture students' evolving perceptions of AI. A pre-implementation survey will assess their initial confidence, use, and perceived reliability of AI, while a post-course survey will gauge whether these perceptions have shifted after interacting with AI-generated course content. Likert-scale questions will quantify changes, and open-ended questions will provide qualitative insights. The results will offer an in-depth understanding of how students’ perceptions of AI evolve as they critique AI-generated answers in a structured educational setting.
Faculty will use AI to design the syllabus, course materials, and assessments, providing a unique opportunity to explore the effectiveness of AI in the course development process. This work aims to provide a roadmap for future AI-driven course redesigns and offer insights into how AI influences both learning outcomes and students’ confidence in using AI technology in professional settings. Preliminary case studies in Thermodynamics, Material and Energy Balance and Kinetics courses suggest that AI can enhance course material design by providing instructors with advanced problem-solving tools and real-time feedback mechanisms. However, challenges such as AI biases and content accuracy remain significant hurdles. This paper discusses the transformative potential of Generative AI in engineering education, with a specific emphasis on overcoming pedagogical challenges in teaching sophomore and junior chemical engineering courses.