Designing an AI Interview Experience Tailored for Blue‑Collar Workers

We examined the market’s AI interview products and found they mainly target educated white‑collar users, leaving blue‑collar workers with high barriers. To address this, we shifted focus to truly understand blue‑collar user needs.

Forget our assumptions, explore user needs.

We recruited a diverse sample of blue‑collar job seekers, both new and experienced with AI interviews, and conducted interviews, preference tests, and task tests. These methods exposed gaps in users’ willingness to speak honestly and their difficulty interpreting questions.

We structured the research around three perception elements to ensure systematic coverage:

Person – the core impression

We broke down the interviewer's appearance, voice, and interaction into quantifiable units to design digital interviewer options.

Scene – layout, visuals, background

We explored how interviewer‑candidate positioning, color schemes, and realistic backgrounds affect users’ sense of equality and comfort.

Flow – end‑to‑end product perception

Using the User Journey Mapping (UJM) tool, we recorded participants’ thoughts at every interview stage, employing task testing to reveal pain points without bias.

Through these steps we identified key insights: users prefer interviewers that feel “real” and trustworthy, layouts that avoid hierarchical cues, and backgrounds that enhance authenticity.

These findings guided concrete design decisions, balancing current AI capabilities with business goals to deliver a more inclusive interview experience for blue‑collar workers.