I narrowed the scope for this concept project and began testing hypotheses with help of AI by crafting prompts to ‘interview’ ChatGPT. I then reviewed current research on productivity and employee perceptions of the value of meeting time. 

My (imagined) client’s organization was a small to mid-sized company of 125 primarily remote or hybrid employees; my needs analysis unearthed that wasting 7 hours per week—a little under the average time wasted on meeting time according to Atlassian—compounds to 45,500 annual hours or the equivalent of about 21 full time employees for this company. 

Using Cathy Moore’s action mapping method, I identified the following skill gap: employees do not know when to meet and when to communicate asynchronously. The ultimate goal? Reduce unproductive meeting time by 30% or about 2 meeting hours per week (that saves 13,650 annual people hours annually for more focused, productive work).

Once I identified that my solution was geared toward reducing unproductive meeting time, I placed several possibilities for solutions on an effort-impact scale. This process helps to rapidly visualize which solutions will be more likely to have the highest value. 

I opted to focus efforts on a solution that would help employees (1) determine fitting asynchronous communication alternatives and (2) run more efficient meetings.

Since this solution is designed for busy hybrid/remote employees, I created a storyboard for a scenario-based micro-course that sought to get as close to the tone and environment of their actual work.

Gagne's 9 events is a classic framework for ensuring a project's effectiveness as a learning solution, and it works as my initial checklist in my storyboarding process.

After storyboarding I iterated several rounds of slides—using a combination of Miro and Adobe Illustrator and Express—and shared my work with an enablement specialist, account executive, and other experienced learning design professionals, who provided feedback on my work. 

I chose to build the project in Articulate Rise, since the platform is mobile-responsive.

This project can be improved! In a live situation, I would deploy Kirkpatrick's levels of evaluation to check affective attunement, actual learning, behavior change, and outcomes, and to gather the relevant data I would explore xAPI.

To improve this project further, it would be fun to experiment with another AI tool like HeyGen to create an even more life-like scenario with an avatar.