No More Mis-Steaks: From Oops to Oustanding!

This project was inspired by real-world observations from my part-time work as a server at Berwick on the Park, a retirement residence. With my hospitality background, I noticed recurring performance issues among new staff: skill gaps such as incorrect orders, overlooked needs, and mishandled complaints. That directly affected resident satisfaction.

To frame the scenario, I created a fictional client, Golden Pines Retirement Residence, positioned as a high-end brand. From the outset, I grounded the project in the ADDIE model and Kirkpatrick’s New World model to ensure a structured process that connected staff behaviors to business outcomes. Guided by Kirkpatrick’s Results level, I defined the business goal as strengthening Golden Pines’ reputation and attracting new residents through exceptional service, with the dining experience as a key differentiator.

The immediate challenge was that resident satisfaction with dining had been declining, due to recurring dining service mistakes. Since these issues stemmed from staff skill gaps rather than external factors, training was the necessary intervention. Addressing this required a solution that would directly target staff performance gaps and lead to measurable improvements in on-the-job behaviors. This is also where the first of my design principles, curiosity, came into play: asking the right questions to uncover the real problem and exploring effective ways to solve it. 

Problem Summary:

Audience: Waitstaff at a retirement residence.

Purpose: Improve resident satisfaction by reducing service errors.

Business Goal (Kirkpatrick Level 4): Strengthen Golden Pines’ brand reputation as a high-end residence by enhancing the dining experience.

Performance Gap: Staff lacked practical skills in ensuring correct delivery, anticipating resident needs, and handling complaints.

Frameworks: ADDIE model and Kirkpatrick’s New World Model.

To design the solution, I applied Cathy Moore’s project goal formula to create measurable objectives:
The number of preventable dining service errors per shift will decrease by 75% by December 2025 as waitstaff consistently verify orders, monitor tables proactively, and respond to complaints professionally.

From there, I identified the specific actions waitstaff needed to perform: verifying orders when in doubt, anticipating residents’ needs, and handling complaints with professionalism. Each action was mapped to scenario-based practice, allowing learners to make decisions, see consequences, and receive feedback in a safe environment.

Since I planned to use Figma as the main work platform across the entire project, I chose FigJam (Figma’s collaboration tool) instead of Miro, which is more common in the community. This ensured a seamless workflow between brainstorming in the Design phase and visual production in the Development phase.

Guided by Kirkpatrick’s model, I built in clear evaluation points:

• Level 2 (Learning): A quiz at the end of the eLearning course tested learners’ knowledge and retention of key service standards.

• Level 3 (Behavior): Post-training, I planned to collaborate with both participants and their supervisor (the host) to evaluate whether critical behaviors were carried out consistently. This included follow-up interviews with participants and reviewing service error data per meal to confirm on-the-job application.

To bring the design to life, I drafted a storyboard with three branching scenarios: handling order mix-ups, prioritizing resident needs, and responding to complaints. Each path included realistic dialogue, feedback, and coaching from the supervisor character, Sandra. To refine the script, I also created a trained GPT model that helped me polish the writing, ensuring the content was clear, professional, and aligned with the tone of the course (click here to try the GPT I created).

As part of my streamlined workflow, I used Figma to create a Masterboard (click here to view the Masterboard) where all project information was consolidated in one place for easy tracking and collaboration. The responsive template I built resized automatically, reducing manual edits and allowing me to focus on writing. This approach not only boosted efficiency but also produced a lean, reusable storyboard that could adapt to future updates.

Design Phase Summary:

Framework: Cathy Moore’s Action Mapping to align training with real workplace behaviors.

Project Goal: Reduce preventable dining service errors by 75% by December 2025.

Key Behaviors Targeted: Verifying orders, anticipating residents’ needs, and handling complaints professionally.

Instructional Strategy: Scenario-based eLearning with branching practice and immediate feedback.

Storyboarding: Designed three branching scenarios, managed via a Figma Masterboard for streamlined tracking and collaboration.

Tools: Used Figma as the central design platform and FigJam (instead of Miro) for brainstorming, ensuring a seamless workflow between the Design and Development phases.

Evaluation Plan (Kirkpatrick):

• Level 2 (Learning): End-of-course quiz to measure knowledge and retention.

• Level 3 (Behavior): Supervisor observation, participant interviews, and analysis of service error data.

During the process, I often stood at a crossroads between going the traditional way or trying something new, and I ended up choosing the latter more than I expected. My three pillars - curiosity, efficiency and innovation - acted like a compass when making decisions. I kept asking myself: Is there another way to do this? Is this the most practical option? Is there a tool or maybe AI that can help me do it faster?

This project also proved my adaptability in learning new software by using unconventional tools like FigJam and Inkscape, as well as Figma for storyboarding. I also successfully tried out the idea of using ChatGPT as a thought partner. It served more like a personal assistant to refine and polish my work, not someone to do the job for me.

The project received positive feedback from colleagues and my manager, reinforcing its value and realism even as a demo. One limitation I realized was that I didn’t pay enough attention to accessibility. I received some verbal feedback that the colors weren’t contrasting enough, which made navigation difficult (for example, the “Next” button blended into the background). That’s something I would definitely improve in future projects.

Along the way, I created a short series called “How I Use AI as a Thought Partner in My Instructional Design Workflow” on my LinkedIn to share what I learned from this project. I highly recommend checking it out for a deeper look into how AI supported my design process.