No More Superficial Answers: The Probing Power of AI

In the world of hiring, the hardest part of getting to know a candidate is going beyond their initial and typically "rehearsed" answers. In traditional interviews, interviewers sometimes get tired, and sometimes forget to ask the right question. This is precisely where AI interviews change the course of the conversation with their "probing" ability, meaning deep questioning. Probing refers to a series of intelligent follow-up questions used to uncover the real motivations, technical depth, and character traits beneath a candidate's superficial answer. Scientific data clearly shows that AI delivers a much more consistent and comprehensive performance than human interviewers in this regard.

Mental models are hidden in the depths of the interview

When you ask a candidate "Why do you want this job?", they typically give the first and most general answer that comes to mind (top-of-mind). However, this answer is insufficient for measuring the person's true competency. The study titled "Conducting Qualitative Interviews with AI" conducted by Felix Chopra and Ingar Haaland (2024) emphasizes that a candidate's real mental models and deep beliefs only emerge in the later stages of the interview, through probing questions.

AI interviewers analyze the candidate's response within seconds and ask specific follow-up questions like "Could you tell me a bit more about the challenges in that area?" or "Can you explain that with an example?" Chopra and Haaland show that through this "adaptive probing" ability, AI captures clues that a human might miss, rescuing the interview from superficiality. This allows companies to identify not just candidates who have memorized well, but those who truly command the subject.

42% more comprehensive interviews

AI's probing power increases not only the depth but also the breadth of interviews. The massive field experiment conducted by Brian Jabarian and Luca Henkel (2026) with 70,000 candidates proves that AI-led interviews are more "comprehensive" than those conducted by human interviewers. This research reveals that AI thoroughly covers all key topics and criteria designated for each candidate.

While human interviewers may lose focus toward the end of an interview, AI probes with the same attention every second. Jabarian and Henkel note that AI agents addressed key hiring topics more frequently than human interviewers. Through this mechanism called "controlled variance," the system both follows the standard protocol and flexes the interview according to each candidate's unique responses.

Discovery of behavioral measures through probing

A candidate's patience, risk appetite, or self-discipline cannot be understood just by looking at a resume. The study titled "Behavioral Measures Improve AI Hiring" by Marie-Pierre Dargnies and colleagues (2025) demonstrates how AI can measure candidates' economic and psychological behaviors through probing questions.

The AI systems used in this research incorporate specialized questions measuring candidates' risk tolerance and patience levels into the interview flow. Dargnies and team emphasize that such behavioral measures significantly improve AI's predictive power, enabling more accurate forecasting of hired candidates' performance. The probing process reveals not just "what a candidate knows" but also "how they behave" during moments of crisis.

Candidate experience: Talking to an intelligence, not a wall

It is critically important for candidates to feel during the interview that they are speaking with an expert, not a robot. Research by Md Nazmus Sakib and colleagues (2018/2024) notes that candidates most experience the feeling of "talking to a wall" in non-interactive systems. If the system simply records the candidate's answer and moves to the next question, the candidate feels devalued.

However, an AI that probes makes the candidate feel actively listened to. Sakib and team prove that systems providing affirming and deepening feedback like "I understand, that's a very interesting experience; so what did you do at that point?" increase candidate motivation. Such interactive designs:

Lower the candidate's stress level
Enable them to express themselves better
Strengthen the perception of the company's technological sophistication

Gen Z wants a transparent probing process

Generation Z, digital natives, are pleased with probing in interviews but with one condition: transparency. Poenaru and Diaconescu's (2025) research shows that Gen Z candidates trust the process much more when they know what the AI is measuring.

Nicole Jurado's (2025) thesis emphasizes that candidates view the balance between AI and human interaction as a mirror of the company's culture. When AI deeply questions technical and analytical skills during the probing phase, candidates code this as a "fair and professional" approach. However, candidates want to know that a human will evaluate this data at the final stage of the interview (Jurado, 2025).

Conclusion: The new standard of data-driven hiring

Probing power transforms AI from a simple survey tool into a strategic business partner. AI gives every candidate an equal chance, dissects every answer down to the finest detail, and uses probing questions to find and extract that "brilliant talent" that human interviewers might overlook.

In the modern business world where speed and quality are simultaneously sought, the depth offered by AI is no longer a luxury but a necessity. When choosing an AI-powered interview solution for your company, focus not just on the system's question-asking capacity but on how deeply it can "listen to" and probe the candidate. Because the best candidates aren't always the ones on the surface; they're the ones waiting to be discovered in the depths.

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, Collaborative Research Center Transregio 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.