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Session Description

Free ticketed event
Generative Artificial Intelligence (AI) continues to disrupt how we teach and learn. With some models now outperforming humans in certain task, and capable of solving assignments that were traditionally used to assess student knowledge and support formative learning, there is a need to re-examine the types of academic tasks we assign and evaluate (Salam et al. 2024).

While some have proposed returning to medieval methods, such as pen-and-paper exams, to preserve academic integrity, engineering educators reimagine the education system to prepare students for the future. We need to rethink how we move forward and redesign learning and assessment in the age of generative AI. Our responsibility is to chart the course for engineering education that advances alongside technological and industrial revolutions rather than relying solely on older practices. This requires innovative thinking about how to create learning experiences that are creative and engender critical thinking and problem-solving skills, and that enable ethical and responsible use, in preparing students for a world and workforce that will continue to be profoundly influenced by generative AI.

Furthermore, as generative AI becomes more integrated into industry and education, engineering educators can play a role in designing GPTs tailored to better support their students and in demonstrating good AI practices to them. This can make it possible to provide personalized Teaching assistants to each student, which Bloom noted can increase students' learning by two sigma (Bloom, 1984). A proposition of four decades ago that generative AI now provides the possibility of actualization.

During this workshop, we will introduce frameworks for advancing learning and assessment in the era of AI that could enhance learning, while also fostering AI literacy, a valuable skill for engineering graduates. Participants will explore strategies to integrate generative AI responsibly into teaching and assessment, ensuring that students are equipped for the challenges and opportunities of the future. By doing so, educators can better prepare learners to collaborate with AI in meaningful, discipline-relevant ways, while also leveraging AI to improve their course offerings.

Expected Outcomes
Participants will:
• Gain a clear understanding of the challenges and opportunities posed by generative AI in engineering learning and assessment
• Learn practical strategies for designing assessments that maintain rigor and relevance in an AI-driven world.
• Be able to develop and personalize a Generative Pretrained Transformer (GPT) chatbot to support learning and assessment.
• Apply tools and frameworks (including TAIWO) to evaluate and enhance student learning experiences.

Format
• Introductions [10 minutes]: Facilitators will briefly present the purpose of this special session and share background information about each organizer.
• Icebreaker and Audience Survey [10 minutes]: Participants will be divided into small groups to introduce themselves, share what brought them to the session, and discuss the primary challenge they face with learning and assessment in the age of AI.
• Teamwork 1: Co-Creating Assessment and Co-Learning with AI [15 minutes]: The facilitator will present theoretical and practical frameworks that support learning and assessment in collaboration with AI. Afterward, each group will redesign one assessment or learning activity (provided by the facilitator or created by the group). They should consider AI literacy, academic integrity, and creativity. If possible, groups should propose ideas for future-proof assessments or learning activities that ensure students learn effectively while collaborating with AI – and determine how to assess that learning.
• Share-Out [7 minutes]: Each team will share key points from their discussion with the larger audience.
• GPT Chatbot Development for Learning & Assessment [15 minutes]: The facilitator introduces the basics of GPT chatbot development, including designing a chatbot persona, structuring system and instructional prompts, tailoring the chatbot for discipline-specific learning support, embedding pedagogical intent and guardrails, and applying prompt-chaining and workflow thinking; participants then work in groups to build or personalize a GPT chatbot tailored to a specific learning or assessment challenge (using provided templates or starter prompts), experimenting with customizing behaviors, testing outputs, and aligning the chatbot’s functionality with defined learning objectives
• Share-Out [7 minutes]: Each team will share their insights from the activity with the audience.
• Teamwork 2: Applying the TAIWO Framework [20 minutes]: The facilitator will introduce and demonstrate how the TAIWO framework can be used as a practical tool for analyzing student feedback (such as students' mid-semester feedback) with large language models. Participants will work in groups to craft TAIWO-aligned prompts, extract meaningful themes related to student learning experiences, and translate these insights into for improving future course offering.
• Large group share out/Summary/Wrap-Up/Reflection [10 minutes]: The session will conclude with final thoughts, questions, key takeaways, and provide a reflection using an online survey on one action participants will implement as a result of the workshop.

Audience
The primary audience for this workshop includes engineering educators at all levels (undergraduate and graduate programs). Graduate students who serve as teaching assistants or aspire to become faculty are welcome. Faculty interested in curriculum innovation and assessment redesign, as well as educational researchers exploring AI integration in engineering education, are also encouraged to attend.

Speakers

1. Dr. Ibukun Samuel Osunbunmi

   The Pennsylvania State University

   Ibukun Osunbunmi is an Assistant Research Professor and Assessment and Instructional Specialist at Leonhard Center, Pennsylvania State University. He holds a Ph.D. degree in Engineering Education from Utah State University. Also, He has B.Sc. and M.Sc. degrees in Mechanical Engineering. His research interests include emerging technology-enhanced learning (Artificial Intelligence and Virtual Reality), faculty development, student success and engagement, broadening participation in STEM education, evidence-based pedagogy, sustainable energy, and material characterization.

2. Taiwo Raphael Feyijimi

   University of Georgia

   Taiwo is a highly skilled AI Engineer, Researcher, and Doctoral Student at the University of Georgia who completed his MS in Electrical and Computer Engineering in the College of Engineering. He is currently leveraging AI to tackle simple and longstanding problems in engineering education. With over a decade of industry experience as a Technology Strategist and Technical Lead, he has established himself as a forward-thinking innovator in AI and EdTech. His expertise spans Exploratory Data Analysis (EDA), Machine Learning (ML), Natural Language Processing (NLP), and Prompt Engineering Techniques (PETs) with Large Language Models (LLMs). Taiwo is known for his ability to collaborate effectively within and across organizations to meet project goals and drive transformative results. He excels in leading technical teams, offering strategic IT consultations, and implementing solutions that enhance productivity

3. Dr. Yashin Brijmohan

   Utah State University

   Yashin Brijmohan is a registered professional engineer who is currently appointed as Chairman of Engineering Education Standing Technical Committee of the Federation of African Engineering Organizations, Executive committee member of the Commonwealth Engineers Council, Board Member of the UNESCO International Centre for Engineering Education, and Co-Chair of the Africa Asia Pacific Engineering Council. He was the founding Executve Dean of Business, Engineering and Technology at Monash South Africa, former Vice President of the World Federation of Engineering Organizations, and led several committees in the engineering profession. Yashin has both leadership and specialist experience within the engineering power industry and education sectors and is known for his thought leadership in capacity building and engineering education.

4. Bolaji Ruth Bamidele

   Utah State University

   Bolaji Bamidele is currently pursuing her doctorate in the Department of Instructional Technology and Learning Sciences at Utah State University. She holds a BSc and an MSc in Sociology. Her research centers on identity, equity, informal science education, language socialization, and broadening participation in STEM education. Specifically, her work focuses on enhancing the participation and representation of Black girls in STEM by investigating science teaching and learning within counterspaces.