Chicago eLearning & Technology Showcase 2026

Learning and development teams have more data than ever before, from completion rates and satisfaction scores to engagement dashboards and learning analytics. Yet many organizations still struggle to answer one critical question: Did learning actually improve performance?

In this interactive session, Vanessa Milara Alzate explores how to move beyond vanity metrics such as attendance, satisfaction, and course completion toward meaningful performance intelligence. Using the modernized Kirkpatrick Four Levels®, you will learn how to design elearning programs with evaluation built into the process from the start.

Through real-world examples, the session demonstrates how LMS analytics, digital learning platforms, and data dashboards can be used to measure behavior change and business outcomes rather than simple activity tracking. Participants will also explore how instructional design decisions, learning technology data, and organizational goals can work together within a unified evaluation strategy.

By the end of the session, participants will be able to

• Differentiate between vanity learning metrics and performance-based evaluation indicators.
• Design elearning programs that integrate the Kirkpatrick Four Levels® evaluation framework from the start.
• Apply learning technology data (LMS analytics, digital learning tools, and performance metrics) to demonstrate measurable business impact.

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.

Most AI training programs are built the wrong way. They teach people about AI through static elearning and then expect them to use AI effectively in dynamic workflows. The result is low adoption, surface-level usage, and learning investments that fail to improve performance.

Using Golin’s global AI transformation as a live case study, this session explores how a 1,700-person communications agency increased AI adoption from 15% to more than 90% in 18 months by rethinking its instructional design approach. You will examine the learning architecture behind those results, including role-specific pathways, expert-led microlearning, a globally scaled AI mentoring program, and adoption metrics tied to business outcomes.

Drawing on additional lessons from AI upskilling efforts across 115,000 employees at Publicis Groupe, Joe Leslie will share the strategic frameworks and instructional models that supported these initiatives, including a problem-first methodology centered on real workplace challenges.

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

• Apply a problem-first framework to AI training design.
• Build role-specific AI learning pathways.
• Evaluate AI training using adoption and performance metrics.

AI isn’t a one-and-done transformation. It’s a series of evolving projects layered across teams, systems, and strategies. In this session, you will explore the AI Implementation Canvas: a practical tool for navigating the complexity of AI adoption with clarity, care, and confidence.

The Canvas helps you ask the right questions, include the right people, and plan for both business value and ethical impact across a variety of AI initiative types. Megan Torrance will lead you in discussion of the role that L&D can play in leading the initiative. Then, you will walk away with a clear model to bring back to your organization and use immediately.

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

• Lead AI implementation conversations across 14 planning dimensions.

• Apply L&D’s unique skillset to support the organization’s collaboration around AI implementations.

• Support your organization with the AI literacy and business change frameworks needed for success.

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.

It’s Monday morning, and you check your email to see, “There’s something wrong with the onboarding process. People don’t know what they need to know after it’s done. The training needs to be revised.” But here’s the issue: you’re working with one person’s assumptions and no time to figure out what’s actually broken.

This session shows you how to stop guessing. Using a real-world example, Shannon Tipton will show you how to work with AI to pinpoint the root cause of the perceived issue and then simplify the development process. You will leave with practical workflows that you can use right away to build training that actually solves the problem.

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

• Diagnose training requests using AI tools to find the real cause of business problems, so you can build solutions that fix actual issues instead of incorrectly perceived ones.

• Apply AI-powered content audits to evaluate existing training materials, helping you keep what works and cut what doesn't, save time, and improve quality.

• Implement AI workflows that link training to business problems, so you can build solutions faster and show stakeholders the real impact.