This concept project is a branching scenario eLearning experience that places new managers inside a realistic workplace situation where every decision shapes trust and team dynamics.
The course's standout feature is an AI-powered email writing practice that delivers instant, personalized feedback to help learners communicate about missed deadlines with clarity and professionalism.
Audience: New and emerging team leads in fast-paced digital product environments
My Role: Instructional design, eLearning development, storyboarding, visual design, UX, AI integration
Tools: Articulate Storyline 360, Figma, Adobe Illustrator, ChatGPT API
First-time team leads are often promoted for their individual performance, not their ability to manage people. One of the first challenges they face is addressing missed deadlines, which is both common and uncomfortable. Without guidance on how to approach these conversations, they may delay addressing the issue, come across too harshly, or leave without clear agreements. Each of these responses erodes trust and makes the problem worse. Most new team leads have limited opportunities to practice these conversations before they happen, which leads to uncertainty and inconsistent responses in real situations. This project was designed to close that gap by placing learners in a realistic scenario where they make decisions and experience the consequences before facing it in real life.
I designed a two-part branching scenario based on a realistic workplace situation. Learners take on the role of a newly promoted team lead managing Andrew, a strong and experienced team member who has missed consecutive deadlines. The experience unfolds across four decision points focused on timing, tone, response, and follow-up. Each decision shapes how the situation develops. Rather than presenting abstract principles, the scenario allows decisions to play out through realistic dialogue and outcomes. Poor choices lead to tension, misalignment, or continued delays instead of simple right or wrong feedback. This reflects the complexity of real leadership situations. The second part focuses on written communication. Learners write an email to Maya, a remote team member who has also missed deadlines. This activity uses the ChatGPT API to provide real-time, personalized feedback on tone, clarity, and expectations. It gives learners a chance to practice and refine their communication in a way that mirrors real workplace feedback.
Action Mapping
The design process began with action mapping. I interviewed an experienced people manager with a background in leading teams in digital product environments to understand where new team leads most commonly struggle when addressing missed deadlines. From that conversation, I identified four critical actions that drive successful outcomes in these situations. A new team lead needs to
address the issue promptly rather than waiting for it to resolve on its own,
open the conversation with curiosity rather than blame,
respond by clarifying expectations rather than avoiding or escalating,
follow up in a structured way that creates visibility before the next deadline slips.
Each action was paired with the correct behavior and the common mistake that new leads typically make at that stage. Rather than creating arbitrary wrong answers, every incorrect branch in the scenario reflects a real pattern the SME had observed in first-time managers, giving the consequence screens credibility and relevance. These four actions became the backbone of the entire scenario, with each decision point in the course mapping directly to one of them.
Analysis
With the action map in place, I identified the core performance problem: new team leads lack practical experience navigating accountability conversations, particularly when the person being addressed is a competent, valued contributor. The challenge is not just knowing what to do. It is knowing when and how. This framing confirmed that a scenario-based approach would be more effective than a traditional knowledge-check course, because the skill is fundamentally situational and interpersonal.
Design
I developed a narrative-driven storyboard built around a single, continuous workplace scenario. The story follows a specific arc: a new team lead, a team member with a recurring delivery problem, and a series of escalating decision points. Each branch was written to feel like a real conversation rather than a training exercise, so learners stay emotionally engaged instead of pattern-matching to correct-answer logic.
I mapped the decision tree carefully to ensure that poor choices had meaningful, compounding consequences while the optimal path rewarded thoughtful, balanced leadership. A trust variable tracked learner choices throughout, shaping the tone and stakes of each subsequent interaction.
For Part 2, I designed the AI feedback system to evaluate email submissions along multiple dimensions: whether the tone avoided blame, whether expectations were stated clearly, and whether the message struck a professional and supportive balance. The feedback was designed to feel developmental rather than evaluative, with the goal of helping learners improve their thinking rather than simply confirming whether they got it right.
Visual Design
All visuals in this project are fully custom. I designed the character illustrations, environments, and UI elements in Figma and Adobe Illustrator, building a visual language that feels grounded and professional. The design system is consistent with a contemporary digital product company aesthetic rather than a generic corporate training look, keeping the focus on the narrative rather than the interface.
Development
The course was built entirely in Articulate Storyline 360. I used variables, conditional triggers, and slide layers to manage the branching logic, track the trust score across scenes, and deliver different dialogue depending on learner choices. The AI-powered email practice module was integrated using the ChatGPT API, with custom prompt engineering to ensure feedback was consistent, specific, and appropriately calibrated to the task.
Iteration
Throughout development, I tested the branching paths repeatedly to verify that each decision led to the correct consequence and that the feedback loops felt authentic rather than mechanical. The AI module required particular attention to prompt design. Early versions produced feedback that was too generic or too lenient, and I iterated on the system prompt until the responses consistently matched the quality standard I was aiming for.