AI Server Generating Tail Questions for Interview Competency Assessment
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Solution Overview
Problem
Existing interview evaluation methods lack the ability to generate in-depth questions that effectively assess an interviewee's competencies, relying heavily on superficial answers.
Innovation Solution
A server and method utilizing artificial intelligence (AI) to analyze previous answers and generate tail questions that target unidentifiable evaluation items, allowing for a deeper assessment of the interviewee's competencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional interview evaluation methods are used, then the interview process is simple and quick, but the ability to assess interviewee competencies in depth is insufficient
Solution Approach 1:
The patent replaces the mechanical system of traditional human-based interview evaluation with an AI-driven automated system. The AI model analyzes answer sentences, identifies unidentifiable evaluation items, and generates tailored tail questions, thereby improving assessment accuracy while reducing reliance on human interviewer judgment and experience.
Solution Approach 2:
The system enables self-service by automatically analyzing the interviewee's previous answers and generating appropriate follow-up questions without requiring complex human intervention. The AI model independently processes the evaluation, identifies gaps in the assessment, and formulates questions to probe deeper into unidentifiable competencies.
2Measurement precision
If tail questions are generated to evaluate unidentifiable items, then the evaluation depth increases, but the time required for the interview process increases
Solution Approach 1:
The AI model performs preliminary analysis of the interviewee's answer sentences before generating tail questions. It pre-identifies which evaluation items remain unidentifiable and prepares targeted questions in advance, allowing the interviewer to efficiently focus only on the most critical unassessed competencies rather than systematically reviewing all possible items.
Solution Approach 2:
Instead of uniformly applying evaluation to all competency areas, the system applies local quality by generating tail questions only for specific unidentifiable evaluation items. This targeted approach ensures deep evaluation of areas that truly need attention while avoiding unnecessary time consumption on already-assessed competencies.
3Reliability
If AI analyzes answer sentences to generate questions, then the evaluation objectivity improves, but the technical complexity of the system increases
Solution Approach 1:
The patent replaces subjective human judgment with an AI-based automated analysis system. The AI model objectively processes answer sentences, identifies evaluation items that remain unidentifiable, and generates questions based on clear, rule-based logic, thereby improving evaluation objectivity and reducing biases associated with human interviewers.
Solution Approach 2:
The AI model serves as an intermediary between the interviewee's answers and the evaluation process. It mediates by analyzing the answer sentences, translating them into structured evaluation data, and generating appropriate tail questions, thereby bridging the gap between raw answers and comprehensive assessment without requiring direct human interpretation.
Data Source
AI summary
In accordance with an aspect of the present disclosure, there is a server for generating a question on the basis of artificial intelligence (AI). The server includes a memory configured to store at least one instruction; and a processor. When the at least one instruction is executed by the processor, the server acquires answer sentences given by an interviewee for an interviewer's interview questions about a job that the interviewee applies for, evaluates each of a plurality of evaluation items required for evaluating suitability for the job on the basis of the acquired answer sentences; selects an additional item to be additionally evaluated from among the plurality of evaluation items on the basis of the evaluation, and generates a tail question on the basis of at least one of the selected additional item, the interview questions, and the acquired answer sentences and provides the tail question to the interviewee.