Why Do Video Interviews Need to Evolve Beyond the Interview?
A video interview captures a candidate’s responses. Evaluating those responses requires recruiters to determine what they reveal about the candidate’s skills and competencies, and how they relate to the requirements of the role.
That distinction matters as AI enters the pre-screening process. LinkedIn’s 2026 Talent research found that 66% of recruiters plan to increase their use of AI for pre-screening interviews in 2026, while 70% believe it will help them have more valuable conversations with candidates. These findings reflect growing interest in using AI to support pre-screening and the expectation that it can create more room for meaningful candidate conversations.
Realizing that potential requires more than automating the interview. Recruiters need to understand what a candidate’s responses indicate, what supports that interpretation, and where further evaluation may be necessary.
An AI interview agent can help by conducting role-specific interviews and evaluating responses against defined criteria. By bringing the resulting assessments and supporting information together for recruiter review, it can make candidate responses easier to evaluate against the requirements of the role. This is one application of AI in recruitment, where technology supports different stages of the hiring process.
What Can a Video Interview Actually Evaluate?
An interview question is useful only when it elicits information relevant to the role. Asking a candidate to describe a past project, for instance, provides a different basis for evaluation than asking how they would handle a hypothetical problem. The question determines what a recruiter can reasonably infer from the response.
Depending on the role and the questions asked, interview responses can provide insight into several areas:
- Technical or functional knowledge: How well the candidate explains concepts, tools, or processes relevant to the role.
- Problem-solving and judgment: How the candidate frames a problem, considers alternatives, and explains a decision.
- Relevant experience: The responsibilities, projects, and outcomes the candidate describes.
- Role-specific competencies: How the candidate describes handling stakeholder relationships, collaboration, planning, or competing priorities.
- Communication: How clearly the candidate explains their reasoning and conveys information relevant to the question.
These areas require different kinds of questions. A question about a past project can elicit examples of how a candidate approached a challenge, while a role-specific scenario can reveal how they would reason through a problem. Neither automatically establishes competence; each provides information that must be evaluated against the requirements of the role.
For recruiters, the key is to define what each question is intended to assess and what a relevant response should demonstrate. That gives the evaluation a clear basis, whether it is conducted by a human interviewer or supported by an AI interview agent.
How Can an AI Interview Agent Evaluate These Capabilities?
An AI interview agent evaluates candidate responses against criteria defined for the role. This requires a clear link between the capability being assessed, the question asked, and what the response demonstrates.
For example, when assessing problem-solving, the evaluation may consider how the candidate frames the problem, explains their approach, weighs alternatives, and justifies a decision. The focus is on the substance of the response in relation to the intended competency, rather than on whether the answer simply sounds convincing.
Depending on the system, the output may include an evaluation, a score, and information supporting the result. Recruiters can use these outputs to examine the assessment and determine whether the response provides sufficient information for the next stage of evaluation.
The quality of the result depends on the criteria being assessed and the information elicited by the question. An AI-generated score alone does not establish that a candidate possesses a skill or will perform well in the role.
How Do Different Interview Questions Reveal Different Competencies?
The value of an interview question lies in what it helps a recruiter evaluate. Different questions elicit different kinds of information, so each should be designed around a specific competency or role requirement.
| Competency | Sample interview question | What the response can reveal |
| Problem-solving | “Tell me about a complex problem you had to solve.” | How the candidate framed the problem, considered alternatives, and explained their approach. |
| Stakeholder management | “Describe a time you had to align stakeholders with different priorities.” | How the candidate describes managing competing interests and communicating decisions. |
| Technical knowledge | “How would you approach [role-specific technical scenario]?” | The candidate’s understanding of relevant concepts and how they would apply them to a practical situation. |
| Relevant experience | “Walk me through a project where you were responsible for [specific requirement].” | The responsibilities, actions, and outcomes the candidate describes. |
The question format also matters. Questions about past experience elicit accounts of what candidates have done, while scenario-based questions explore how they would approach a particular situation. Each provides a different basis for evaluation, and neither establishes overall competence on its own.
For recruiters, the starting point is to define what each question is intended to assess and what a relevant response should demonstrate. That gives the subsequent evaluation a clear purpose.
Example of assessment interpretation: A candidate may describe a structured approach to solving a problem but offer little explanation of why they chose that approach over alternatives. This provides evidence of problem-solving ability but leaves the candidate’s reasoning unclear. The recruiter can probe this gap in a follow-up question rather than drawing a conclusion from the initial response alone.
What Makes Video Interview Assessment More Structured?
A structured interview assessment links role requirements to the questions asked, the criteria used to evaluate responses, and the information recorded for review. This gives recruiters a consistent basis for evaluating candidates against the requirements of a role.
Four elements help establish that structure:
- Assessment criteria: The skills and competencies relevant to the role.
- Question design: Questions that elicit information relevant to those requirements.
- Evaluation framework: Defined standards for interpreting candidate responses.
- Documentation: A record of responses and evaluations for subsequent review.
AI interview agents can help apply defined criteria across interviews and organize the resulting information. This can make assessments easier to compare, provided the questions and criteria are appropriate for the role.
Consistency alone, however, does not guarantee a sound assessment. If questions fail to elicit relevant information or the criteria do not reflect the role, applying the same framework across candidates will not resolve those weaknesses.
The value of a structured approach lies in making the basis of an evaluation clearer: what the role requires, what the candidate’s responses demonstrate, and how the assessment relates to the defined criteria.
What Does an AI Interview Report Tell Recruiters?
An AI Interview Report brings together a candidate’s responses and the resulting evaluation for recruiter review. Its value lies in showing not just the outcome, but also the information that informs it.
Depending on the system, a report may include:
- Interview transcript: A written record of the candidate’s responses.
- Skills assessments and scores: Evaluations against criteria defined for the role.
- Supporting evidence: Relevant parts of the responses that inform the evaluation.
- Evaluation insights: Findings from the interview that help recruiters interpret the results.
Each element serves a different purpose. The transcript records what the candidate said, scores summarize the evaluation, and supporting evidence helps recruiters examine the basis for the result.
When reviewing a report, recruiters should look beyond the overall score. Do the responses support the evaluation? Were the relevant skills covered? Are there gaps or inconsistencies that require follow-up? These checks help recruiters determine how the findings should inform the next stage of evaluation.
An AI Interview Report provides input for that decision; it does not, by itself, establish a candidate’s overall suitability for the role.
What Can’t an AI Interview Agent Reliably Evaluate?
An interview provides only a partial view of a candidate’s capabilities. What it reveals depends on the questions asked and the information the candidate shares in response.
Three limitations matter when interpreting the results:
- Overall suitability: Interview responses capture only part of a candidate’s skills, experience, and capabilities.
- Future job performance: Answers may demonstrate relevant knowledge or describe past experience, but they cannot establish how a candidate will perform in the role.
- Capabilities not assessed: An interview cannot provide meaningful evidence of skills or competencies that its questions do not address.
These limits apply to AI-assisted and human-led interviews alike. A detailed report or numerical score does not overcome gaps in the information collected.
Recruiters should interpret the results within the scope of the interview and determine whether additional information or evaluation is needed before progressing the candidate.
Why Does Human Judgment Still Matter in Video Interview Assessment?
An interview assessment informs candidate evaluation, but recruiters must interpret its findings in the context of the role and the wider hiring process. A score or recommendation alone does not determine whether a candidate should progress.
Recruiters and hiring managers need to:
- Review the findings: Examine the candidate’s responses and the information supporting the assessment.
- Resolve gaps: Identify where follow-up questions or additional evaluation may be necessary.
- Determine progression: Consider interview findings alongside other relevant candidate information before deciding on next steps.
AI interview agents can organize responses and generate assessment outputs for review. Recruiters and hiring managers remain responsible for interpreting those outputs and making progression and hiring decisions.
How Does Taggd Use TARA for Candidate Interviews?
Taggd uses TARA, its agentic AI recruitment engine to conduct adaptive, role-specific asynchronous interviews through TARA’s Interview Agent. The agent uses the structured hiring requirements, candidate persona, required skills, competencies, and hiring priorities established for the role to shape the interview. Hiring teams can also add role-specific questions before launch.
During the interview, TARA’s Interview Agent captures candidate responses and evaluates them against defined assessment criteria. Its built-in Validator checks whether the required skills, competencies, and evaluation areas have been assessed, helping identify potential gaps in interview coverage.
After the interview, TARA generates an AI Interview Report containing the interview transcript, candidate responses, skills assessments, scores, supporting evidence, evaluation insights, and AI recommendations. This gives recruiters a structured record of the interview and the evidence underlying the evaluation.
These insights can then contribute to TARA’s Decision Support Agent, which consolidates interview reports, screening results, recruiter evaluations, and supporting evidence to help hiring teams compare candidates and make informed decisions. Recruiters and hiring managers remain responsible for reviewing the findings and making the final hiring decision.
FAQs
What is an asynchronous AI interview?
An asynchronous AI interview allows candidates to complete an interview without a human interviewer participating in real time. Depending on the platform, AI may conduct the interview, evaluate responses, or support both activities.
Is an AI interview the same as a one-way video interview?
No. A one-way video interview typically involves candidates recording responses to predefined questions for later review. An AI interview describes the use of artificial intelligence in the interview process, which may involve conducting or evaluating interviews. The format and capabilities vary by platform, and an AI interview does not necessarily require video.
Can AI interview agents reduce interview bias?
AI interview agents can help apply defined assessment criteria consistently across candidates. However, consistency does not guarantee an unbiased assessment. Questions, evaluation criteria, and system outputs need appropriate testing and review.
Can candidates use generative AI tools to answer interview questions?
Candidates may use generative AI tools to prepare for interviews or assist with responses during an interview. Whether a platform can identify AI-assisted answers depends on its specific capabilities. Hiring teams should establish clear guidelines for AI use and avoid assuming that a response was AI-generated without reliable evidence.
Do AI interview agents replace human interviewers?
AI interview agents can conduct defined interview activities and generate assessment outputs without requiring a human interviewer to participate in every interaction. Recruiters and hiring managers remain responsible for reviewing the findings, determining candidate progression, and making hiring decisions.
What should recruiters consider before adopting an AI interview agent?
Recruiters should evaluate what the platform assesses, how its scores and recommendations are supported, and how candidate data is collected, used, retained, and protected. They should also examine accessibility, candidate experience, bias testing, and human oversight. Requirements vary by jurisdiction and use case, so organizations should obtain legal review before deployment.
