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Jack's Grumpy Knee

Can You Win Over Jack . . . and His Grumpy Knee?

This AI-powered simulation gives physical therapists the opportunity to practice communicating with a reluctant patient in a realistic, low-risk environment. Learners must listen to Jack's concerns, build rapport, and use effective communication strategies to encourage him to participate in the recommended exercises.

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Click the image to experience the project.
Audience
  • Physical therapists
  • Developing communication skills
  • Working with reluctant patients
  • Working with resistant patients
Responsibilities
  • ​Subject Matter Expert consultation​​
  • Instructional Design
  • eLearning Development
  • Learner feedback
  • Testing and refinement
Tools Used
  • ​Articulate Storyline 360
  • devlin.ai
  • ChatGPT
  • OpenArt AI​

The Problem

Physical therapists need more than clinical knowledge to successfully engage reluctant patients. They need to be able to listen to concerns, respond with empathy, and build trust, but finding a way to practice navigating these conversations in a realistic, low-risk environment is challenging. 

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Jack is not sure he is ready for today's physical therapy session.

The Solution

A common patient communication challenge was transformed into an interactive AI simulation.  Rather than simply teaching how to communicate with reluctant patients, the experience requires them to do it.  Learners engage in a conversation with Jack, respond to his concerns, and make communication choices that influence the interaction.  

  • A scenario-based introduction immerses learners in Jack’s situation and provides key information to help them effectively navigate the conversation.

  • Learners have seven opportunities to respond and encourage Jack to participate in his recommended exercise.

  • Learner choices influence Jack's responses and reactions, allowing learners to experience the impact of different communication approaches.​

  • Learners must earn 70% or higher to successfully complete the training.

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The learner interacts with Jack in a realistic AI-powered simulation.

Dr. Emma, also an AI simulation, serves as a coach throughout the experience, providing real-time, AI generated coaching based on the ongoing conversation with Jack. Dr. Emma debriefs with the learner when the simulation with Jack is finished.

  • Learners can access Dr. Emma at any point during the Jack simulation by clicking her image in the upper-right corner of the screen for guidance or to talk through how the conversation is progressing.

  • Dr. Emma provides real-time, AI-generated coaching based on the learner's ongoing conversation with Jack.

  • After the Jack simulation, Dr. Emma provides the learner's final score and debriefs the interaction allowing learners to reflect on their communication approach.

  • A link to the Jack simulation transcript allows learners to revisit the conversation and reflect on their responses and communication choices.

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The learner interacts with Dr. Emma during the debrief.

My Process

Using the ADDIE framework, I transformed a patient communication challenge into an inter-active, AI-powered learning experience.

ADDIE Framework
Analysis
  • Met with a physical therapist to identify patient interaction needs 
  • Defined relevant communication challenges
  • Identified opportunities for a low- risk practice
  • Established the goal of improving patient engagement and communication skills
Design

  • Invented a realistic patient scenario centered on Jack's recovery and personal goals

  • Created opportunities for learners to practice realistic conversations

  • Planned how learner choices would influence Jack

  • Designed Dr. Emma as an AI coach to provide guidance and reflection

Development
  • Developed the interactive experience in Articulate Storyline
  • Built the AI simulations for Jack and Dr. Emma in devlin.ai
  • Created scenario content, response options, feedback, and scoring
  • Developed supporting graphics and visual elements
Implementation

  • Piloted the experience with an audience

  • Gathered feedback and identified areas for improvement

  • Refined AI prompts for Jack and Dr. Emma simulations

  • Revised evaluation and scoring criteria through multiple iterations

Evaluation

  • Evaluated learner responses against defined communication criteria

  • Used simulation scoring to measure progress

  • Reviewed simulation data in devlin.ai

  • Refined the experience based on testing and feedback

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