Chicago eLearning & Technology Showcase 2026

Most L&D teams still respond to stakeholder requests by building courses. The real problem is never “we need training”—it’s an undiagnosed performance gap hiding in workflow breakdowns, missing tools, or management blind spots. This session introduces the Human-Machine Performance Analyst framework—a practical model for shifting from content order-taker to AI-augmented diagnostician.

Through live demonstrations with real workforce data, you’ll watch AI separate genuine training needs from systemic issues, map tasks across a 7-level human-AI collaboration scale, and generate executive-ready attribution narratives in real time. You will leave understanding how a small L&D team can diagnose at enterprise scale.

By the end of this session, you will be able to

• Distinguish between genuine training needs and systemic performance issues using an AI-augmented diagnostic process.
• Map current L&D tasks to the 7-Level Human-AI Task Scale to identify where AI should lead, support, or stay out of the way.
• Structure a 90-second executive data narrative that uses attribution modeling to connect learning interventions to business outcomes.

Many learning teams struggle to support audiences who value education but lack time for long courses or crowded LMS environments. These audiences need fast access to credible, practical learning that fits demanding schedules. This session explores how the American Academy of Dermatology uses instructional technology to make digital learning more accessible and easier to consume.

You will examine a real-world case study of using a customized LMS interface and a focused mobile app experience to surface high-value learning resources. The session will explore learner behavior, content prioritization, discoverability, and low-friction engagement strategies for busy professionals. You will also see how podcasts, quick-hit questions, and chunked learning experiences can work together within a connected learning ecosystem rather than as isolated resources.

Designed for instructional designers, LMS administrators, content managers, and learning leaders, this session provides a practical framework for improving content visibility and using existing resources more effectively to create learner-centered digital experiences.

By the end of this session, you will be able to

• Identify barriers that prevent busy professionals from engaging with digital learning, especially on mobile devices.

• Evaluate ways to organize and surface high-value content so learners can quickly find the most relevant resources.

• Apply a more intentional digital learning experience, especially if you have the benefits of AI curation.

Ever built a course where the content was solid—but learners still struggled to get through it?

Digital learning often fails not because content is wrong, but because design creates friction. In this session, you will explore a practical, 3-part framework for reducing cognitive friction across working memory, attention, and task initiation. Through real-world examples, you will identify common issues like split attention, unnecessary complexity, and unclear navigation.

Using your own device (no software required), you will complete short, guided activities to analyze screens and make design decisions. You will also review before-and-after redesigns and leave with practical UX strategies that you can immediately apply to improve learner performance.

By the end of this session, you will be able to

• Identify sources of cognitive friction related to working memory, attention, and task initiation in elearning design.

• Evaluate how visual hierarchy, interaction design, and navigation impact learner usability and performance.

• Apply practical UX and instructional design patterns to reduce cognitive friction in your own courses.

AI can generate content in seconds. That’s the good news.

The bad news is that organizations are drowning in auto-generated articles, chat responses, SOP summaries, and micro-courses that are technically correct but unusable at the moment of need. Frontline workers need the right answer instantly, in context, and without friction.

The challenge is building performance support systems that are searchable, trustworthy, AI-enhanced, and structured without creating duplication, hallucinations, or compliance risk.

In this session, Chad Udell will show you how performance support must evolve in the age of AI. You will explore how to

• Design a performance support strategy before using AI.

• Structure source materials so AI can safely augment them.

• Improve search, retrieval, and categorization.

• Blend learning with real-time AI retrieval.

• Create governance guardrails that protect data integrity.

This session offers a practical, experience-driven approach to making AI useful while maintaining control of your content ecosystem.

By the end of this session, you will be able to

• Design a structured performance support content architecture that enables safe and effective AI augmentation.
• Apply practical criteria to determine when to use AI for content creation versus when to use human-led curation.
• Develop governance and retrieval strategies that improve search accuracy, reduce hallucinations, and increase frontline usability.