Event Experience

AI can make it easier to generate content while making the deeper work of design more visible. In this opening session, participants will explore how AI is reshaping the goals, relationships, and practices of instructional design. The conversation moves from tool talk toward learning purpose, professional judgment, and the conditions that help people learn and make decisions well.

Upon completion of this session, participants will be able to:

1. Identify key ways AI is reshaping instructional designers’ work, relationships, and decision-making.
2. Distinguish between using AI to produce content and using design judgment to support meaningful learning.
3. Name one practical next step or conversation for their own roles, teams, or institution.

Lance Eaton, Senior Associate Director of AI in Teaching and Learning, Northeastern University

AI makes it increasingly easy for learners to outsource not only tasks, but also reasoning, judgment, and decision-making. This interactive session introduces UnBlooms™, a framework to distinguish productive cognitive offloading from cognitive surrender and to design learning that keeps consequential thinking with the human. Participants will explore how instructional design can shift from optimizing AI-assisted performance toward building durable human capability: what learners can notice, question, explain, defend, and ultimately do without AI.

Upon completion of this session, participants will be able to:

1. Distinguish productive cognitive offloading from cognitive surrender.
2. Apply UnBlooms to identify where AI is extending, versus replacing, learner thinking.
3. Redesign an AI-enabled learning activity to preserve human judgment, metacognition, and independent capability.

Tina Austin, Professor, UCLA

Much of the conversation about AI and assessment has centered on academic integrity: detection, restriction, and suspicion of students. This session invites everyone who designs learning experiences, whether as faculty, instructional designers, or academic leaders, into a different conversation, one that asks whether our assessments measure deep and meaningful learning in the first place. After all, an assessment that AI can easily best should have been rethought long before AI arrived. Grounded in experiential learning theory, metacognitive research, and access-centered design, the session explores what makes an assessment authentic and how AI can support that work, from co-created criteria to formative feedback to evidence of growth over time. The session then turns to the Dimensions of AI Literacies taxonomy, developed through UNESCO IITE-commissioned research spanning six continents, to reveal the AI literacies that educators and designers use and gain through the work of redesign, and that students use and gain by engaging in the resulting authentic assessments. Participants will leave with questions and strategies to carry into the breakout conversation and into their own assessment design.

Upon completion of this session, participants will be able to:

1. Shift the driving question of assessment redesign from how to safeguard existing assessments against AI toward whether those assessments measure deep and meaningful learning.
2. Recognize how AI can support the design of authentic assessment to include co-created criteria, formative feedback, and evidence of growth over time.
3. Use the Dimensions of AI Literacies taxonomy to name the AI literacies that educators and designers use and gain through assessment redesign, and that students use and gain by engaging in authentic assessments.

Angela Gunder, Innovative Pedagogy Specialist, University of Arizona

Generative AI can draft learning outcomes, suggest activities, revise course language, and analyze instructional materials in seconds.

But producing an instructional artifact is not the same as designing for learning.

In this session, the presenter will introduce an (overly) simple way to think about where AI has added value in the design process at the University of Arizona Global Campus: as an accelerator that helps initiate and scaffold work, and as a multiplier that helps interrogate, refine, and strengthen what already exists. From there, we’ll dig into how we think about what that means for human-in-the-lead expertise – everything from institutional intelligence about learners and context to expertise-backed insights into purpose, alignment, and, ultimately, the kind of learning we are actually trying to create. And we’ll consider what happens when some of that production work takes less time: where we can do more, or better, or differently than we’ve been able to do before.

Upon completion of this session, participants will be able to:

1. Distinguish between AI uses that accelerate the initiation of design work and those that help deepen or strengthen existing designs.
2. Identify areas of instructional design work where human judgment, disciplinary context, and learner-centered expertise remain essential.
3. Apply the framework to consider how AI-enabled workflows might redirect instructional design capacity toward higher-value work within their own institutional context.

Nathan Prittsr, Principal AI Strategist, University of Arizona

As generative AI becomes embedded in instructional design practice, its greatest impact is not faster content creation, but rather a fundamental shift in how instructional design work is orchestrated. At the University of Pittsburgh, we are exploring scalable AI-enabled workflows that support the creation of course design documents, quality assurance, copyright review and guidance, and learning-time analysis. Together, these initiatives offer a window into how AI can redistribute work across an instructional design organization and create capacity at scale.

Upon completion of this session, participants will be able to:

1. Identify instructional design activities that can be facilitated through AI-enabled workflows, and distinguish between tasks appropriate for AI assistance and those requiring human judgment.
2. Examine how AI-assisted design, quality assurance, copyright, and learning-time analysis workflows can redistribute instructional design capacity and shift professional roles and responsibilities.
3. Consider how the instructional designer’s role may evolve from artifact production toward critical evaluation, quality stewardship, strategic design, and partnership.

Rae Mancilla, Executive Director of University Digital Education, University of Pittsburgh

As Generative AI streamlines baseline instructional design workflows from initial course outlines to draft assessment items, a central question emerges for leadership: What do we do with the time we save? Rather than simply increasing course volume, instructional design teams have a strategic opportunity to redirect capacity toward high-impact, human-centered work that AI cannot replicate.

In this session, we will explore how instructional design teams are transitioning from content producers to strategic pedagogical partners. We will examine practical frameworks for identifying low-leverage tasks to offload to AI and share models for reallocating that capacity toward deep faculty development, universal design for learning (UDL) initiatives, complex learning analytics, and ethical AI integration. Participants will leave with a strategic mapping exercise to evaluate and pivot their own team’s priorities.

Upon completion of this session, participants will be able to:

1. Identify instructional design tasks that can be responsibly offloaded to AI to free up operational capacity.
2. Evaluate high-impact areas, such as educational development and inclusive design, to redirect team capacity toward maximum institutional value.
3. Draft a preliminary capacity redirection plan to guide job role evolution and workflow priorities within their institution's instructional design team.

Kate Bowersox, Director, Center for Digital Education, Washington University in St. Louis

AI has become part of many instructional designer’s daily practice, not to replace the work of instructional design, but to reduce friction in processes that can slow them down. Used intentionally, AI can create more space for the work at the heart of instructional design: thinking deeply, solving messy problems, experimenting with ideas, collaborating with faculty, and designing meaningful learning experiences.

This session explores how one instructional designer has found a rhythm with AI, including where they are incorporating it into their processes, where they are choosing not to, and how that relationship with AI has changed the way they work. Moving beyond a narrow focus on productivity or doing more with less, the session considers how AI can make room for the creative, relational, and deeply human dimensions of instructional design.

Upon completion of this session, participants will be able to:

1. Identify opportunities to use AI intentionally to reduce friction in instructional design work while preserving human judgment and creativity.
2. Examine how AI-supported workflows can create space for deeper thinking, experimentation, faculty collaboration, and meaningful learning design.
3. Discuss their own answers to the question: If AI can reduce friction in instructional design work, what do we want to make more space for?

Megan Slatton, Instructional Designer, Auburn University

Adapting instruction in the age of AI can be a daunting task for instructors. This talk will present microfellowships as a way to engage instructors in short-term design sprints in which instructors identify and redesign one aspect of their course. By the end of the session, participants will have the opportunity to adapt a microfellowship plan to their individual institutional contexts.

Upon completion of this session, participants will be able to:

1. Identify ways in which AI has impacted instructional design practice.
2. Explore strategies for engaging instructors in microdesign strategies.
3. Apply the microdesign fellowship model to individual institutional contexts.

Alex Rockey, Instructional Technology Instructor, Bakersfield College

Faculty at Harvard Medical School juggle teaching, research, clinical care, and mentoring. A support model built on siloed training and one-to-one consultation could not reach them at the moment of need. The Graduate Education Innovation and Scholarship and Teaching and Learning Technologies teams responded by building a shared Canvas site that curates evidence-based pedagogical and educational technology guidance into modules that span faculty onboarding through AI in teaching and learning. They embedded Tutorbots trained on those same teaching tenets to make the hub interactive and available 24/7. This session uses that build as a case study for how AI redistributes instructional design work. This session will demonstrate a Tutorbot, invite participants to try one, and detail what shifted behind the scenes: consultations the teams no longer field, new work that emerged in curation, guardrail design, response review, and the capabilities that became more valuable. Participants will leave with an adaptable model and a candid account of its tradeoffs.

Upon completion of this session, participants will be able to:

1. Describe a duplicable model for extending faculty development capacity by pairing a curated cross-team resource hub with AI Tutorbots available 24/7.
2. Identify how cross-school and cross-unit collaboration closes the gap between pedagogical and ed tech guidance, and what that structure requires to sustain.
3. Examine how building and maintaining this model redistributed instructional design capacity: which routine consultations were absorbed and what new categories of work emerged.
4. Interact with a Tutorbot trained in evidence-based teaching tenets and consider how a comparable approach could be adapted to participants' own institutional context.

Esther Brandon Kotecha, Associate Director for Teaching and Learning Technologies, Harvard University

Annabelle Royer, Assistant Director of Curriculum Innovation, Harvard University

As generative AI becomes increasingly capable of supporting instructional design work, the question is shifting from “What can GenAI do?” to “How should instructional designers work with it?” Using design thinking as a framework, this session shares three examples from my evolving practice with generative AI, including creating assessments from video- and text-based content and accelerating professional upskilling.

Through these iterative experiments, the presenter will highlight where AI accelerated the design process, where it fell short, and where instructional designer judgment remained essential. The session concludes by exploring how AI may shift instructional designers toward higher-value responsibilities, including strategic design, quality assurance, faculty partnership, prompting practices, and building faculty AI literacy.

Upon completion of this session, participants will be able to:

1. Map potential uses of generative AI across an iterative design process and distinguish between tasks that benefit from AI assistance and decisions that require instructional designers’ judgment and expertise.
2. Identify lessons from iterative experimentation with generative AI to improve evaluation, refinement, and integration of AI-generated outputs into design practices.
3. Reflect on how increased use of generative AI may shift instructional designers’ time and expertise toward strategic design, quality assurance, faculty partnership, prompting, and AI literacy.

Linda A, Director of Client & Instructional Services, University of Pennsylvania

Instructional designers arrive at AI professional development wanting to know which tools to use. That question gets answered fairly quickly, but what surfaces gets harder as we consider which parts of design work are still ours to do and who decides.

Drawing on facilitation of the EDUCAUSE AI for Instructional Designers cohort, this session reports what participants across institutions consistently raise once the tool talk ends. The pattern is not about capability; it is about decision-making and professional judgment. This holds true whether a designer sits in a centralized team or in an academic unit. Participants will leave with a clearer picture of questions their own teams are likely already considering and a way to surface them before scope erodes unintentionally.

Upon completion of this session, participants will be able to:

1. Surface where design judgment is shifting in their organization.
2. Distinguish capability questions from decision-making questions in an ID team's AI conversations.
3. Name the design decisions their team intends to keep before they shift by default.

Joshua Herron, Lead Faculty | Associate Professor, University of Arizona Global Campus

Ask ten institutions to define the role of an instructional designer, and you may receive ten different answers. Designers work across libraries, teaching centers, academic technology units, and other departments with considerable variation in their titles, responsibilities, and decision-making authority. Generative AI is reshaping all of these roles—accelerating content development, course design, and assessment creation while expanding responsibilities related to strategy, AI literacy, and quality assurance.

This session explores a multiyear example of one instructional designer’s evolution from providing tool support to shaping an AI-enabled role. Participants will trace how an internal, cross-unit professional development experiment grew into international collaborations that now inform conference programming. Through grant-funded cohorts, departmental literacy modules, active pilots, and peer learning circles, the session offers a repeatable approach for instructional designers who seek to evolve their own practice: identify where AI literacy is missing, make it manageable, and build capacity, one partnership at a time.

Upon completion of this session, participants will be able to:

1. Examine how one instructional designer's role evolved from tool support toward learning strategy and AI literacy leadership.
2. Identify transferable moves—exercising agency, proposing professional development, and capitalizing on partnerships—that instructional designers can use to redirect their own capacity as AI advances.
3. Consider what emerging practices such as AI literacy advocacy, thought partnership, and cross-institutional collaboration signal about the evolving identity and value of the instructional design profession.

Lauren Kelley, Instructional Designer, University of Delaware

As higher education designers and leaders, we’re navigating complex realities associated with instructional design capacity and possibility. How should AI productively complement a team’s design processes and products? And how might we guide staff-focused change efforts to cultivate new AI-enabled skills and opportunities? This session describes how Duke’s Center for Teaching and Learning embraced these questions and built an internal professional learning initiative to address both technical and organizational capacity. This initiative has guided and sustained a multi-team, cross-functional cohort to cultivate new skills and dispositions as designers utilize AI to identify challenges, reimagine workflows, deploy prototypes, and iterate solutions.

Upon completion of this session, participants will be able to:

1. Describe a professional development program that guided a cohort to explore agentic AI features, identify workflow challenges, and deploy new prototypes.
2. Identify the human and technical capabilities of a capacity-building program, including AI-assisted development, deployment, critique, and iteration.
3. Evaluate how a build-and-iterate culture can shape new roles, identities, and capacities within a center’s instructional design staff.

Remi Kalir, Associate Director, Center for Applied Research and Design in Transformative Education, Duke University