Event Experience

As generative AI becomes a constant presence in higher education, the question for faculty is no longer whether students can use AI, but how assessment can still produce trustworthy evidence of learning. This session reframes assessment design through an AI-aware lens that prioritizes learning intent, cognitive work, and disciplinary judgment rather than tool policing or detection strategies. Participants will explore how to distinguish between the process and product of learning, identify when AI meaningfully supports understanding versus when it undermines it, and design assignments that remain valid even in an AI-rich environment. Using concrete frameworks, redesign strategies, and student-facing communication tools, this session provides faculty with practical ways to clarify expectations, protect essential evidence of learning, and align grading with what truly matters. The focus is not on restricting technology, but on designing assessments where learning is visible, decision-making is intentional, and faculty judgment remains central.

Jean Mandernach, Executive Director, Center for Innovation in Research and Teaching, Grand Canyon University

This session outlines our S.A.F.E.R. (Standards-Anchored, Faculty-Led, Evidence-Producing Redesign) model. Rather than focusing on "AI literacy" or policing, this framework treats GenAI as "cognitive infrastructure." It offers institutions a six-phase roadmap to transition faculty from the exhausting role of "AI detectives" to designers of learning experiences where student thinking is visible. It specifically addresses the "wicked problem" of assessment breakdown by aligning pedagogical redesign through Design-Based Research.

Elizabeth McAlpin , Director, Educational Technology Research, New York University

Podcasting offers a dynamic, easily distributable platform for students to share research findings, conduct expert interviews, and synthesize complex ideas into widely available audio stories. This flash talk explores an interdisciplinary approach to teaching podcasting to students in industrial engineering, communication and journalism, history (technology and civilization), and animal science. By integrating podcast creation into coursework, students engage more deeply with subject matter while developing vital presentation and storytelling skills that enhance their career readiness.

We utilize tools such as Adobe Podcast and Adobe Audition, which feature AI-enhanced audio tools that help students and researchers achieve studio-quality sound regardless of their recording environment. These skills are transferable to a variety of platforms, including many freely available options. To further support creative expression and professional presentation, Adobe Express is used to design podcast cover art and provide access to AI-generated imagery through Adobe Firefly, an image-creation tool that is responsibly sourced and commercially safe. The podcast format empowers students to showcase their creative talents and build audio artifacts for professional portfolios, making their work both shareable and impactful beyond the classroom.

Chelsy Hooper, Instructional Technology Coordinator, Auburn University

Given faculty concerns about AI-assisted or even wholly-authored student papers, faculty could use a simple feature in any Google document, spreadsheet, or presentation slide: its version history, which includes a summary or detailed date and time stamp of every addition or edit. As such, the goal is not to detect the presence of AI in student writing, which is currently very difficult due to “false positives,” but more to detect the absence of originality.

John Fritz, Associate Vice President, Instructional Technology, University of Maryland, Baltimore County

Generative AI has fundamentally altered where learning begins. When assessment strategies remain unchanged, AI can shrink the time and productive struggle historically required to produce solutions. In computer science courses, this presents a significant challenge: students can generate functional software almost instantly, bypassing the iterative thinking that builds durable understanding. Faculty responses have often focused on restricting or policing AI use, which can unintentionally cultivate distrust rather than learning.

To address this shift, a second-semester Java programming course at Waukesha County Technical College was redesigned to explicitly incorporate AI as a structured collaborator rather than to prohibit its use. Traditional “requirements-met” projects were transformed into a three-phase iterative model using version control. This emerging model suggests that when AI becomes the starting point rather than the endpoint, assessment can evolve to preserve rigor while expanding opportunity for deeper learning.

Brittney Schultz, Instructor, Web & Software Developer, Waukesha County Technical College

Higher education assessment systems largely assume equal starting points, stable learning trajectories, and performance as a proxy for understanding. These assumptions break down in practice, especially in large, diverse, and AI-mediated learning environments. As generative AI makes process, iteration, and sensemaking increasingly visible, it also exposes a deeper mismatch between how humans actually learn and how we typically assess them.

This session introduces a reframing of assessment from performance-based aggregation to growth-oriented, additive learning models that emphasize judgment, practice, and feedback over time. Drawing on cognitive science, organizational learning, and emerging AI-enabled instructional practices, we will examine why traditional grading structures systematically obscure learning progress and how instructors can redesign assessments to capture development without increasing workload or sacrificing rigor.

The session is intentionally split between theory and application. Participants will leave with a practical, adaptable assessment framework and a ready-to-use handout that helps instructors redesign one existing assignment to surface learning growth, decision quality, and reflective judgment. Examples will span large enrollment courses, team-based projects, and AI-supported learning activities. The goal is not to replace grades, but to make learning visible in ways that better align with how students actually grow.

Wendy Fritz, Executive Director, Teaching & Learning, Wisconsin School of Business, University of Wisconsin - Madison

Grading, as a practice of providing summative assessment of student work, only emerged as a cornerstone of education over the past hundred years or so. Yet it has entrenched itself so deeply into bureaucratic structures that it is extremely difficult to dismantle and remove. AI can either exacerbate and further reinforce grading as a fundamental aspect of education or it can help facilitate alternative approaches to evaluating the process of learning, growth, and achievement. This session will analyze and expose the history of grading to hopefully help illuminate pathways to undo and reimagine what assessment, learning, and education actually are, and should be, in this era of technological transformation.

Leif Nelson , Executive Director of Learning Technology Solutions and Research Computing, Boise State University

Generative AI has changed the conditions under which students learn, create, and demonstrate mastery. In response, many institutions have turned first to AI detection or restrictive policies. However, as the EDUCAUSE community increasingly recognizes, surveillance-based approaches alone can undermine trust, limit innovation, and miss the larger opportunity to redesign assessment around transparency, student agency, and authentic learning.

In this session, Apporto and a higher education partner institution will facilitate an open conversation about how campuses can move from a policing mindset to a partnership model for AI-enabled assessment. We will explore how academic and technology leaders can work together to support responsible AI use across the full learning life cycle: setting expectations, guiding students as they create work, making the learning process more transparent, supporting faculty feedback and grading, and delivering secure assessment environments when needed.

The discussion will focus on practical institutional questions: How do we help students use AI without outsourcing learning? How do we give faculty visibility without increasing their burden? How do we align course-level AI expectations with exams and assessments? And how do we avoid a patchwork of disconnected tools and policies in favor of a more coherent, institution-wide approach?

Participants will leave with a clearer framework for thinking about AI-aware assessment as a coordinated ecosystem, one that connects pedagogy, integrity, technology, and student success.

Antony Awaida, CEO, Apporto

Bao Johri, Vice President for IT and CIO, California State University, Fresno

Lenore Goldberg, Dean - Colleges and Curriculum, DeVry University - Lisle

Generative AI (GenAI) is fundamentally shifting the landscape of computing practice and education. The automation of code generation, data analysis, and technical writing necessitates a critical pivot: from assessing surface-level outputs to evaluating authentic learning processes like critical thinking, ethical reasoning, and creative problem-solving.

This project is currently in its early stages, and I am excited to be developing a framework to address this urgent need. The work will investigate how diverse computing and informatics faculty are redesigning assignments and assessments to foster authentic learning in a GenAI-integrated environment. The envisioned outcome is a field-tested, open-access "Authentic Assessment with AI" Toolkit: A high-impact community resource featuring exemplary assignments, adaptable rubrics, and essential policy language. I look forward to sharing emerging insights and early findings as this work takes shape.

Akesha Horton , Director of Academic Engagement and Learning, Indiana University

This session focuses on walking instructors through rethinking assignment design and learning goals in light of AI’s impact; setting specific parameters for AI use; and establishing clear assessment expectations with a rubric.

Using Fink's Taxonomy of Significant Learning, instructors reflect on how AI does (or does not) affect the development of essential professional and personal competencies in their disciplines and students’ future workplaces. They will then learn how to revise a course assignment rubric to clearly articulate AI use with learning objectives that foster the development of career-ready skills as well as human dimensions such as curiosity, empathy, and connection with community. Participants are encouraged to share materials generated from the workshop in a shared creative common document.

Sally PW Wu, Director, Instructional Design & Media Production, UCLA

Kevin Chan, Associate Instructional Designer, UCLA