Exploring Artificial Intelligence Tools for Materials Science in Engineering: A Work-in-Progress in Undergraduate Classroom Integration

The onset of user-friendly Artificial Intelligence (AI) tools has significantly disrupted traditional educational methodologies within higher education. This paper explores the application of advanced AI technologies, specifically GPT-4, to enhance student assessments in material science education. It details the strategic integration of AI to develop dynamic and personalized assessments, such as multiple-choice quizzes and open-ended case studies, aiming to redefine classroom engagement by adapting to diverse student needs and learning environments. AI is a promising transformative tool in educational assessment processes within material science. Utilizing GPT-4, the study investigates the creation of diverse assessment forms, showing AI's capability to tailor assessments to individual learning requirements and curriculum standards. This approach deepens student engagement and advances educational strategies by equipping educators with dynamic tools that respond to the evolving educational landscape. The current study particularly emphasizes prompt engineering with AI, a critical element in optimizing AI's utility for generating advanced, curriculum-aligned assessments. It assesses how effectively crafted prompts can guide AI to produce more relevant educational content, thereby enhancing learning experiences. As effective prompts are developed, GPT-4's potential to customize assessments to meet specific student needs and address the complexities of material science theories is highlighted, presenting a valuable approach to boost student engagement, and understanding. These AI-driven methodologies aim to enhance the creative process in educational material development, offering educators an expanded array of tools for designing customized instructional materials. The role of AI in enriching educational content is expected to significantly elevate student engagement and deepen their comprehension of complex material science concepts. The study documents iterative testing and refinement of AI tools in producing and improving educational materials, providing tangible examples of AI's contributions to educational innovation. The results add important insights to the discourse on integrating AI into engineering education, underscoring its potential as a collaborative tool in a rapidly evolving academic environment.