Experience and Adaptation in AI-mediated Hiring Systems: A Combined Analysis of Online Discourse and Interface Design
Md Nazmus Sakib, Naga Manogna Rayasam, Sanorita Dey
TL;DR
The paper addresses the problem of candidate uncertainty in AI-mediated hiring and evaluates how increased familiarity with large language models reshapes expectations. It combines large-scale online discourse analysis with two empirical studies (one exploratory, one design-and-evaluation) to uncover gaps in transparency, accountability, and candidate support. The authors deploy a Self-Determination Theory–inspired interface with response and feedback variants to test autonomy, competence, and relatedness, finding that editing, flexible response options, and combined motivational-evaluative feedback improve user experience, though design details critically affect cognitive load and perceived authenticity. The work advances practical guidelines for transparent, candidate-centered AI interviews and highlights the need to move beyond performance accuracy toward humane, equitable interaction in automated hiring systems.
Abstract
Automated interviewing tools are now widely adopted to manage recruitment at scale, often replacing early human screening with algorithmic assessments. While these systems are promoted as efficient and consistent, they also generate new forms of uncertainty for applicants. Efforts to soften these experiences through human-like design features have only partially addressed underlying concerns. To understand how candidates interpret and cope with such systems, we conducted a mixed empirical investigation that combined analysis of online discussions, responses from more than one hundred and fifty survey participants, and follow-up conversations with seventeen interviewees. The findings point to several recurring problems, including unclear evaluation criteria, limited organizational responsibility for automated outcomes, and a lack of practical support for preparation. Many participants described the technology as far less advanced than advertised, leading them to infer how decisions might be made in the absence of guidance. This speculation often intensified stress and emotional strain. Furthermore, the minimal sense of interpersonal engagement contributed to feelings of detachment and disposability. Based on these observations, we propose design directions aimed at improving clarity, accountability, and candidate support in AI-mediated hiring processes.