In our last blog post on rapid reskilling, we argued that a radical rethink of training is called for, to help workers quickly transition into new occupations. In this post, we discuss some of the shortcomings of conventional training from a reskilling perspective, and how we address them in our new instructional model for reskilling, called the Spiral-Stair-Case Model. This case-based model draws on Alelo’s experience in AI-based training technologies for rapid skill development, as well as proven instructional methods such as case-based learning and problem-based learning.
All too often, training courses make the mistake of focusing on academic knowledge for its own sake, without reference to how that knowledge is used in practice. Courses focus on learning objectives that involve articulating knowledge instead of applying it, using verbs such as describe (e.g., “describe what COVID-19 is”), define (e.g., “define social distancing”), list (e.g., “list symptoms of COVID-19”), etc. Instructional designers make the mistake of assuming that because experts have this knowledge, focusing on it is a good way to learn. Yet learning science research shows that people learn better by doing and learn better in context. Failing to follow these principles can result in courseware that is slow, tedious, and gives trainees few opportunities to apply their skills from other disciplines. Focusing on knowledge out of context fails to prepare people to use the knowledge appropriately in practice (e.g., documenting a COVID-19 case using appropriate terminology, versus explaining COVID-19 to a contact who is unfamiliar with medical terminology).
In the Spiral-Stair-Case Model, training is instead organized around competencies, tasks, and cases. The goal of the training is to master the target competencies, by presenting trainees with increasingly challenging tasks to perform and cases to work through. Trainees learn from experienced practitioners about how they handle cases, by discussing cases with their peers, and by practicing tasks themselves in simulations. AI plays an essential role in driving the simulations, assessing trainee performance, and providing feedback.
When implementing this model, we organize trainees into small groups containing people with diverse professional backgrounds, who collaborate online and work through cases together. We encourage trainees to draw analogies between the case being discussed and cases in their own experience. For example, trainees with experience in hospitality can relate their experiences in solving guests’ problems to the challenge in community health of communicating with angry or distressed contacts. These sessions facilitate re-skilling across occupations and promote deeper learning.
Learning in this model proceeds in a series of steps, moving laterally (from one competency to the next) and vertically (to progressively higher skill levels). As trainees progress, they spiral back to competencies they have trained before, but are presented with more challenging cases to further improve their skills. In this way, learning progresses rapidly in an ever-ascending spiral.
In the next blog post in the rapid reskilling series, we will discuss how Alelo uses artificial intelligence technology to help trainees reskill rapidly and quickly ascend the spiral staircase of learning.
About The Author
Dr. W. Lewis Johnson is President of Alelo and an internationally recognized expert in AI in education. He won DARPA’s Significant Technical Achievement Award and the I/ITSEC Serious Games Challenge, and was a finalist in XPRIZE Rapid Reskilling. He has been a past President of the International AI in Education Society, and was co-winner of the 2017 Autonomous Agents Influential Paper Award for his work in the field of pedagogical agents. He is regularly invited to speak at international conferences for distinguished organizations such as the National Science Foundation.