Use case 2: Adaptive training interface

Description:

Trainees (care providers in training) who complete the core training modules can use the training curriculum interface with an adaptive e-learning algorithm to receive additional, personalized training that reinforces specific skills. Care providers can continue using this interface after graduating from the task-sharing programs to refresh their training and avoid losing knowledge and skills.

Example scenario:

After completing core training modules, a trainee logs into the training curriculum interface and sees a personalized dashboard featuring evidence-based training modules tailored to the strengths and weaknesses identified during the roleplaying exercise they participated in during the interview (such as cognitive restructuring, empathetic listening, problem solving, or flexible thinking). The AI tool tailors the training curriculum by adjusting the pace and difficulty based on the provider’s baseline assessment and ongoing performance.

For example, a trainee struggles with accurately identifying thought patterns in cognitive behavioral therapy (CBT). The system detects this challenge based on practice rounds with the AI-generated scenarios, based on the feedback supervisors provide manually, or based on the feedback that the provider companion (see use case 9) gather during the practice sessions. The adaptive e-learning algorithm then suggests additional learning materials, practice scenarios, and simpler modules before advancing to more-complex techniques.

Opportunities unlocked:

The training curriculum interface could address several challenges in mental health task sharing programs, including the following:

• Resource-intensive training processes. Traditional interactive sessions, role-plays, and practical exercises require substantial human and financial resources. The adaptive e-learning could reduce dependency on human facilitators by offering personalized, self-paced training, allowing trainees to practice repeatedly without logistical constraints.
• Ensuring adherence to protocol. Maintaining consistent training quality is challenging without robust monitoring. The training curriculum interface could track each trainee’s progress and ensure all essential modules are completed at a pace that is comfortable to the trainee. By standardizing training, feedback, and performance tracking, the AI tool could ensure that best practices are followed, regardless of the trainee’s location. This approach allows all trainees to complete the program equipped with the same skills through standardization.
• Drift and post-program adherence. Some care providers may struggle to maintain protocols and may experience a decline in knowledge or skills without ongoing supervision or support after the program. This training curriculum interface with an adaptive e-learning algorithm could serve as a continuous resource with microlearning modules to reinforce best practices and keep skills sharp even after program completion.

How could the end user(s) benefit from this solution?

The primary end user who could directly benefit from this solution is:

• Care provider. Trainees or care providers could engage with the platform for personalized, adaptive learning experiences that help them address gaps in their skills and knowledge and get to the same level of competency as their peers.