AI Interview Question Generation and Scorecards

Begin with a job analysis: identify the work, competencies, and evidence that matters for success. A model can turn those requirements into behavioral or situational prompts, follow-up probes, and a draft rating scale. Review each question for clarity, accessibility, and relevance. Remove questions that solicit protected or unnecessary personal information, test unrelated trivia, or reward familiarity with a particular phrasing rather than the needed skill for the actual role. A structured interview uses predetermined questions and common evaluation standards. The U.S. Office of Personnel Management describes asking candidates the same questions and assessing responses with the same scale as core features of structured interviewing. A scorecard should describe observable evidence at each rating level, such as whether a response identifies a risk and explains a relevant action. Do not let an AI-generated ideal answer become the only acceptable response when multiple job-relevant approaches exist. Pilot questions with trained reviewers. Check whether raters interpret the scale similarly and whether candidates have a meaningful chance to demonstrate the competency. Keep interviewer notes tied to evidence rather than impressions. If AI drafts feedback or summarizes responses, compare it with the recording or notes and retain human responsibility for scores and decisions. Monitor completion, accommodations, candidate experience, and differences in outcomes. Question generation is a drafting aid; job relevance and evaluation quality remain the organization’s responsibility.