SIDLIT 2026

As generative AI becomes increasingly embedded in online and blended education, instructors face a difficult question: how can learning be supported and assessed when students can quickly generate polished answers, explanations, lesson materials, and multimedia products? Much of the current conversation focuses on academic integrity, prompt writing, tool adoption, or AI-use policies. While these issues are important, they do not fully address a deeper instructional question: what does meaningful learning look like when AI-generated output becomes part of the learning process? This work-in-progress session begins with a dialectical autoethnographic inquiry into a 24-turn interaction between the presenter and a generative AI system. Although the interaction began with a personally meaningful problem related to IRA planning, the focus of the analysis is not financial decision-making. Rather, the episode is used as a situated case for examining how learning unfolds within a human–AI–artifact system. Preliminary analysis suggests that AI-generated outputs should not be treated as final answers, neutral tools, or authoritative explanations. Instead, they function as epistemically unstable learning materials that require human verification, revision, justification, and synthesis. Building from this analysis, the session translates the emerging theoretical insight into practical strategies for online and blended teaching. The presenter will introduce assignment and assessment structures that foreground process evidence rather than only final products, including prompt archives, revision logs, delta reports, AI feedback loops, AI defense activities, peer process audits, and reflective synthesis prompts. These strategies are designed to help instructors evaluate how students define problems, examine AI-generated output, verify information, revise their thinking, justify decisions, and produce a defensible final synthesis. Participants will leave with a conceptual vocabulary for understanding generative AI-mediated learning and a set of adaptable strategies for designing assignments that make student judgment visible. The session is intended for educators, instructional technologists, online program leaders, and educational technology researchers interested in moving beyond AI-use compliance toward more rigorous, reflective, and assessable forms of AI-supported learning.

Generative artificial intelligence (GenAI) avatars—digital representations of human instructors driven by synthesized speech, natural language processing, and advanced kinematic video generation—represent a structural shift in how asynchronous educational content is designed, delivered, and consumed. This session will provide introductory material for the design, implementation and sources for generating these technologies in higher ed.

This session explores how NotebookLM can transform both studying and content delivery in higher education. Participants will see a demonstration of how NotebookLM can be used to create and manage Open Educational Resources (OER). The session will also highlight its value as a student learning tool, including features such as interactive chat, AI-generated podcasts, quizzes, slides, flashcards, infographics, study guides, and mind maps. Because NotebookLM is grounded in user-provided materials, it offers a reliable and focused AI experience for both instructors and students.

Canvas is packed with powerful features that can save instructors time, improve course quality, and enhance the student experience, yet many of these tools remain underutilized. In this session, participants will explore practical Canvas tips and hidden gems, including targeted messaging in the Gradebook, content recovery tools, accessibility features, page design enhancements, and AI-supported workflows. Attendees will leave with actionable strategies to streamline course management, create more accessible and engaging content, and make better use of Canvas’s built-in capabilities.
Participants are recommended to attend with a laptop and try these tips during the presentation. 

High engagement doesn’t come from expensive tools; it comes from intentional design. In this session, we’ll share how our nonprofit team builds highly engaging Canvas professional development for adult learners ages 14–70+ by thoughtfully selecting and layering the right tools for the right purpose. 
You’ll see real examples of how we use Canvas with interactive slides, flip cards, embedded activities, and structured discussions; alongside aligned objectives, reflection, and knowledge checks; to create meaningful learning experiences. More importantly, we’ll unpack the “why” behind each choice: how we match tools to learning goals, cognitive load, application, and desired levels of interaction. 
Leave with practical engagement strategies and tools that are purposeful, scalable, and budget-conscious; no premium integrations required. 

During our inaugural year in partnership with the Center on Rural Innovation’s Artificial Intelligence Consortium (CORI-AI Consortium), Emporia State University (ESU) engaged in a collaborative, community-centered initiative to advance AI literacy and institutional capacity within a rural context. As part of the “Higher Ed AI Consortiums in Rural America” project, supported by Microsoft and LinkedIn, ESU partnered with Emporia Main Street and the Emporia Public Library to design and implement a scalable and sustainable framework for AI education delivery. What transpired from this partnership was the development of a flexible “template app” that supports the delivery of repeatable workshops across diverse audiences. Grounded in a train-the-trainer framework and a growing community of practice, the model enables facilitators to maintain consistency while adapting content to local needs. The resulting model offers implications for broader adoption, contributing to workforce readiness and regional economic development in increasingly AI-integrated landscapes. This presentation shares both the process and the outcomes of this first year, highlighting lessons learned at the intersection of community engagement and the push for equitable AI understanding, usage, and exploration.