In hiring processes, technical knowledge and past experience always take center stage. But in the business world, what truly determines an employee's success is not just "what they know" — it's "how they behave" under pressure. AI interviews now go beyond standard questions to measure core candidate behaviors like patience, risk appetite, and self-regulation. This enables companies to find not just "talented" candidates, but the "right" ones. Current research reveals how these AI-powered deep analyses are fundamentally transforming hiring quality.

How behavioral measures influence AI decisions

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

The research data also reveals which variables the AI model weighs most heavily in its decisions. In the model developed by Dargnies and team, "Risk Aversion" ranked at the top with a 100% importance score, followed by "Patience" at 91.5% and "Self-Confidence" at 88.2%. These results demonstrate that AI relies more on these deep-rooted behavioral traits than on technical knowledge when predicting whether a candidate will succeed in the long term. Particularly in sectors like finance and microfinance where risk management is critical, candidates selected through AI have been observed to manage significantly lower-risk portfolios.

AI goes beyond surface-level answers

In traditional interviews, candidates tend to give the "ideal" answers they think the interviewer wants to hear. But AI breaks through this wall with its "probing" — deep questioning — capability. A study by Felix Chopra and Ingar Haaland (2024) titled "Conducting Qualitative Interviews with AI" highlights that candidates' true mental models only emerge in the later stages of the interview, thanks to the AI's persistent follow-up questions.

Instead of accepting a candidate's initial surface-level and formulaic responses, AI agents generate new questions that probe the underlying motivations behind those answers. Chopra and Haaland (2024) note that "deep mental models" that were virtually invisible at the start of the interview were successfully identified by its end. This way, AI doesn't simply ask a candidate whether they're patient — it distills that data from the examples they provide and the reasoning chains they construct.

Jabarian and Henkel (2026) find that AI interviewers address key hiring topics more thoroughly than human interviewers, resulting in more comprehensive interviews. This ensures that every candidate's traits — such as risk management and patience — are measured on the same scale.

In conclusion, AI interviews are not merely an automation tool; they are a powerful analytical system that translates human psychology into data. Dargnies and team (2025) show that AI's selections based on 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 measure candidates' technical skills while also understanding their character.

When choosing an AI interview tool for your company, it's worth paying attention to whether the system is merely counting words or actually measuring values like patience and self-regulation at a candidate's core. It's important to remember 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.