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This research presents JobMate, a generative AI prototype that converts static peer job-seeking posts into persona-grounded conversations to reduce anxiety and aid career sensemaking. The study demonstrates how integrating authentic user data with conversational agents can shift the burden of interpretation from the individual to the system, fostering more reflective exploration.
Same Stories, Different Journeys: Exploring Persona-Grounded Conversational Agents for Supporting Career Exploration with Peers' Posts
arXiv:2607.11039v2 Announce Type: replace-cross Abstract: Young job seekers frequently explore their career possibilities by browsing peers' posts that share job-seeking experiences. However, static browsing requires them to reconstruct fragmented cases and privately judge what others' experiences mean for themselves, sometimes intensifying anxiety through upward social comparison. In this paper, we examine how transforming these posts into persona-grounded conversations reshapes this sensemaking process. We developed JobMate, a prototype featuring agents that have personas built upon peers' posts and follow the self-determination theory to converse with users. In a between-subjects comparative study with 24 participants, RedNote browsing exposed diverse trajectories but left reconstruction and comparison largely to users, whereas JobMate supported case selection and continued questioning. The conversations further prompted users to articulate previously implicit constraints and accept, challenge, or revise the agent's interpretations. We discuss design implications for combining authentic peer experiences with generative AI in career exploration.
Abstract:Young job seekers frequently explore their career possibilities by browsing peers' posts that share job-seeking experiences. However, static browsing requires them to reconstruct fragmented cases and privately judge what others' experiences mean for themselves, sometimes intensifying anxiety through upward social comparison. In this paper, we examine how transforming these posts into persona-grounded conversations reshapes this sensemaking process. We developed JobMate, a prototype featuring agents that have personas built upon peers' posts and follow the self-determination theory to converse with users. In a between-subjects comparative study with 24 participants, RedNote browsing exposed diverse trajectories but left reconstruction and comparison largely to users, whereas JobMate supported case selection and continued questioning. The conversations further prompted users to articulate previously implicit constraints and accept, challenge, or revise the agent's interpretations. We discuss design implications for combining authentic peer experiences with generative AI in career exploration.
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
arXiv:2607.11039 [cs.HC]
(or arXiv:2607.11039v2 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2607.11039 Focus to learn more arXiv-issued DOI via DataCite
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