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Session Description

Free ticketed event
This interactive, hands-on workshop bridges the gap between understanding how AI systems learn and designing meaningful ways to integrate AI tools into engineering and design courses. Participants will explore how machine learning—the foundation of most AI systems—enables pattern recognition, decision-making, and adaptive learning. Through short demonstrations and collaborative activities, attendees will gain both conceptual literacy and practical strategies for helping students use AI effectively and creatively in engineering contexts.

The workshop begins by demystifying how AI and machine learning work. Facilitators will explain, in accessible terms, how data-driven algorithms identify patterns and generate outputs—whether classifying images, predicting outcomes, or generating text. Participants will engage in a simple visual demonstration using a web-based tool such as Google’s Teachable Machine to experience how training data influences a model’s performance. This foundational understanding helps educators and students alike grasp how AI systems process data and why their outputs vary based on training.

Building on this foundation, the session transitions into applications for engineering and design education. Facilitators will showcase classroom-tested examples of integrating AI into design processes—such as using ChatGPT or other AI systems for concept generation, material selection brainstorming, or sustainability prompts. Participants will analyze how these tools can support creativity, critical thinking, and reflection without replacing students’ own problem-solving and communication skills.

The second half of the session focuses on curriculum design. Working in small groups, participants will co-develop a short module or assignment that uses AI in a design or engineering context. Each group will use a guided template to define learning objectives, describe how AI will be incorporated, and identify assessment strategies. By the end of the session, each participant will leave with a concrete, adaptable activity they can implement in their own course.

This workshop is particularly relevant for faculty interested in:
Understanding how machine learning underlies the AI tools increasingly used by students.
Integrating AI literacy into existing STEM and design courses.
Developing assignments that balance creativity and technical rigor.
Aligning AI-enhanced learning outcomes with ABET student outcomes and institutional goals.

Speakers

1. Burcu Ozden

   Pennsylvania State University

   Dr. Burcu Ozden is an Assistant Professor of Physics and Engineering at Penn State Abington, where she teaches engineering design, circuits, and first-year seminars. Her research spans defect engineering in two-dimensional materials, photoelectrocatalytic systems for water treatment, and engineering education focused on inclusive design, sustainability, and AI integration in design learning. Dr. Ozden leads several NSF- and DOE-funded projects and serves as an EDGE (Experiential Digital Global Engagement) Influencer, promoting global, technology-enhanced collaboration in STEM education. She has extensive experience designing hands-on learning modules that help students apply emerging technologies such as artificial intelligence to creative, socially relevant engineering problems.

2. Dr. Sabahattin Gokhan Ozden

   Dr. Sabahattin Gokhan Ozden is an Associate Professor of Information Sciences & Technology at Penn State Abington. He is also the inaugural program chair of the Data Science program at PSU Abington. He holds a Ph.D. in Industrial and Systems Engineering from Auburn University. He graduated from MISE program in Auburn University Industrial and Systems Engineering Department in 2012. He has a Bachelor of Science in Software Engineering and a Double Major in Industrial Systems Engineering from Izmir University of Economics. He is a two times recipient of the Material Handling Education Foundation, Inc. Scholarship. His research has been awarded by National Science Foundation, Penn State Strategic Initiative Seed Grant, Penn State Social Science Research Institute, Penn State Justice Center for Research, Penn State Leonhard Center, and Penn State Abington Chancellor’s grant. His research interests include warehouse design, heuristic optimization, sequencing and scheduling, simulation, model driven engineering, and engineering education.