Handle with Care: Coaching Conversations that Matter

Helping managers coach underperforming employees requires more than recognizing that performance has declined. They need to uncover the root cause, adapt their coaching approach, and work collaboratively with employees to create an effective plan. Rather than presenting a series of multiple-choice conversations, I wanted learners to practice authentic coaching discussions where they could ask their own questions, respond naturally, and experience the consequences of their decisions in a realistic workplace setting.

I began by designing an opening sequence that places learners inside Summit Trail Outfitters before the title screen even appears. As they walk through the store, they observe two employees interacting with customers — one demonstrating strong performance and Jordan showing subtle signs of disengagement.

After the introduction, learners receive a manager briefing and review a calendar that summarizes Jordan’s recent performance, outlines their objectives for the first one-on-one, and serves as a reference throughout the conversation.

To create the coaching conversations, I built two AI-powered employee simulations using devlin.ai and integrated them into Articulate Storyline. During each conversation, Jordan responds naturally to the learner’s questions while his body language changes in real time to reflect his emotional state — becoming more engaged, frustrated, or withdrawn based on how the conversation unfolds. Rather than scripting every possible response, the AI lets learners have authentic conversations while Storyline tracks their progress toward the learning objectives.

Between meetings, learners return to the calendar to review how they performed during the first conversation and prepare for the follow-up. The calendar doubles as a lightweight job aid, providing coaching strategies tailored to the performance issue the learner identified. During the second conversation, learners work with Jordan to develop a plan that addresses the underlying cause of his disengagement — before seeing the long-term impact of their coaching through one of three workplace outcomes: celebrating Jordan’s renewed performance, scheduling a continued performance check-in, or attending Jordan’s final day with the company.

I also wanted feedback to feel as personalized as the conversations themselves. Using devlin.ai, I created evaluation criteria tied directly to the performance objectives so learners receive AI-generated feedback after completing both coaching conversations. Instead of simply indicating success or failure, the feedback explains how effectively they diagnosed the performance issue, built rapport with Jordan, and collaborated on an appropriate coaching plan.