Center for Teaching and Learning Excellence

AI

Did They or Didn’t They?: The RWC’s Perspective on Artificial Intelligence in an Age of Anxiety
The Reading and Writing Center (RWC) addresses the impact of AI on teaching and evaluation. Rather than fostering a culture of blame, this session offers practical strategies to safeguard authentic student voice, support cognitive development, and strengthen classroom belonging in the age of AI. Access presentation slides and video to learn more. Posted January 31, 2026
A screenshot of Paul Montone's Anderson Conference 2026 presentation, "What Works Well in Online Teaching: Building AI Best Practices Together" What Works Well in Online Teaching: Building AI Best Practices Together
Learn about the latest updates to PCC's "What Works Well in Online Teaching" resource, featuring strategies and collaborative faculty guidance for integrating ethical AI practices into online courses. Posted January 31, 2026
The first slide of the Anderson Conference 2026 presentation, "Artificial Intelligence and Natural Stupidity" Artificial Intelligence and Natural Stupidity
Juliet Pursell and Gale Czerski explore the ethics and dangers of artificial intelligence, focusing on its environmental footprint, creative copyright concerns, and internal biases. Posted January 31, 2026
Beyond Decorative: Chicanx & Latinx Communities as AI Prompts
Generative AI often flattens Chicanx and Latinx identities into narrow stereotypes, erases diverse populations, and commercializes sacred traditions like Day of the Dead Ofrendas. Meanwhile, algorithms frequently censor social justice content. To counter this, presenters advocate for critical AI literacy, ethical tech development, and community-grounded pedagogical practices in higher education. Posted January 30, 2026
Screenshot of the Anderson Conference 2026 presentation, "Designing Your Syllabus Al Policy: A Cross-Disciplinary Workshop" Designing Your Syllabus Al Policy: A Cross-Disciplinary Workshop
Marc Goodman’s workshop guides educators in crafting discipline-specific syllabus AI policies. Rather than using unreliable detection tools, faculty explore 200+ real-world examples to establish transparent expectations. The session helps instructors evaluate permission levels, integrate ethical guidelines, and teach students how to responsibly cite, document, and reflect on their AI tool usage. Posted January 30, 2026
Screenshot of the Anderson Conference 2026 presentation "Leveraging a Framework for Effective & Equitable Teaching in an Al World" Leveraging a Framework for Effective & Equitable Teaching in an Al World
In this presentation, PCC Biology Instructor Josephine Pino explores how generative AI impacts flipped classrooms and course design. Grounded in NASEM’s 7 Principles of Effective and Equitable Teaching, she shares practical strategies to redesign assessments, prevent cheating, foster academic integrity, and support Universal Design for Learning without relying on policing technology. Posted January 30, 2026
Screenshot of Marc Goodman's Anderson Conference 2026 presentation, "What Every Student Needs to Know About Al" Beyond Bias: What Every Student Needs to Know About Al
In this presentation, CIS Department Chair Marc Goodman examines how AI engineering, tokenization, and engagement-driven architectures shape model accuracy. Moving beyond basic data bias, he breaks down hallucinations, retrieval-augmented generation (RAG), and reinforcement learning. Goodman reveals why chatbots prioritize persuasion over objective truth and shares practical strategies to teach critical AI fact-checking skills. Posted January 30, 2026
Screenshot of the Anderson Conference 2026 presentation, "Will Al Elevate Student Achievement and Equity or Will Poorly Executed Al Drive Poorer Outcomes and Wider Academic Gaps?" Will Al Elevate Student Achievement and Equity or Will Poorly Executed Al Drive Poorer Outcomes and Wider Academic Gaps?
In an interactive roundtable, Portland Community College presenters explore how AI impacts student achievement, equity, and practice. Treating AI as a socio-technical issue, PCC rejects automated proctoring and plagiarism detection to avoid algorithmic bias. The session emphasizes "backstage learning"—valuing process over final products—alongside compassionate human care teams, data privacy, and ethical institutional governance. Posted January 30, 2026
Al and Copyright: Legal Realities and Ethical Questions
Cascade Faculty Librarian Rachel Bridgewater examines the intersection of copyright law and generative AI across four key questions, including output eligibility and training fair use. Highlighting case law, she explains why copyright law often falls short of addressing ethical concerns and why real solutions depend on licensing, labor regulations, and ethical standards. Posted January 30, 2026
Higher (order) Education: The Role of Colleges in Teaching the Cyborg Student
As AI evolves into reasoning agents, higher education must reframe technology from an external threat to an extension of student cognition. While AI simplifies synthesis, it risks superficial "informational collages." Consequently, colleges must shift focus toward teaching epistemic hygiene, metacognition, source validation, and complex project design rather than policing tool usage. Posted January 30, 2026