Define the purpose, role, competencies, and interview format
Establish what the interview should measure and why the evidence is relevant to the target position.
Output: approved interview specification
AI interview readiness checklist
Checklist for AI Interviews
Use this checklist to define the interview purpose, align questions with the role, create evidence-based scoring, communicate clearly with candidates, test accessibility and technology, protect data, review fairness, retain human oversight, and monitor the interview after launch.
Structured questions
Candidate safeguards
Accountable decisions
Checklist structure
Organise AI interview readiness into four connected areas
A strong process combines assessment design, candidate protection, evidence quality, and decision governance. Weakness in one area can affect every later stage.
Explain the process and provide accessible participation
Prepare instructions, technical guidance, support, accommodations, privacy information, and interruption handling.
Output: tested candidate experience
Create structured questions, rubrics, and review controls
Define observable evidence, scoring levels, validation checks, reviewer guidance, and technology limitations.
Output: interpretable interview evidence
Assign human responsibility and monitor interview outcomes
Control access, thresholds, overrides, exceptions, fairness, changes, reporting, and post-launch review.
Output: accountable decision process
Complete AI interview checklist
Sixteen checks to complete before and after launch
Document the evidence supporting every completed item. Assign an owner, resolution date, approval status, and review schedule where further action is required.
01
Assessment purpose
Define the decision the AI interview should support
Clarify why an interview is required, which hiring stage it serves, who will use the results, and which decisions are outside its approved purpose.
Document the target role and candidate population.
Identify the interview stage and intended decision.
Record prohibited or unsupported uses.
Required evidence: approved interview-purpose statement
02
Role relevance
Map every assessed competency to actual work requirements
Use job analysis, role activities, expected decisions, stakeholder input, and performance requirements to select interview competencies.
Define each competency operationally.
Link competencies to relevant role activities.
Remove unrelated or vaguely defined attributes.
Required evidence: role-to-competency mapping
03
Interview format
Select the right AI-assisted interview workflow
Distinguish asynchronous video, live interview assistance, transcription, evidence extraction, suggested scoring, and automated decision features.
Define which features candidates will encounter.
Separate administrative automation from evaluation.
Document the role of every automated output.
Required evidence: interview workflow diagram
04
Question design
Create structured, job-relevant interview questions
Every question should collect evidence connected to a defined competency and be clear enough for candidates and reviewers to interpret consistently.
Map each question to one primary competency.
Define the evidence expected in a strong response.
Pilot wording, timing, and response instructions.
Required evidence: approved structured question bank
05
Scoring rubric
Anchor scores to observable response evidence
Avoid broad labels such as confidence, personality, or overall impression unless they are replaced with defined, role-relevant, observable indicators.
Describe evidence for every score level.
Separate response content from presentation preferences.
Include guidance for incomplete or unclear responses.
Required evidence: anchored scoring rubric
06
Reviewer calibration
Train interview users to apply the rubric consistently
Recruiters and hiring managers should practise scoring the same sample answers and discuss differences before reviewing live candidates.
Provide sample responses and scoring explanations.
Compare independent reviewer scores.
Revise unclear rubric language.
Required evidence: reviewer calibration record
07
Candidate communication
Explain the interview process before candidates begin
Candidates should understand the interview format, expected duration, recording, relevant AI use, response requirements, support, data handling, and next steps.
Use clear, direct candidate instructions.
Explain which information is recorded or analysed.
Provide a visible support contact.
Required evidence: candidate communication template
08
Accessibility
Test participation across different candidate needs
Review instructions, camera and audio requirements, time limits, devices, response formats, assistive technology, and alternative arrangements.
Test accessibility before launch.
Provide a confidential accommodation route.
Validate approved alternative interview journeys.
Required evidence: accessibility and accommodation process
09
Technical testing
Test the complete interview journey, not only the recording
Check invitations, login, permissions, supported devices, connectivity loss, retries, expiration, submission, help requests, and reviewer access.
Test desktop and mobile candidate journeys.
Verify recovery after interruptions.
Confirm support and escalation procedures.
Required evidence: end-to-end technical test results
10
Privacy and data
Control recordings, transcripts, scores, and generated outputs
Document what is collected, why it is required, who can access it, how it is transferred, and when it is deleted or anonymised.
Limit access using appropriate permissions.
Define retention and deletion periods.
Review downloads, exports, and integrations.
Required evidence: interview data-governance record
11
Automated outputs
Validate summaries, indicators, scores, and recommendations
Automated outputs should be tested against the intended interview evidence and reviewed for accuracy, consistency, limitations, and unsupported inferences.
Identify the data used to generate each output.
Compare outputs with accountable human review.
Document known limitations and error handling.
Required evidence: automated-output validation report
12
Human oversight
Define where people must inspect and challenge the evidence
Human review should include sufficient context and authority to question an automated result, correct an error, and consider conflicting information.
Assign accountable decision owners.
Define override and exception procedures.
Record the reason for consequential decisions.
Required evidence: human-review and escalation framework
13
Fairness review
Monitor the complete candidate journey across groups
Review invitation delivery, starts, completion, technical events, accommodations, scores, reviewer decisions, overrides, progression, and outcomes.
Define appropriate candidate-group comparisons.
Review sample size and relevant context.
Assign investigation and corrective-action owners.
Required evidence: fairness monitoring plan
14
Candidate incidents
Prepare for technical, privacy, access, and scoring concerns
Candidates and recruiters need clear procedures for reporting, investigating, resolving, and documenting problems affecting an interview.
Define retry and rescheduling rules.
Preserve relevant event records for investigation.
Communicate the resolution to affected stakeholders.
Required evidence: incident response procedure
15
Reporting
Present evidence without encouraging unsupported conclusions
Reports should explain score meaning, relevant candidate evidence, limitations, review flags, technical events, and where additional human judgement is required.
Use clear competency and evidence labels.
Avoid unsupported personality or potential claims.
Control report access and downloads.
Required evidence: approved interview report design
16
Post-launch monitoring
Review interview quality and suitability continuously
Roles, questions, candidate populations, scoring methods, technology, languages, policies, and decision rules can change after implementation.
Track completion, incidents, support, and feedback.
Review scoring consistency and override patterns.
Reapprove significant process or technology changes.
Required evidence: recurring AI interview review schedule
Candidate journey checks
Protect the candidate before, during, and after the interview
Review the full journey from invitation to decision. Candidate safeguards should also cover interruptions, accommodation requests, complaints, retries, and exceptional cases.
Before the interview
Prepare candidates with clear and accurate information
Explain the interview purpose, format, time, technology, recording, support, preparation, accommodations, and relevant data use.
Before
Completion evidence
Candidate instructions have been reviewed and tested
Confirm that someone unfamiliar with the platform can understand and complete the preparation steps.
During the interview
Provide consistent questions, conditions, and support
Monitor access, permissions, timing, response submission, technical events, progress, and visible support routes.
During
Completion evidence
Normal and interrupted interview journeys have been tested
Include low connectivity, failed permissions, device changes, retries, pauses, and support-assisted completion.
After the interview
Confirm submission and communicate the next step
Candidates should receive clear confirmation, support information, relevant data notices, and appropriate process communication.
After
Completion evidence
Candidate communication continues after the recording ends
Verify submission confirmation, support escalation, record handling, and hiring-process status communication.
Exceptional journey
Review incidents without penalising candidates automatically
Technical failures, approved accommodations, incomplete uploads, incorrect links, or scoring concerns require documented human investigation.
Review
Completion evidence
Exception, retry, correction, and appeal rules are documented
Assign ownership for investigation, candidate communication, resolution, evidence correction, and final decision review.
Build meaningful differences between rubric levels
Score levels should describe observable differences in the relevance, completeness, reasoning, ownership, and outcome of the candidate’s response.
01
Limited evidence
The response provides little relevant information
Key actions, reasoning, ownership, or outcomes are missing, unclear, or unrelated to the interview question.
02
Developing evidence
The response provides some relevant information
The candidate describes part of the situation or action but leaves important reasoning, detail, or results unexplained.
03
Effective evidence
The response addresses the competency clearly
Relevant actions, reasoning, responsibilities, and outcomes are explained with sufficient context.
04
Strong evidence
The response demonstrates depth and relevant judgement
The candidate explains trade-offs, adapts actions to context, evaluates outcomes, and identifies meaningful learning.
Keep human responsibility connected to every consequential output
Interview users should be able to inspect evidence, identify technical or process limitations, challenge automated conclusions, resolve exceptions, and record the final decision.
Inspect the candidate response supporting the score
Do not rely only on a final number, label, summary, or recommendation.
Check technical events, accommodations, and incomplete attempts
Determine whether the recorded evidence is sufficient for a fair decision.
Compare interview evidence with relevant hiring information
Consider skills, experience, structured interviews, work samples, and role requirements.
Allow reviewers to question or correct automated outputs
Provide the authority, evidence, guidance, and process required for meaningful challenge.
Record who made the decision and why
Maintain appropriate evidence, exception, override, and approval records.
Illustrative readiness review
Convert checklist completion into an implementation decision
Review incomplete checks, evidence gaps, responsible owners, candidate impact, and unresolved risks before approving launch or expansion. The values below are illustrative.
AR
AI Interview Readiness Review
Illustrative view
Checklist overview
Evidence, ownership, and open actions
Current implementation cycle
Illustrative readiness
Checklist completion by assessment area
Purpose and role
92
Question design
82
Candidate journey
74
Scoring evidence
68
Governance
79
Illustrative open actions
Items requiring resolution before launch
Accessibility
Complete testing of the approved alternative interview
format.
Scoring
Revise overlapping descriptors in two rubric levels.
Privacy
Confirm deletion rules for exported interview reports.
Oversight
Assign ownership for override and appeal review.
Illustrative values and interface elements demonstrate a readiness review structure. Actual completion rules, evidence requirements, owners, thresholds, approvals, and actions should reflect the role, candidate population, interview design, technology, and governance framework.
Governance checklist
Preserve assessment evidence and accountability
Store the documentation supporting the interview purpose, questions, scoring, candidate communication, data handling, automated outputs, human review, fairness monitoring, and implementation decisions.
Track changes to questions, rubrics, models, and reports
Record what changed, why it changed, who approved it, and when the new version became active.
Restrict recordings, scores, reports, and candidate data
Assign permissions based on role and review access regularly.
Document consequential decisions and significant exceptions
Preserve appropriate evidence, reviewer actions, overrides, corrections, and escalation outcomes.
Reassess the interview when its context changes
Repeat review after changes to the role, candidate population, questions, technology, scoring, or decision rules.
Frequently asked questions
AI Interview Checklist FAQs
Review common questions about AI interview purpose, structured questions, scoring, candidate communication, accessibility, privacy, automated outputs, fairness, human oversight, and monitoring.
What should be completed before launching an AI interview?
Define the interview purpose, target role, competencies, questions,
scoring rubric, candidate journey, accessibility process, privacy
controls, technical testing, automated-output validation, human
oversight, fairness monitoring, reporting, and governance.
How should AI interview questions be selected?
Select questions through role analysis. Each question should map to
a defined competency, collect relevant evidence, use clear wording,
fit the candidate population, and have an anchored scoring rubric.
What should an AI interview scoring rubric contain?
The rubric should define observable evidence for each performance
level, distinguish adjacent scores meaningfully, explain incomplete
responses, avoid vague impression labels, and support reviewer
calibration.
What should candidates be told before an AI interview?
Explain the interview purpose, format, expected duration, recording,
relevant technology use, preparation, response requirements,
support, accommodations, data handling, and next steps.
How should accessibility be included in the checklist?
Review instructions, time limits, camera and audio requirements,
device compatibility, response formats, assistive technology,
accommodation requests, alternative arrangements, and support
outcomes.
Which AI interview data controls should be checked?
Review the purpose, collection, recording, transcription, generated
outputs, permissions, sharing, downloads, integrations, retention,
deletion, incident handling, and audit records.
How should automated interview outputs be validated?
Identify how each output is generated, compare it with relevant
human-reviewed evidence, examine consistency and errors, document
limitations, test intended populations, and define correction and
escalation procedures.
Where is human review required?
Human review is important when interpreting candidate evidence,
resolving technical incidents, considering accommodations,
reviewing conflicting information, challenging automated outputs,
approving exceptions, and making consequential hiring decisions.
How should fairness be monitored after launch?
Review invitation delivery, starts, completion, technical events,
accommodations, interview scores, reviewer decisions, overrides,
progression, and final outcomes across appropriately defined
candidate groups.
How can CloudTest support AI interview workflows?
CloudTest can support configurable interview workflows, structured
questions, candidate attempt management, evaluation processes, and
assessment reporting. Available capabilities may vary by plan and
implementation.
Complete the safeguards before enabling automation
Build AI interviews around structured evidence and human review
Define the interview purpose, align questions with the role, anchor scoring, support candidates, test accessibility and technology, protect data, validate outputs, monitor fairness, and assign accountable decision owners.