Managers often need to address performance concerns without knowing what is causing them. This project explores that challenge through a scenario-based learning experience in which the learner must navigate a performance conversation, gather information, and avoid jumping to conclusions before identifying an appropriate response.

 I designed the experience around realistic dialogue, open-ended decision-making, and responsive character interactions to give learners meaningful practice with a conversation that does not have a single scripted path. 

The project is currently in development, with the design and storyboarding phases complete and development toward the first alpha version underway. 

I began by defining the performance problem and identifying what the learner needs to do during an initial performance conversation: present observable evidence, invite the employee’s perspective, gather relevant information, and avoid assumptions before the cause is clear.

I mapped these behaviors in a performance flowchart that follows the conversation through several possible employee responses. This helped me identify the decisions learners need to practice, anticipate how the employee might react, and establish the foundation for the scenario and its interactions.

 Using the performance analysis as a guide, I developed a realistic workplace scenario in which the learner takes the role of a manager addressing a recent decline in an employee’s performance. I storyboarded the experience to establish the setting, dialogue, learner prompts, character reactions, and key decision points before beginning development.

Rather than relying on multiple-choice responses, I designed the scenario around open-ended learner input. This allows learners to practice forming their own responses while the employee’s dialogue and reactions adapt to how they approach the conversation.

 I developed a consistent visual system to support the scenario and keep attention on the conversation. Characters remain in color against a largely grayscale environment, helping their expressions and reactions stand out while maintaining a professional workplace setting.

I also created character expression and pose families for each role so visual reactions can change with the conversation while maintaining consistency across scenes. These assets were designed to support subtle emotional cues without telling the learner how to interpret each response.

With the scenario, interactions, and visual system established, I began developing the experience as a custom web-based application using HTML, CSS, and JavaScript. The experience is being built to accept open-ended learner responses and use AI to evaluate those responses within the context of the conversation rather than against a single predetermined answer.

Development is currently underway toward the first alpha version. The completed design documentation, storyboards, and visual assets provide the framework for building and testing the interactive experience.