Symposium

This session explores the transformative impact of AI in higher education, focusing on responsible adoption, strategic priorities and practical applications. It highlights key trends, challenges and opportunities for institutions, educators, and students while emphasizing ethical considerations and future-proofing skills. 

Presenter: Dustin Lange

The accelerating influence of artificial intelligence in education presents both urgency and opportunity. As institutions grapple with how to prepare students for an AI-augmented world, traditional models of teaching and assessment must evolve. This white paper introduces a reframed version of Bloom’s Taxonomy, updated for the age of AI, that emphasizes identity formation, interdisciplinary synthesis and real-world impact. Through this lens, educators can shift from fear to framework, leveraging AI not as a threat, but as a tool for deeper, more human learning. Anchored in pedagogical theory, the framework provides a practical, ethical path forward for institutions seeking to lead in the era of AI. This is not just a framework -- it is a blueprint for empowering educators and students to thrive in a future that is already here.  

Presenter: Lisa Clark

This session explores the development of a music application built using Google’s Antigravity framework, focusing on how generative AI supported ideation, prototyping and core functionality. Rather than centering on novelty, the presentation walks through concrete design decisions, technical constraints and lessons learned while collaborating with AI as a development partner. Attendees will leave with a grounded understanding of where AI meaningfully accelerated the work, and where human judgment remained essential.  

Presenter: Dr. Birch Browning

The Assignment Design Café invites participants to bring a current assignment and collaboratively rethink it in light of generative AI. Facilitators from the CSU Center for Faculty Excellence will guide small-group discussion focused on redesigning assignments to emphasize process, judgment and authentic student engagement rather than AI avoidance. Attendees will leave with concrete ideas — and in many cases a revised assignment — better aligned with teaching and learning in an AI-enabled context.

Presenters: Dr. Shelley Rose, Dr. Molly Buckley Marudas  

In this candid student panel, learners share how they’re actually using AI in their classes — from homework help and study tools to writing support and creative projects. Hear firsthand what’s working, what’s not and how students are thinking about AI in their academic lives. This discussion offers a grounded look at the real role of AI in the student experience.

Presenter: Dr. Xiongyi Liu 

This session explores how scholars can thoughtfully integrate AI across the research lifecycle, from idea generation and research question development to literature review and analysis, while maintaining human judgment, rigor and ethical responsibility. Attendees will leave with practical strategies and ethical frameworks for using AI to support innovative, methodologically sound research.  

Presenters: Dr. Becky Odom Bartel, Dr. Anup Kumar, Dr. Claire Hughes 

Can people tell the difference between AI-generated images and human-made images? What makes it different? This session invites participants to examine how perception, authorship, intention and context shape our judgments of photographic authenticity in an era where AI blurs the boundaries between seeing, making and meaning. 

Presenter: Mark Slankard

AI is more than ChatGPT, Copilot, Claude, Gemini, Perplexity, etc. A whole lot more. In just 30 minutes, you’ll see 30 AI applications that can immediately improve how you research, create, analyze and work. This fast-paced session cuts through the hype and shows you practical tools you can start using the same day.  

Presenter: John Zito 

Drawing on interdisciplinary perspectives from communication and education, this session reports findings from a national survey of U.S. students and instructors on adoption, utility and beliefs of generative AI in learning and teaching. Presenters will explore how GenAI is being used, how ethical boundaries are perceived and whether these trends suggest a new digital divide.  

Presenters: Dr. Karla Hamlen Mansour, Dr. Selma Koc, Dr. Anup Kumar, Dr. Richard Perloff, Allyson Lindsley

Generative AI shifts AI from merely analyzing existing data to creating new content, ideas and solutions. It is the topic nowadays considered by many to be more significant than the internet or electricity. This presentation will describe the underlying design of GenAI software tools and differentiate them. Ethical aspects of GenAI will also be addressed.  

Presenter: Dr. Stefan Andrei

As AI has become a ubiquitous buzzword across society, there is increasing demand for AI tools in climate action - from accelerating global climate models to assessing impacts on agriculture. In this talk, we will consider how AI can be a meaningful tool in tackling climate change, and what kinds of AI innovations will help achieve these impacts. We will also discuss ways in which AI is making climate change worse, and how to align the development of AI more broadly with climate action. David Rolnick is an Assistant Professor and Canada CIFAR AI Chair in the School of Computer Science at McGill University and at Mila – Quebec AI Institute. He is a Co-founder and Chair of Climate Change AI and serves as Scientific Co-director of Sustainability in the Digital Age and co-lead of the Global Center on AI and Biodiversity Change (ABC). Dr. Rolnick is a Sloan Research Fellow and an AI2050 Early Career Fellow and was named to the MIT Technology Review’s 2021 list of “35 Innovators Under 35” for his work in building the field of AI and climate change. He received his Ph.D. in Applied Mathematics from MIT and is a former Fulbright Scholar, NSF Graduate Research Fellow and NSF Mathematical Sciences Postdoctoral Research Fellow. 

Presenter: David Rolnick

Federated learning is a decentralized machine learning approach that consists of servers and clients. It protects data privacy during model training by keeping the training data locally in each client. However, the requirement for the server and clients to frequently synchronize the parameters of the model brings a heavy burden to the communication links, especially when the model size has grown drastically in recent years. Several methods have been proposed to compress the model size by sparsification to reduce the communication overhead, albeit with significant accuracy degradation. In this work, we propose methods to better trade-off between model accuracy and training efficiency in federated learning. 

Presenter: Dr. Tianyun Zhang 

This panel brings together First-Year Writing instructors from the same institution and program with diverse perspectives on GenAI. Panelists will share classroom practices, policies and assignments while inviting audience discussion on how GenAI is reshaping writing instruction and what thoughtful, intentional engagement with this technology can look like.

Presenters: Dr. John Brentar, Dr. Yvonne Bruce, Dr. Joe Kane, Dr. Amanda Lloyd, Dr. Rachel Rickel

Understanding when AI is appropriate can be one of the biggest challenges in building AI literacy. Because expectations vary widely across courses and contexts, there’s no single rule book for when AI supports learning, or when it crosses the line into plagiarism or undermines research skills. Scenario-based activities can help surface these tensions and spark productive discussion. In this session, we’ll explore sample scenarios, practice using them in small groups and consider how they can clarify expectations in the classroom. No AI use is required to participate.  

Presenters: Kathy Fisher, Mandi Goodsett, Theresa Nawalaniec