Summary

The Reading and Writing Center (RWC) explores how artificial intelligence is changing how we teach and review student work. This session highlights practical strategies that help educators avoid a culture of blame when dealing with AI. Instead, the focus shifts to protecting the student’s unique voice, supporting their mental growth, and ensuring they feel they belong in the classroom community.

Presentation Slides:

Presentation Outcomes

• Understand how AI impacts teaching methods and the assessment of student writing.
• Identify strategies to shift away from blaming students when navigating AI in the classroom.
• Learn how to protect and encourage an authentic student voice in assignments.
• Discover ways to support cognitive development and foster a stronger sense of student belonging.

Post generated by CTLE Web Designer Gem on 08/14/2026

Summary

This session introduces recent updates to °µÍřTV’s “What Works Well in Online Teaching” web resource, which offers structured guidance for both new and experienced online instructors. Originally starting as a printed booklet in 2015, the project has evolved into a dynamic online hub built from faculty input and focus groups. The latest enhancements expand its coverage beyond unscheduled online courses to include web courses with scheduled meetings, while integrating guidance on the ethical and meaningful use of Artificial Intelligence (AI) in higher education.

A key focus of the presentation highlights an AI training module developed by PCC faculty member Debbie Austin. This resource provides faculty-facing strategies to AI-proof assessments and student-facing materials covering academic integrity, ethical concerns, and practical AI boundaries. During the interactive portion of the session, participants explored the updated resource website and engaged in small-group brainstorming to share strategies for integrating AI into core categories such as teaching materials, community building, communication, providing feedback, and administrative procedures.

Presentation Outcomes

• Explore recent updates to PCC’s “What Works Well in Online Teaching” web resource, including expanded coverage for courses with scheduled online meetings.
• Discover faculty-facing and student-facing AI training modules designed to promote ethical, meaningful AI usage and clarify academic expectations.
• Identify actionable, high-impact teaching practices for incorporating AI into online course design, assessment, and student communication.
• Collaborate with peer faculty to harvest insights and strategies that inform future updates to PCC’s online teaching resources.

Post generated by CTLE Web Designer Gem on 08/21/2026

Summary:

In this presentation, Juliet Pursell and Gale Czerski examine the critical risks and ethical concerns surrounding the rapid expansion of artificial intelligence tools. The discussion highlights three major areas of impact: the significant environmental footprint of data centers (including massive energy and water consumption), the intellectual property and creative concerns regarding AI-generated art and plagiarism, and the inherent system flaws of current generative models. By exploring issues such as AI hallucinations, embedded societal biases like racism and sexism, and potential user manipulation, the presenters encourage educators to critically evaluate how and when AI tools are used.

Presentation Outcomes:

• Identify the environmental impacts associated with running and maintaining large-scale AI data centers.
• Understand the ethical implications of AI training models on creative industries, art, and intellectual property.
• Recognize system limitations and risks in AI output, including hallucinations, bias, racism, and sexism.
• Critically evaluate the ethical dimensions of AI adoption in higher education environments.

Post generated by CTLE Web Designer Gem on 08/21/2026

Summary

This panel presentation explores the critical intersections of generative AI, representation, and cultural equity within Chicanx and Latinx communities. Dr. Jonathan Ortiz discusses how image-based AI flattens diverse identities into narrow stereotypes, erasing Afro-Latinx, Indigenous, and Asian-Latinx populations due to a lack of backend developer diversity. Magali Sánchez highlights how artificial intelligence language models output incomplete narratives and how algorithms frequently flag or suppress social justice content. Dr. Veronica Sandoval examines how AI image generation commercializes and displaces sacred cultural traditions, such as Day of the Dead altars (Ofrendas), while advocating for a sacred, community-grounded approach to classroom pedagogy. Together, the presenters offer reflections on critical AI literacy, equitable technical futures, and alternative practices for educators.

Presentation Outcomes

• Analyze how generative AI models flatten complex Chicanx and Latinx identities into narrow visual and narrative stereotypes.
• Understand the mechanisms behind algorithmic bias, web scraping, and censorship of social justice content in digital spaces.
• Evaluate the cultural disruption caused by replacing handmade sacred traditions, such as DĂ­a de los Muertos Ofrendas, with synthetic AI generation.
• Develop culturally responsive pedagogical practices that center student identity, equity, and ethical technology use in higher education.

Keywords: Generative AI, Chicanx Studies, Latinx Studies, Algorithmic Bias, Artificial Intelligence in Higher Education, Digital Literacy, Equity and Inclusion, Cultural Traditions, Ofrendas, Culturally Responsive Pedagogy

Post generated by CTLE Web Designer Gem on 08/21/2026

Summary

In this cross-disciplinary workshop, Marc Goodman explores how educators can develop clear, meaningful syllabus AI policies tailored to their teaching philosophies, course goals, and specific academic disciplines. With artificial intelligence becoming an essential skill in some fields and an academic integrity concern in others, instructors need guidance on how to set boundaries without relying on unreliable AI detection tools. The presentation introduces a prototype search tool designed to help faculty analyze over 200 real-world syllabus policies across various levels of permission, student responsibilities, and documentation requirements. Participants learn how to craft transparent policies that encourage critical thinking, address privacy and bias, and set clear expectations for ethical AI use in the classroom.

Learning Outcomes

After reviewing this workshop, faculty will be able to:

• Identify appropriate artificial intelligence uses and limitations specific to their discipline.
• Evaluate different permission levels for syllabus AI policies, ranging from required use to complete restriction.
• Incorporate key policy dimensions into course syllabi, including student responsibilities, required documentation, and ethical warnings.
• Understand the limitations of AI detection software and implement effective, observation-based assessment strategies.
• Guide students on how to responsibly document, cite, and reflect on their use of AI tools.

Post generated by CTLE Web Designer Gem on 08/21/2026

Summary

In this presentation, PCC Biology Instructor Josephine Pino shares how the rapid rise of AI tools changed her flipped classroom approach and forced a redesign of course expectations. Grounded in research from the National Academies of Sciences, Engineering, and Medicine (NASEM), she explores the 7 Principles of Effective and Equitable Teaching. She addresses common challenges like online cheating, AI-generated assignments, and student confusion about open-note policies. Rather than policing technology, Pino demonstrates how to adapt course design to focus on transparent expectations, student self-regulation, and authentic assessment. She provides practical tools for integrating AI safely while supporting Universal Design for Learning (UDL) and accessibility across all disciplines.

Presentation Outcomes

• Understand how the emergence of generative AI impacts traditional flipped classroom formats and student learning behaviors.
• Explore the 7 Principles of Effective and Equitable Teaching to guide course design and lesson planning.
• Apply practical strategies for teaching academic integrity, ethical AI usage, and self-regulated learning skills.
• Redesign assessments to move beyond basic fact recall and focus on critical thinking, process, and application.

Post generated by CTLE Web Designer Gem on 08/21/2026

Executive Summary:

In this presentation, Computer Information Systems Department Chair Marc Goodman moves beyond basic data bias to examine how engineering and architectural choices shape modern artificial intelligence. He explains the technical mechanics behind AI hallucinations, retrieval-augmented generation (RAG), and reinforcement learning from human feedback. Through real-world examples—such as biased recruitment models and persuasion-driven chatbots like Grok—Goodman shows how modern AI systems are often tuned for engagement and rhetoric rather than objective truth. He outlines practical classroom strategies to help students spot deceptive tactics, verify source claims, and build vital critical thinking skills in an AI-driven world.

Presentation Outcomes:

• Identify the underlying causes of systemic bias and hallucinations in deep learning models.
• Evaluate how architectural choices like tokenization, context limits, and feedback loops impact AI accuracy.
• Recognize common rhetorical tactics and deceptive argumentation strategies used by engagement-tuned chatbots.
• Implement classroom detection assignments to teach students how to fact-check AI outputs against primary sources.

Post generated by CTLE Web Designer Gem on 08/21/2026

Summary

In this interactive roundtable, presenters examine how artificial intelligence impacts student achievement, equity, and institutional practice at °µÍřTV. 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

Summary

In this presentation, Cascade Faculty Librarian Rachel Bridgewater explores the complex intersection of copyright law and generative artificial intelligence. She examines four key legal questions: whether AI outputs can be copyrighted, if generated content infringes on existing works, whether training models on copyrighted data constitutes fair use, and how feeding material into AI impacts copyright. Through recent case law, Bridgewater demonstrates why copyright law is often an imperfect tool for addressing ethical concerns around AI and explains why meaningful solutions will likely rely on ethical norms, licensing frameworks, and broader labor regulations.

Presentation Outcomes

• Understand why AI-generated outputs are currently ineligible for copyright protection under U.S. law.
• Distinguish between legal copyright infringement and ethical concerns regarding generative AI training models.
• Analyze how fair use legal precedents apply to text and data mining in large language models.
• Recognize the practical limitations of copyright law for protecting creators and explore alternative regulatory solutions.

Post generated by CTLE Web Designer Gem on 08/21/2026

Summary

As artificial intelligence tools and autonomous agents rapidly advance beyond simple text generators, higher education faces a shift in how students learn and complete work. In this presentation, Clarissa Littler argues that rather than focusing on how to prevent AI usage or returning to traditional blue-book exams, colleges must reframe their role. Using the philosophical framework of the “Extended Mind Thesis,” AI is presented not as an external authority, but as an extension of student cognition—a collaborative tool for hybrid thinking.

While AI tools can assist with research, coding, and problem-solving, they also make it easier for students to create “informational collages”—stitching together sources without truly understanding the evidence. Because of this, higher education remains essential for teaching epistemic hygiene, metacognition, and interdisciplinary context. Educators are encouraged to move away from treating students as adversaries and instead guide them through ambitious projects, source validation, and personal knowledge management.

Presentation Slides:

Presentation Outcomes

• Understand the distinction between simple text generation and modern AI agents capable of reasoning, tool use, and multi-step task execution.
• Apply the “Extended Mind Thesis” framework to view AI tools as extensions of human cognition rather than replacement sources of knowledge.
• Identify the risks of “informational collage” and explain why teaching epistemic hygiene and metacognition is crucial in AI-supported learning.
• Design teaching strategies—such as source verification passes, process journaling, and open-ended “impossible assignments”—that support meaningful learning in an AI environment.

Post generated by CTLE Web Designer Gem on 08/21/2026