2026 ASEE Annual Conference & Exposition

An Instructor-Guided AI Scaffolding Framework for Enhancing Learning in Freshman Engineering Courses

Presented at Engineering Technology Division (ETD) Technical Session 2

Freshman engineering students frequently encounter difficulties transitioning from procedural learning in high school to the conceptual and analytical reasoning expected in university-level engineering. Large class sizes, variations in individual learning pace and prior preparation, combined with abstract and conceptually demanding content, often lead to disengagement, cognitive overload, performance gaps, and reduced academic persistence. These challenges highlight the difficulty of meeting diverse learner needs within traditional lecture-centered environments. Addressing them requires scalable yet personalized instructional support that complements, rather than replaces, human teaching presence.
This paper introduces an Instructor-Guided AI Scaffolding Framework designed to enhance conceptual understanding, procedural fluency, and reflective learning in first-year engineering classes. The framework integrates five layers of adaptive support, conceptual, procedural, strategic, reflective, and affective, each aligned with the developmental needs of novice learners. The AI-integrated platform employs adaptive algorithms to deliver real-time, personalized interventions such as visual explanations of abstract concepts, stepwise problem-solving hints, design-thinking prompts, and motivational feedback. Instructors guide the AI’s deployment by customizing scaffold parameters through an open-source platform, monitoring student progress via real-time analytics, and ensuring alignment with course objectives.
The study will pilot these AI scaffolds in freshman-level engineering courses to examine their effects on students’ conceptual understanding, engagement, and cognitive load. Planned mixed-methods evaluation will use concept inventories, learning analytics, and reflective responses to measure changes in performance and perception. The anticipated outcomes will inform evidence-based strategies for integrating AI into engineering pedagogy, advancing equitable and engaging learning experiences across foundational engineering courses.

Authors

Dr. Sonia Mary Chacko http://orcid.org/0000-0002-2057-2132 Bowling Green State University [biography]

Dr. Sonia Chacko is an Assistant Professor of Robotics Engineering at Bowling Green State University. She earned her Ph.D. in Mechanical Engineering with a specialization in Robotics from New York University, where she also served as a research assistant on NSF-funded robotics-integrated K–12 STEM projects. Her research integrates robotics, extended reality, and artificial intelligence to enhance engineering learning and human–robot collaboration. Her current work focuses on developing scalable, immersive learning modules that foster conceptual understanding and innovation in STEM education.
Dipika Bogati Bowling Green State University [biography]

Dipika Bogati received B. Sc. (Hons.) in Computing from London Metropolitan University from Kathmandu, Nepal in 2021. She is Master of Science candidate in Computer Science from Bowling Green State University (BGSU) expecting to graduate at May 2026 and serves as a Graduate Teaching Assistant at BGSU since January 2025. She brings over three years of industry experience as a Python Data Engineer at Verisk Nepal. Her current research interest involves Data Processing Techniques in AI, AI-driven chatbots, AI4SE (Artificial Intelligence for Software Engineering), Data Engineering.
Mohammed Shakeel Shaik Bowling Green State University [biography]

Mohammed Shakeel Shaik received the B.Tech. degree in computer science and engineering from Jawaharlal Nehru Technological University, India, in 2023, and is currently pursuing the M.S. degree in computer science at Bowling Green State University, Bowling Green, OH, USA. He is currently a Graduate Research Assistant with the College of Engineering at Bowling Green State University. His research interests include machine learning, wireless communication systems, data analytics, and artificial intelligence.
Dr. Dulaj Heshan Gunasinghe http://orcid.org/0000-0001-8537-7082 Bowling Green State University [biography]

Dr. Dulaj Gunasinghe received the B.Sc. degree (with Honors) in engineering from the Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa, Sri Lanka, in 2018, and the Ph.D. degree in electrical and computer engineering from the School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL, USA, in 2024. He is currently an Assistant Professor with the School of Engineering, Bowling Green State University, Bowling Green, OH, USA. His research interests include intelligent reflective surfaces, integrated sensing and communication, extremely large antenna arrays, and STEM education.

Download paper (889 KB)

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.

» Download paper

« View session

For those interested in:

Broadening Participation in Engineering and Engineering Technology
computer science
engineering
engineering technology
Faculty
undergraduate