I worked as part of a team of three to co-design prototypes for jargon translation tools to assist with re-entry into the workforce. The prototypes explored various aspects of jargon-related difficulties in the context of searching for and applying to jobs and was intended to help our participant speak the same language as potential employers when preparing job application materials.
Our co-designer is a 57-year-old disabled veteran who had a stroke a handful of years ago that resulted in a brain injury. Prior to his stroke, he worked as an internet director at a car dealership. He would like to get back into working in information technology (IT) and hopes this project will help him work towards that goal.
His needs are underserved as much of the technology designed for people with brain injuries is oriented at rehabilitation rather than re-entry into the job force by re-learning previous IT skills.
We conducted a brainstorming session to gain a better understanding of our participant’s daily life as well as his job searching goals and habits. Our participant suggested potential tools that could help him return to work and ranked the three possible design directions:
Our team decided to focus on the jargon translator option. We conducted a co-design session to better understand the exact contexts and modalities for when and how a jargon translator tool would be most useful. We identified four key features the tool should have.
Immediately following the requirements identification, all four co-designers produced sketches to correspond with each key feature. We discussed the sketches and used sticky notes and star icons in Figma to record specific aspects the participant identified as important.
During the final remote 45-minute session, we presented three Figma prototypes and solicited feedback. We asked open-ended questions regarding ease of use and provided variations on design choices to better understand our participant’s preferences and needs during key interactions.
The first prototype is a jargon assistant tool for LinkedIn. When active, this tool automatically highlights jargon-like terms and phrases within job postings on LinkedIn’s website. Users can click on a highlighted term or phrase that interests them, and the jargon assistant tool will appear as a movable overlay. Users can manually click on the different highlighted words and phrases within the job posting to see individual definitions. Alternatively, users can choose to navigate through all of the highlighted terms on the page using the controls within the jargon assistant overlay.
The second prototype is Notework—a web browser extension drawer component that flies out from the right-hand side of their screen. As users navigate job websites, they can save unfamiliar words or phrases by highlighting content using their cursor. Users can copy, paste, upload media, and add styling options to their definitions. Notework entries can be saved as a new word, definition, or media that can then be searched or filtered by field, date, alphabetical order, content type or language.
The third prototype is an add-in for Microsoft Word that leverages features from the jargon translator and Notework by providing context-specific writing suggestions. Users first specify the kind of document and the job and/or industry they are applying to. The tool can also call the user’s LinkedIn profile for saved jobs to provide more relevant wording suggestions. If a user is using a word or phrase in an unexpected way or the text seems too vague, the plug-in will provide more appropriate suggestions. For example, “helping people'” could be replaced with “working as a liaison between our customers and the product team.” Users can ignore suggestions. When jargon terms appear in a wording suggestions, there is a Define button that will trigger the jargon translator. Similar to Notework, users can add attachments including audio and media as well as tags for organization purposes. Users can browse their saved Notework entries by keyword, phrase, or tag.
Overall, our participant was impressed but wanted the three prototypes to be incorporated into a single tool. There are several ways the prototypes could be unified:
Our participatory design sessions were conducted remotely via Zoom during the COVID-19 pandemic, and while certain activities may have been more quickly or easily accomplished in-person, our team found that it is feasible to conduct this type of work remotely. Sketching together as a team and sharing our feedback with one another was particularly productive. Our participant was specific and articulate regarding his needs. He also provided useful insight about where he suspected other users might struggle with the three prototypes.
We utilized a diverse array of methods and tools throughout design sessions and prototyping process. For example, Jamboard exercises helped with problem identification; sketching as a team using paper ensured our participant’s ideas and perspectives laid the foundation of the tool; and lo-fidelity prototyping using Figma enabled rapid iteration and feedback.
In hindsight, our process could have been improved had we been able to meet with our participant face-to-face. There were some design activities that could have been employed differently using physical materials that may have inspired other ideas. In addition, we believe that 90-minute sessions with the participant would have been more appropriate than 60-minutes sessions. Given the virtual nature of our work together, certain tasks required more time than they would have at an in-person meeting. Overall, however, we are satisfied with the selection of methods used, doubly functioning as a learning opportunity for our team and participant.