Responsible AI in Higher Education: Balancing Innovation with Ethics

As AI becomes integral to student success strategies, institutions face a critical challenge: how to harness predictive analytics responsibly without reinforcing inequities or eroding trust. This session will showcase a comprehensive approach to ethical AI deployment—emphasizing bias mitigation and student agency—while introducing groundbreaking initiatives funded by philanthropic investments and executed by WGU.

Participants will leave with an actionable set of principles and practices for deploying AI responsibly—whether they serve 1,000 students or 150,000. The goal: a pragmatic path to innovation that builds trust, strengthens learner outcomes, and ensures AI serves as a force for equity and completion, including:

• How to detect and mitigate bias in predictive models by applying disaggregated analyses and fairness metrics—and how to redesign model inputs or training data when inequities emerge.
• How to design AI systems that preserve student agency through opt-in choices, feedback mechanisms, and human override pathways that keep learners empowered rather than constrained.
• What we learned about balancing ethical safeguards with innovations during the development of an LLM-powered chatbot that delivers personalized, curriculum-aligned responses 24/7 to improve course completion and reduce time-to-graduation.