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Session Description

Free ticketed event
Artificial Intelligence (AI) has captured widespread attention across education, industry, and the media. While AI originated as a discipline in the 1950s, advances in computational power and the rapid emergence of new technologies, AI had taken center stage.
This workshop is designed for middle and high school educators, as well as outreach practitioners, seeking to enhance student engagement in AI through accessible, hands-on learning. Participants will explore a collection of “unplugged” activities that require no computer equipment, making them suitable for classrooms, camps, and outreach events with limited technology resources.
Throughout the session, participants will engage in a sequence of interactive 15 to 20-minute exercises that model key AI concepts such as classification, pattern recognition, and iterative improvement. Everyday objects—including fruit and rubber ducks—serve as tangible teaching tools to demonstrate how AI systems learn to categorize and make decisions. A complementary card-based activity introduces the principles of feedback and model refinement, allowing participants to experience the adaptive nature of machine learning.
A third activity provides a tactile representation of neural networks using string and cardboard tubes to simulate information flow between interconnected “neurons.” This exercise offers an accessible, “under-the-hood” look at how neural networks process inputs and generate outputs.
The fourth segment introduces no-code, web-based platforms that let students explore AI concepts in a low-barrier, interactive environment. For example, participants will experiment with computer vision—an area of AI that enables computers to identify objects, faces, and movement—using free, browser-based tools.
By the conclusion of the workshop, attendees will have gained practical, classroom-ready strategies and adaptable materials for introducing AI fundamentals. These unplugged activities not only make abstract computational ideas concrete but also empower educators to cultivate curiosity and confidence in students as they explore how intelligent systems perceive, learn, and improve.

Speakers

1. Dr. Stephany Coffman-Wolph

   Ohio Northern University

   Dr. Stephany Coffman-Wolph received her PhD from Western Michigan University and is currently an Assistant Professor in the ECCS Department (in Computer Science) at Ohio Northern University (ONU). Previously, she worked at The University of Texas at Austin and West Virginia University Institute of Technology (WVU Tech). She is actively involved in community outreach with a goal of increasing the number of women in STEM and creating effective methods for introducing young children to CS concepts and topics – especially Artificial Intelligence. Stephany teaches a variety of courses at ONU, using her preferred active learning style, including Software Development, Programming Languages, Theory of Computation, Artificial Intelligence, and Deep Learning. Dr. Coffman-Wolph’s research interests include: Artificial Intelligence, Fuzzy Logic, Software Engineering, STEM Education, and Diversity and Inclusion within STEM.

2. Dr. Kimberlyn Gray

   West Virginia University Institute of Technology

   Dr. Kimberlyn Gray received her PhD from Louisiana Tech University and is currently a Professor of Chemical Engineering at West Virginia University Institute of Technology (WVU Tech). She serves as an Engaged Teaching Scholar for the Teaching and Learning Center at WVU. Kimberlyn is active in outreach, teaching several k-12 summer programs, and focusing on introducing engineering topics to k-12 students. Kimberlyn teaches a range of first year and chemical engineering courses, including Introduction to Engineering, Engineering Math, Material and Energy Balances, and Biomaterials.