Ojobo Agbo Eje
Presentation Title: Responsible AI for Online Learning at Institutional Scale
Artificial intelligence is now in online and hybrid classrooms. Yet most institutions have no unified framework for deploying it in ways that protect learning quality, academic integrity, equity, and student trust. Faculty and administrators are being asked to adopt AI tools without clear guidance on governance, ethics, or long-term impact.
This session will present a practical Responsible AI framework, drawing on institutional research, emerging regulatory standards, and real-world academic use cases. Participants will learn how universities can move toward intentional, scalable, and accountable AI adoption.
The session will address three core questions facing online learning leaders:
Where should AI be used in teaching and learning?
Participants will explore high-impact use cases, with attention to how these differ in online, hybrid, and in-person environments.
What are the risks-and how do we manage them?
The session will review academic integrity, algorithmic bias, data privacy, accessibility, and faculty workload, and introduce a risk-based governance model that institutions can apply when selecting and deploying AI tools.
How can institutions operationalize Responsible AI?
Participants will be introduced to a step-by-step institutional framework that integrates AI into learning design, faculty development, and student support while maintaining compliance, transparency, and equity.
Attendees will go back with:
- A Responsible AI evaluation checklist for online learning tools
- A model for AI use
- Examples of how students and faculty can be engaged as partners in ethical AI adoption
This session is designed for faculty, instructional designers, administrators, and learning technology leaders who aim to transition from uncertainty and experimentation to confident, responsible, and institution-wide adoption of AI.
Bio
Ojobo Agbo Eje is a professional with over a decade of expertise working at the intersection of technology, management consulting, and human behavior. He is presently leading Responsible AI innovation, adoption, and literacy in New Jersey, USA. He obtained a Master's degree in Data Science from Rutgers University, USA, before which he had earned an MBA, and a Bachelor's degree in Engineering. His recent work involves AI Adoption readiness, Explainable AI, driving AI literacy, and leveraging responsible AI innovation for profitability. He has achieved this through published research, the development of frameworks, and consulting work with organizations. He was the co-lead on the ‘U.S. States AI Readiness Index’ project, and also the Author of the book, 'A Layman’s Guide to Understanding Artificial Intelligence.'