Rapid Reskilling Part III: AI-Based Practice and Assessment

In previous blogs, we discussed the need to prepare workers for the post-COVID economy, and presented our Spiral-Stair-Case model for rapid reskilling. This blog describes how Alelo’s Enskill® technology supports this process, and how it can help large numbers of trainees reskill rapidly at a low cost per trainee. Enskill has been shown to help learners quickly develop communicative competencies in foreign languages and cultures, and now shows promise more broadly to help trainees quickly develop skills for new occupations.

Enskill curricula are built around cases, which here means specific situations in which workers must perform tasks as part of their occupation. For public health workers, each interview of a specific client is a case. Trainees study and practice a variety of cases until they achieve full mastery of the course objectives. Cases can be presented in multiple ways:

  1. As annotated examples. A module on interviewing skills might include annotated transcripts of interviews with clients.
  2. As practitioner commentaries. Experts and practitioners talk on video about cases they encountered and how they handled them.
  3. As group exercises. Groups of trainees work through a case together and suggest possible ways to address it.
  4. As simulations. The trainee is presented with an avatar playing the role of a client or patient, and must interact with the avatar to accomplish the task.

In each case, the trainee must employ one or more competencies, sometimes referred to as can-do statements. Successful completion of the task provides evidence that the trainee has mastered the associated competencies. Rapid reskilling in healthcare and other industries introduces further complexities:

  1. Competencies can be broad and encompass multiple subcompetencies. For example, the core competency of Effective Communication in the Massachusetts Core Competencies for Community Health Workers subsumes clear communication, professional communication, using plain language, asking open-ended questions, and active listening.
  2. Tasks can be complex and subsume multiple subtasks. For example, a typical contact-tracing interview comprises multiple phases: introduction, information gathering and listening, advice and instructions, and conclusion. These can be practiced individually or together.
  3. Some cases require more subcompetencies than others. Enskill therefore starts with simple cases and then presents cases that require a broader range of subcompetencies, in accordance with the Spiral-Stair-Case Model.

Enskill utilizes artificial intelligence in multiple ways throughout the training process:

  1. To control the behavior of the avatars in the case simulations,
  2. To assess the trainee’s performance on each case, and to estimate the trainee’s mastery of the associated competencies, and
  3. To make personalized training recommendations, based on the trainee’s performance in the simulations.

Enskill saves substantial time and cost over conventional practical training. Instead of hiring actors to play the role of clients and patients, and engaging trainers to observe and evaluate training sessions, avatar-based simulations can be run and scored automatically.

About The Author

Lewis Johnson

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.

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