AI Learning Model

I experimented with different overlay dialog boxes to ask the user if they would like to continue with the next task once it is confirmed, marking it as complete. However, after presenting the solution to the users, they wanted to see the tasks completed, what task was next, and what tasks remained.

My second iteration included a way for operators to choose which task to complete next. However, after presenting that solution to the technical machine learning development manager, I learned that the operational tasks must be completed in order.

My third iteration asked the user if they wanted to continue to the next task, listed below, along with all remaining tasks for the current artifact. This version tested very well with operators and received the manager's stamp of approval.

The overlay dialog box was one solution to the workflow issue. However, there was more to this problem. The dashboard also needs to be changed to reflect this new workflow. It needed to focus on the artifacts and the tasks associated with those artifacts.

Currently, the dashboard is organized by task type; this new dashboard, organized by artifact, provides the operators with the optimal view for a more efficient flow. I did have a design constraint on the new dashboard; it had to be a table chart. The current dashboard had too much text and very few visuals. Too much text could make operations less efficient in their work.

To address the dashboard issue, I reorganized the chart to list artifact IDs first, with an image icon next to each artifact. The task is listed to the right of the artifact ID. This change will help operators see each artifact and each task associated with it at a glance. It also provides the optimal workflow for operators. To help with the too much text, I created status chips organized by color. These chips communicate to the operator if the task is ready to annotate, review, or on hold or if the task has rework or is completed. This change helps the operators work more efficiently. These updates to the dashboard tested very well with the manager and operators, so now the operators had their optimal workflow.

For the first iteration, I updated the state to show the colored chips I had created for the dashboard. I also made the save button a primary button and modified the placement of the other information listed on the bottom bar. This information includes the project name, the task name, who is doing the work, who is reviewing the work, and the artifact identification number. I also changed the icon on the bottom bar. The team designed the icon to put a task on hold before my arrival, but I needed clarification since it was a compose icon. The last modification was to change the compose icon to an edit icon and add a menu overlay that allows a user to mark a task as complete, put a task on hold, or assign the task to an operator (if the user was a manager).

For the second iteration, I changed the edit icon to an overflow menu icon, placing it on the far right side of the bottom bar. I then arranged the project name, task type, and artifact number in the center of the bottom bar. The information on the state of the task, who will complete the task, and who will review the task is placed on the far left side of the bottom bar.

An overlay will also appear when assigning a task. The manager will click the dropdowns, select the operators to annotate and review, and then click the "assign" button.