AI interviewers can often make a reasonable local choice between asking a follow-up and moving to the next scripted question in controlled tests. That is not the same as proving they can safely abandon an interview plan in a live conversation. The clearest direct evidence is a 2026 text-only study using Russian interview transcripts; it found that tested models’ decisions to skip follow-ups were usually judged appropriate, while their probing, empathy and use of earlier context varied.
What “abandon the script” means in the available evidence
The phrase can mean two different things: deciding whether to ask one more question about an answer, or leaving the overall interview protocol to pursue an unexpected topic. The most direct study tests the first. It examines whether a model should follow a fixed main question with a probe or move on—not whether an AI can safely redesign an interview as it unfolds.
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That distinction matters because a good interviewer needs more than fluent questions. It should identify when relevant information is missing, ask without steering the respondent, use earlier disclosures appropriately and recognize when an answer is complete.
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In a 2026 Scientific Reports study, researchers evaluated six large language models as semi-structured psychological interviewers. They replayed ten baseline human interview transcripts through each model. Every system received the same scripted questions and respondent answers, with a script of 54 main questions across seven life domains, including biography, family, interests, formative experiences, values, work and health.
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The models judged whether each answer had sufficiently addressed its main question. If not, they generated a follow-up; otherwise, they moved on. This matched, text-only design makes the systems’ behavior comparable under the study conditions, but it is not a live interview: the models did not hear voice, tone or pauses, and a single LLM interviewee supplied answers to the follow-ups.
Three expert psycholinguists annotated 1,658 generated follow-up questions and 1,275 turns in which the model skipped a follow-up. They rated five dimensions:
- Benevolence: whether the question used an empathic tone.
- Necessity: whether a follow-up was warranted.
- Context-awareness: whether it made appropriate use of earlier disclosures.
- Openness: whether it avoided leading phrasing.
- Justified skip: whether moving on without a follow-up was appropriate.
Agreement among the three raters varied by dimension: Fleiss’ κ ranged from 0.67 for necessity to 0.93 for benevolence. The study therefore evaluated more than whether a question sounded natural, while also showing that some judgments were less consistently rated than others.
How often did the models know to move on?
Across the tested models, ratings for justified skipping ranged from 0.85 to 0.93. In this setup, evaluators usually considered it appropriate when a system did not ask another question. That is encouraging evidence for the narrow ask-or-skip decision, not a universal success rate for AI interviewers: it comes from one Russian-language, text-based protocol and the study’s particular transcripts, prompts and rating method.
The systems also differed in how much they probed. Grok 4 averaged 45.7 follow-ups per interview and was rated lowest on necessity; GPT-5 Chat averaged 19.4 and scored highest on necessity. These are results for the versions tested in the paper, not a current or permanent ranking of products. A larger number of questions is not automatically better: extra probes can add detail, but they can also burden a respondent or pursue information that is not needed.
All models scored above 0.80 on openness in the study, but ratings for empathy and context use varied. Gemini 2.5 Pro was rated most empathic, while GPT-5 Chat was more selective. Grok 4 and Qwen3 made strong use of context, though the authors noted that Grok could overuse salient details. These findings illustrate different interviewing styles rather than establishing which model is best for a real hiring process.
Does this show that AI can adapt in hiring interviews?
Not by itself. A separate 2026 CESifo working paper reports a natural field experiment involving 70,000 applicants randomly assigned to human recruiters or AI voice agents. Its authors report that applicants interviewed by AI agents were 12% more likely to receive job offers, and describe the AI interviews as more structured and consistent while still responsive to individual applicants. Human recruiters evaluated interviews and made hiring decisions.
This is relevant evidence about AI voice interviews in a hiring setting, but it does not directly measure when an agent decides to ask a follow-up or move on. It should not be treated as proof that AI agents know when to depart from a script, nor as evidence that a particular follow-up strategy caused the reported hiring outcome. The paper is identified as a working paper.
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What adjacent studies add—and what they do not
A 2024 user study with 26 participants examined follow-up strategies for conversational agents. Concept-focused and related-concept follow-ups had lower drop rates and better relevance, while general follow-ups elicited more informative responses, according to the study record from HKUST. This points to a practical trade-off: a focused question can stay on topic, while a broader one may invite a richer answer. It is design evidence, not a direct test of current LLM interviewers with job candidates.
A Findings of ACL 2025 paper uses multi-turn interviewer interactions to assess LLMs’ reasoning, factuality, instruction-following and response to feedback. It shows how interview-like exchanges can be used to evaluate model behavior dynamically. The models are test subjects in that work; it does not establish how an AI interviewer performs with human candidates.
How to judge an AI interviewer’s follow-up decisions
A meaningful evaluation should inspect both the questions asked and the moments when the system stays silent. For a real deployment, useful checks include:
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- Justified questions and skips: Does the system ask when essential information is missing and move on when the answer is complete?
- Relevance and openness: Does each probe connect to the answer without implying the response the interviewer wants?
- Context use: Does it remember relevant earlier details without repeatedly fixating on one striking disclosure?
- Handling of ambiguity or unexpected disclosures: Can it clarify uncertainty, respect a refusal and route sensitive situations to an appropriate human?
- Realistic language and voice conditions: Does performance hold up with the languages, accents, audio quality and conversational interruptions expected in use?
- Human oversight: Is there a clear path for review or escalation when the system encounters a high-stakes or sensitive answer?
The available studies do not provide a comprehensive, current comparison of commercial interview platforms across these dimensions. In particular, the controlled Russian text study does not establish performance in live English voice interviews, employment screening or clinical settings, and it did not measure participant satisfaction, diagnostic accuracy or downstream outcomes.
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