In this episode of The L&D Mindshift Podcast, we explore Marc Rosenberg's 2015 perspective on why the term 'learner' is limiting, and why it’s more relevant than ever today in the Generative AI era. We discuss how moving beyond the 'learner' label can empower employees as creators and contributors, transforming workplace development. We also conduct a SWOT analysis to understand the strengths, weaknesses, opportunities, and threats of this shift.

In this episode of 'The L&D Mindshift Podcast', we highlight the concerns expressed in a report by Donald H Taylor and Eglė Vinauskaitė, suggesting that AI-driven content production could devalue L&D's role, potentially leading to budget cuts and a perception of L&D as a cost center. The episode proposes an alternative vision outlined by Marc Ramos, where L&D takes on a proactive role akin to R&D, positioning itself as a strategic hub for innovation. We explore this approach through a SWOT analysis, detailing the challenges and opportunities involved.

Check out the original report & the article here: - Report - 'AI in L&D: Intention and Reality' - from Donald H Taylor and Eglė Vinauskaitė's: https://donaldhtaylor.co.uk/research_base/focus03-intention-and-reality/

- Article - 'Learning & Development is the New Research & Development by Marc Steven Ramos: https://ramosmarcs.substack.com/p/388

This episode of 'The L&D Mindshift Podcast' explores the potential end of traditional online learning as we know it, with a focus on how Large Language Model (LLM)-driven agents could transform workplace learning forever. We dive into the powerful opportunities these AI agents present, such as hyper-personalized training and 24/7 scalability, as well as the significant challenges and risks, including job displacement and ethical concerns. Join us as we analyze whether LLM-driven agents are the future of workplace learning or simply the latest passing trend.

The original research paper is here: https://www.researchgate.net/publication/383791910_From_MOOC_to_MAIC_Reshaping_Online_Teaching_and_Learning_through_LLM-driven_Agents