I designed the overall course in Articulate Rise, which was quite simple. I include a prologue story to draw the audience in and changed each background section image to make it more immersive.

I used ChatGPT heavily to create the explanation for RPO and the table comparing it to in-house and agency recruiting. I found role prompting and chain-of-thought prompting to be particularly useful in building nuanced comparisons between the three types of recruitment, which I transferred into a table (also through ChatGPT).

Through this process, I collaborated with the client to ensure a smooth user experience, both visually and in terms of content. We made several iterations, particularly to make each section more succinct.

The goal of this course was to show potential clients what RPO is, with the goal of creating more partnerships. The top five sub-points are for the quiz at the end; I focused on broad areas of consideration for determining if RPO was the right choice, and used AI prompt engineering in ChatGPT to generate concerns that potential clients might have regarding the service.

The client is a small RPO recruitment firm that is currently conducting outreach to potential client companies for possibility of partnership. However, because the RPO market is still up-and-coming in the region they operate in, they are finding that the companies they speak with don't know what RPO is. The company hired me to create a short course for potential clients about RPO and its benefits.

Is Recruitment Process Outsourcing Right For You?

This concept project was designed for a company that offers Recruitment Process Outsourcing (RPO) - an outsourcing service that takes care of another company's recruitment needs. The course is customer-facing, designed for potential clients to understand what RPO is and how they could benefit from it. At the end of the course, there is a quiz that potential clients can take to determine whether RPO is right for them or not.

This project involved heavy ChatGPT prompt engineering as well as rapid prototyping to create the final product.

Tools: Articulate Rise, Articulate Storyline 360, Canva, ChatGPT

Responsibilities: Action Mapping, Storyboarding, Visual Mockups, eLearning Development

Overview

The Process

For this project, I acted as the Subject Matter Expert (SME) based on my previous experience as a recruiter in an RPO company. I consulted with senior stakeholders about the common misconceptions around RPO and designed the Action Map. I then made a rudimentary prototype and continued to collaborate with stakeholders using an agile approach to iterate and evaluate subsequent versions.

Once the design was fully built out, I continued into the development phrase and iterated with feedback from stakeholders until the final product was ready.

Action Map

Takeaways

Full Development and Results

Articulate Rise Prototype

Creating the final product was a very natural step from developing and rapid iteration - ChatGPT and prompt engineering helped speed up the process as well. Feedback from my client through regular collaboration was invaluable, as it made the rapid prototyping very smooth. The project was very well-received by both my client company and their potential clients, who mentioned the ending quiz to be particularly helpful in determining their needs.

I do feel that while the project was a success, the impact of the initial story and the explanation could have been bigger with an interactive video - for example, using Vyond or Camtasia.

Excited to make one soon!

Articulate Storyline Quiz Prototype

For the Storyline 360 quiz, I made sure that the results would be tailored to whatever choices the audience made. This means that the quiz had to be based heavily on variables. I developed a numerical variable called "RPOornot" which would increase or stay at zero based on the point value of the choices the audience made. See image below for an example:

Based on the total point value of the audience's answers, the results would display "Yes", "Maybe" or "No" screens to show whether RPO was a fit or not. The level of customizability required frequent iterations to ensure no detail was overlooked.