Beyond Knowledge: AI's Behavioral Assessment

In hiring processes, technical knowledge and past experience always take center stage. However, in the business world, what determines an employee's success is generally not just "what they know" but "how they behave" under stress. AI interviews now go beyond classic questions to measure candidates' fundamental behaviors such as patience, risk appetite, and self-discipline. This enables companies to find not just "talented" candidates, but also the "right" ones. Current research reveals how these AI-powered in-depth analyses are fundamentally changing hiring quality.

The impact of behavioral measures on AI decisions

When people think of AI in hiring, resume screening usually comes to mind first. However, the study titled "Behavioral Measures Improve AI Hiring: A Field Experiment" conducted by Marie-Pierre Dargnies, Rustamdjan Hakimov, and Dorothea Kubler (2025) proves that behavioral data collected from candidates, such as patience and risk management, tremendously increases AI's prediction accuracy. The AI algorithms used in this research analyze not only candidates' technical answers but also their preferences measured through surveys and games.

The data from the research also shows which variables the AI model weighs when making decisions. In the model prepared by Dargnies and team, the "Risk Aversion" variable tops the list with a 100% importance score, followed by "Patience" at 91.5% and "Self-Confidence" at 88.2%. These results show that when AI predicts whether a candidate will succeed in the long term, it relies on these fundamental behavioral characteristics rather than their technical knowledge. Particularly in sectors where risk management is critical, such as finance and microfinance, candidates selected through AI are observed to manage much lower-risk portfolios.

AI goes beyond superficial answers

In traditional interviews, candidates tend to give the "ideal" answers the interviewer wants to hear. However, AI breaks through this wall with its "probing" or deep questioning ability. The study titled "Conducting Qualitative Interviews with AI" prepared by Felix Chopra and Ingar Haaland (2024) emphasizes that candidates' real mental models only emerge in the later stages of the interview, thanks to AI's persistent follow-up questions.

AI agents, rather than accepting the candidate's initial superficial and formulaic answers, generate new questions that probe the motivations beneath those answers. Chopra and Haaland (2024) note that "deep mental models" that were virtually invisible at the beginning of the interview were successfully identified by the end. This way, AI doesn't simply ask candidates whether they are patient; it distills this data from the examples they provide and the reasoning chains they construct.

Jabarian and Henkel (2026) note that AI interviewers addressed key hiring topics more frequently than human interviewers and that interviews were more comprehensive. Thus, each candidate's characteristics like risk management and patience are measured on the same scale.

In conclusion, AI interviews are not just an automation tool; they are a powerful analysis system that converts human psychology into data. Dargnies and team (2025) show that selections made by AI measuring patience and risk preferences are marginally more efficient than decisions made by human managers. The hiring strategies of the future are being built on these hybrid models that understand candidates' characters while measuring their technical skills.

When choosing an AI interview tool for your company, it's best to pay attention to whether the system is just counting words or measuring core values like patience and self-discipline at the candidate's core. It's worth remembering that technical knowledge can be taught; but character and behavior are the most challenging and most valuable parts of hiring.

References

Chopra, F., & Haaland, I. (2024). Conducting Qualitative Interviews with AI. CESifo Working Papers, No. 10666.
Dargnies, M. P., Hakimov, R., & Kübler, D. (2025). Behavioral Measures Improve AI Hiring: A Field Experiment. Discussion Paper No. 532, CRC TRR 190.
Jabarian, B., & Henkel, L. (2026). Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews. Booth School of Business, University of Chicago.
Jurado, N. (2025). The effects of artificial intelligence on shaping employer brand perception: insights from entry-level hiring practices. Master Thesis, Universidad Carlos III de Madrid.
Poenaru, L. F., & Diaconescu, V. (2025). Bridging Technology and Talent: Gen Z's Take on AI in Recruiting and Hiring. Bucharest University of Economic Studies.
Sakib, M. N., Rayasam, N. M., & Dey, S. (2018/2024). Experience and Adaptation in AI-mediated Hiring Systems: A Combined Analysis of Online Discourse and Interface Design. University of Maryland.
Lee, B. C., & Kim, B. Y. (2021). Development of an AI-based interview system for remote hiring. International Journal of Advanced Research in Engineering and Technology (IJARET), 12(3), 654-663.