Artificial intelligence (AI) holds immense promise for transforming workplace learning—enable personalized performance support, streamline content curation, analyze and predict learning patterns. But under the hood, these systems often reflect hidden biases, questionable data practices, and oversimplified algorithms that can unintentionally exclude, misinform, or misguide learners. As L&D professionals, we must go beyond technical fascination to ask: Who trained this model? On what data? And at whose expense?

This webinar dives into the messy, often opaque world of AI decision-making in learning tools—from biased content recommendations to algorithmic nudging and inaccurate and even harmful predictive analytics. We’ll explore real-world examples, dissect common sources of bias, and provide a practical framework for evaluating the ethical risks of AI-powered solutions in your learning ecosystem. Whether you’re just beginning to implement AI or already working with vendors, this session will help you ask better questions, spot red flags, and act as a steward of ethical learning design.

Strategic EdTech Consultant and AI Literacy Architect with 20+ years of global experience helping organizations design transformative, future-ready learning ecosystems. I work across government, higher education, nonprofits, and industry to lead digital learning innovation that is ethical, inclusive, and aligned with workforce needs.

As the creator of an AI Literacy Framework for Educators and Learning Professionals, I bring a research-driven and practice-informed approach to integrating AI into learning strategy, capability development, and policy planning. My work spans AI adoption, micro-credentialing systems, LMS strategy, learning analytics, and instructional design.