Takeaways
- Auditing and structuring your learning content sets the stage for reliable retrieval-augmented generation (RAG), preventing AI from hallucinating or indexing obsolete materials.
- Embed domain expertise directly into AI agent workflows, such as instructional‐design checklists and style rules, to elevate output quality beyond generic chatbot responses.
- Building knowledge bases from vetted course files enables semantic search and virtual subject‐matter expert chatbots that both validate technical accuracy and support on-demand learning.
- Creating workspaces that ground AI interactions in specific data silos ensures that generated scripts, assessments or branching scenarios remain contextually relevant and trustworthy.
- Rethinking L&D workflows rather than automating existing steps helps teams leverage AI’s intelligence while applying human wisdom to guide quality and consistency.
Summary
Training Industry’s Learning Tech Showcase, introduced by Liz Speight, frames a session on scaling AI-powered learning. Liz Speight sets the stage by contextualizing the importance of innovation in learning technologies. Jeff Fissel, Vice President of Technology at GP Strategies, then outlines how organizations can move beyond simple chatbot trials to achieve consistent, high-quality training outputs. Fissel emphasizes that raw generative AI tools often produce unreliable results when fed unstructured or outdated content.
The presentation highlights two foundational pillars: high-quality data inputs and domain-embedded AI agents. First, practitioners must audit and curate their learning assets—videos, SCORM packages, slide decks and documents—to ensure accuracy, context and relevance. Structured metadata—such as skill tags, time-based outlines and validated assessment answers—enables retrieval-augmented generation tasks to surface the correct information and minimize hallucinations. Awareness of bias in source material further safeguards against misleading outputs.
Second, learners and instructional designers should architect AI agents that mirror expert workflows. By decomposing tasks—objective generation, question creation and internal reviews—into discrete AI calls, organizations can embed instructional-design rules and style guidelines. Fissel demonstrates how the Content AIQ platform supports this process: building knowledge bases from grouped course files, creating workspaces that ground AI interactions in specific content, and deploying output agents for video scripts, assessments and branching scenarios. Integrations with Azure OpenAI services and a synthetic media tool illustrate end-to-end automation, including multilingual script generation and direct deployment to e-learning platforms.
Fissel concludes by urging attendees to rethink traditional design processes rather than merely layering AI onto existing workflows. By codifying “what good looks like” into agent architectures and leveraging curated, structured data, L&D teams can repurpose legacy content, accelerate creation timelines and maintain consistency at scale. The session closes with a call—reinforced by both Speight and Fissel—to experiment with custom agents, pilot small projects and apply human wisdom to steward AI’s intelligence.
