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205 Learning Engineering: Adapting L&D for the Future of Work and Learning

1:00 PM - 2:00 PM
Wednesday, April 12

Tracks: Management & Strategy

Learning professionals face unprecedented challenges due to shifting attitudes about work (e.g. the Great Resignation) and the shifting nature of how work gets done (mobile, flex, artificial intelligence agents, etc.). Increasingly the working and learning experiences of humans will blur together and be shared with automated intelligent agents embedded in real-world or virtual work environments. An “intelligence augmentation economy” is replacing the knowledge economy. Just like the industrial economy and service economies before it, the IA economy will fundamentally change the nature of work, what people need to learn, and what learning looks like. Do you and your team have the strategies and tools to navigate this new age of human-machine productivity?

This session you’ll learn about a process used by leading organizations like FedX, Duolingo, and the US Army to adapt L&D for present and future shifts in how people work and learn. You will unpack learning engineering use cases that iteratively apply the learning sciences using human-centered engineering design and data-informed decision making to support learners and their development. You will learn how your organization can use the learning engineering process to respond to challenges due to shifting attitudes about work and the shifting nature of how work gets done. You will learn how instructional designers work within multi-disciplinary learning engineering teams and the shared understanding needed for success. You will learn about learning engineering strategies, tools, and communities of practice available to help you transition your organization for the future.

In this session you will learn:

  • How the “intelligence augmentation economy” is replacing the knowledge economy and how you can respond
  • What learning engineering is and how you can develop your team into a learning engineering team
  • The role of instrumentation and data analytics in learning engineering
  • Who is using learning engineering to push the envelope on learning and development
  • How the US Army is using learning engineering tools for learning with military precision

Technology discussed:

DuoLingo platform, The US Army’s Generalized Intelligent Framework for Tutoring (GIFT), The Advanced Distributed Learning (ADL) Initiative’s Total Learning Architecture (TLA), The US Army’s Synthetic Training Environment Experiential Learning for Readiness (STEEL-R), IEEE Learning Technology Standards, Experience API providers and learning record stores for instrumentation of learning experiences.

Benjamin Goldberg

Senior Technical Lead of Adaptive and Intelligent Training Systems (AITS) Team

Army Research Laboratory

Dr. Benjamin Goldberg is a senior scientist at the US Army DEVCOM Soldier Center, Simulation and Training Technology Center (STTC) in Orlando. His research focuses on adaptive experiential learning with an emphasis on simulation-based environments and leveraging artificial intelligence to create personalized experiences. This involves exploring research questions linked to data science, intelligent tutoring, competency development, persistent learner modeling, immersive interaction, and mixed reality. Dr. Goldberg’s research aims to impact how technology and learning engineering can be applied to support the development and sustainment of expertise in challenging domains that require a combination of cognitive, psychomotor, and affective knowledge and skill sets.

Jim Goodell

Director of Innovation


Jim Goodell, editor of Learning Engineering Toolkit, is director of innovation at QIP, where he helps lead development of the US Department of Education-sponsored Common Education Data Standards. He is chair of the IEEE Learning Technology Standards Committee, the Competency Data Standards Workgroup, and Adaptive Instructional Systems Interoperability Workgroup. He leads the US Chamber of Commerce Foundation’s T3 Innovation Network’s Data and Technology Standards Network and the Jobs and Employment Data Exchange (JEDx) System Architecture Workgroup.