Will Al Elevate Student Achievement and Equity or Will Poorly Executed Al Drive Poorer Outcomes and Wider Academic Gaps?
Posted by anne.grey
Presenters:
Monica Marlo Martinez-Gallagher, Andy Freed, and Anne Grey
Summary
In this interactive roundtable, presenters examine how artificial intelligence impacts student achievement, equity, and institutional practice at Portland Community College. Addressing AI as a socio-technical challenge rather than just a technology issue, the discussion highlights why PCC avoids predictive success analytics, automated plagiarism detectors, and digital proctoring tools due to built-in algorithmic biases. Presenters explore the concept of “backstage learning”—focusing on the student’s thinking, drafting, and revision processes rather than just the final product. The session emphasizes the critical role of human-centered support systems, compassionate intervention, and institutional data governance in keeping AI tools ethical, equitable, and privacy-focused for students and faculty alike.
Presentation Outcomes
- Understand why predictive AI and automated detection tools often amplify historical biases and inequities in higher education.
- Explore strategies for shift-focusing from final assignment “products” to “backstage learning” processes like critical thinking, ideation, and revision.
- Recognize how high-touch, non-punitive human connection and care teams improve student success outcomes over automated technology.
- Learn about PCC’s institutional approach to AI governance, data privacy, and student accessibility.
Post generated by CTLE Web Designer Gem on 08/21/2026