2025 ASEE Annual Conference & Exposition

AI as a Catalyst for Transforming Higher Education: Enhancing Teaching Strategies and Student Outcomes

Presented at Civil Engineering in the Age of AI

Artificial Intelligence (AI) is rapidly reshaping higher education, transforming teaching strategies and
enhancing student outcomes. This paper explores AI's role as a catalyst for personalized learning, adaptive teaching methods, and data-driven decision-making. Integrating AI into curriculum design enables
educators to create dynamic, student-centered environments catering to diverse learning needs. AI tools can automate administrative tasks, provide real-time feedback, and foster active learning, allowing instructors to focus on mentorship and complex problem-solving. The study highlights AI-enhanced
analytics that offer insights into student performance and engagement, facilitating targeted interventions to boost retention and success rates. Preliminary results indicate that faculty engagement with AI has led to innovative course designs and improved teaching practices, while student interactions with AI tools
have contributed to enhanced academic performance and equity in success across diverse demographics.
Our research also includes AI-driven pedagogical innovations aimed at inclusive and culturally responsive teaching. Data from participating STEM faculty reveal significant improvements in student engagement and learning outcomes. This paper outlines these findings, discusses implications for future practice, and highlights strategies for scaling AI integration across institutions to promote equitable learning
environments.
This transformation not only benefits students by providing tailored educational experiences but also
empowers educators to focus on fostering critical thinking and creativity. As AI continues to evolve, its role as a catalyst in higher education will undoubtedly grow, offering unprecedented opportunities for the
advancement of teaching strategies and academic achievement. Despite the numerous benefits, challenges such as data privacy, ethical considerations, and the digital divide must be addressed to ensure equitable and effective implementation. Ultimately, AI has the potential to revolutionize higher education by making learning more efficient, inclusive, and adaptable to the needs of the learner.
Keywords: Artificial Intelligence, higher education, personalized learning, adaptive teaching, student outcomes, data-driven education.

Authors

Siddaiah Yarra University of California Merced [biography]

Dr. Yarra is currently serving as an Assistant Professor of Teaching in Civil and Environmental Engineering at the University of California, Merced. He specializes in pedagogical research, AI in higher education, and forensic engineering education, focusing on innovative teaching methods and supporting academically talented, first-generation, and economically disadvantaged students throughout their academic journey. His disciplinary research includes structural health monitoring, computational and analytical modeling of adaptive materials, testing of structural components and systems, and applying advanced materials to enhance the sustainability and resilience of civil engineering structures. As a teaching-focused faculty member, he is committed to integrating cutting-edge research with hands-on learning to improve engineering education and prepare students for professional success.
Prof. Abbas Ghassemi University of California, Merced [biography]

Dr. Ghassemi, Professor Emeritus of Chemical Engineering, has been a Teaching Professor in Civil and Environmental Engineering at the University of California Merced since February 2018. Prior to that, Dr. Ghassemi served as Director of the Institute for Energy and Environment as well as Executive Director of WERC, a Consortium for Environmental Education and Technology Development, and Professor of Chemical Engineering at New Mexico State University from 1999 to 2015.

Professor Ghassemi has extensive expertise in the areas of innovative programs in active and adaptive learning methodologies, research, outreach in renewable energy, advanced water treatment, carbon cycle, carbon generation and management, air pollution control and biofuels. As a teaching professor, he co-leads an experiential learning program which includes senior level capstone, modeling and design of energy systems and advanced water treatment courses. Additionally, Professor Ghassemi teaches classes in heat transfer, renewable energy fundamentals and environmental engineering fundamentals.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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