Q01
Message
All templatesResearch
AI Interview Data Quality Assessment
A meta-research coding instrument for researchers to systematically evaluate and compare data quality from AI-moderated interviews versus traditional qualitative methods. Designed for repeated use across multiple data sets. Estimated completion time: 12-15 minutes per data set evaluated.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
29 questions · ~12 min
Q02
Multiple Choice
Have you conducted, supervised, or formally analyzed qualitative research data within the past 12 months?
- Yes
- No
Q03
Dropdown
How many years of experience do you have conducting or analyzing qualitative research?
- Less than 1 year
- 1–3 years
- 4–7 years
- 8–15 years
- More than 15 years
Q04
Message
You are about to evaluate a set of qualitative interview data on several quality dimensions. Please have the transcript or data set you are coding readily available before proceeding. Rate each dimension based on the data set as a whole, not individual responses.
Q05
Dropdown
How many distinct themes did you identify in this data set?
- 1–3 themes
- 4–6 themes
- 7–10 themes
- 11–15 themes
- 16–20 themes
- More than 20 themes
Q06
Opinion Scale
Rate the extent to which respondents provided specific examples, anecdotes, or concrete details in their responses.
Scale: 1 – 7
Min:No concrete detailsMax:Highly specific and detailed
Q07
Opinion Scale
Rate the degree to which respondents appeared genuinely engaged with the interview process.
Scale: 1 – 7
Min:Not at all engagedMax:Highly engaged
Q08
Opinion Scale
How confident are you that the data in this set would support reliable coding by multiple researchers?
Scale: 1 – 7
Min:Not at all confidentMax:Extremely confident
Q09
Opinion Scale
Compared to your typical experience with traditionally moderated qualitative data, how would you rate the overall quality of this data set?
Scale: 1 – 7
Min:Much lower qualityMax:Much higher quality
Q10
Dropdown
Which of the following best describes your primary professional role?
- Academic researcher / faculty
- Postdoctoral researcher
- Graduate student / research assistant
- Market research / UX researcher
- Research consultant
- Data scientist / analyst
- Research director / manager
- Other
Q11
Multiple Choice
Have you personally reviewed or coded data collected through an AI-moderated interview tool (e.g., AI follow-up probes, automated qualitative interviewing)?
- Yes
- No
Q12
Multiple Choice
Which qualitative data collection methods have you used or analyzed in the past 2 years? (Select all that apply)
- In-depth interviews (in-person)
- In-depth interviews (video/phone)
- Focus groups
- Online asynchronous discussions
- AI-moderated interviews
- Ethnographic observation
- Open-ended survey questions
- Other
Q13
Multiple Choice
Which data collection method was used to produce the data set you are currently evaluating?
- AI-moderated interview (automated follow-up probes)
- Human-moderated interview (live interviewer)
- Self-administered open-ended questions (no follow-ups)
- Mixed or hybrid method
- Unsure / not disclosed
Q14
Opinion Scale
Rate the degree to which the data set contained themes you did not anticipate before analysis.
Scale: 1 – 7
Min:No unanticipated themesMax:Many unanticipated themes
Q15
Opinion Scale
Rate the extent to which respondents elaborated beyond the minimum required to answer each question.
Scale: 1 – 7
Min:Minimal responses onlyMax:Extensive spontaneous elaboration
Q16
Opinion Scale
Rate the degree to which responses appeared authentic and genuine rather than performative or superficial.
Scale: 1 – 7
Min:Entirely performative/superficialMax:Entirely authentic/genuine
Q17
Opinion Scale
How confident are you that this data set provides sufficient depth to generate actionable insights or theoretical contributions?
Scale: 1 – 7
Min:Not at all confidentMax:Extremely confident
Q18
AI Interview
What specific strengths or weaknesses did you observe in this data set that influenced your quality ratings? Please describe any patterns, surprising findings, or methodological concerns.
Q19
Dropdown
In which type of organization do you primarily conduct research?
- University / academic institution
- Market research agency
- Corporate / in-house research team
- Government / public sector
- Nonprofit / NGO
- Independent / freelance
- Other
Q20
Opinion Scale
Prior to this evaluation, how would you describe your general attitude toward AI-moderated interviewing as a qualitative research method?
Scale: 1 – 7
Min:Very skepticalMax:Very enthusiastic
Q21
Dropdown
Approximately how many individual responses or transcripts are in the data set you are evaluating?
- 1–5
- 6–15
- 16–30
- 31–50
- 51–100
- More than 100
Q22
Opinion Scale
Rate the level of elaboration and detail present within the themes identified.
Scale: 1 – 7
Min:Very superficialMax:Very elaborated
Q23
Opinion Scale
Rate the extent to which responses included emotional, experiential, or personal content.
Scale: 1 – 7
Min:Entirely factual/impersonalMax:Deeply personal and experiential
Q24
Opinion Scale
Rate the prevalence of satisficing behaviors (e.g., minimal answers, repetitive phrasing, off-topic responses) in this data set.
Scale: 1 – 7
Min:No satisficing observedMax:Pervasive satisficing
Q25
Opinion Scale
How confident are you that this data set adequately captures the range of experiences relevant to the research topic?
Scale: 1 – 7
Min:Not at all confidentMax:Extremely confident
Q26
Opinion Scale
Rate the diversity of perspectives or viewpoints represented across the data set.
Scale: 1 – 7
Min:Very homogeneousMax:Very diverse
Q27
Opinion Scale
Rate the overall depth of responses in this data set.
Scale: 1 – 7
Min:Very shallowMax:Very deep
Q28
Opinion Scale
Rate how natural and conversational the flow of the interview felt based on the data.
Scale: 1 – 7
Min:Very mechanical/stiltedMax:Very natural/conversational
Q29
Opinion Scale
How willing would you be to base published research findings or strategic recommendations on this data set alone?
Scale: 1 – 7
Min:Not at all willingMax:Completely willing
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
Frequently asked questions
What questions are in the “AI Interview Data Quality Assessment” template?
The template includes 29 ready-to-use questions, starting with: “Welcome to the AI Interview Data Quality Assessment. This survey is a structured coding instrument designed to help you…” · “Have you conducted, supervised, or formally analyzed qualitative research data within the past 12 months?” · “How many years of experience do you have conducting or analyzing qualitative research?”. The full set is previewed above, and every question is editable.
How long does this survey take to complete?
Respondents typically finish the 29 questions in about 12 minutes.
Can I customize this template?
Yes — every question, answer option, and the ordering is editable before you launch. You can add or remove questions, or ask the AI editor to rework the survey around your research goal.
Is this template free to use?
Yes. Open it in the editor and start customizing right away — no account required to try it, and the free plan covers launching your survey.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
Related templates
More studies on similar topics.
Research
Stakeholder Interview Eligibility Screener
Pre-qualifies potential stakeholder interviewees by assessing role fit, recent domain involvement, and scheduling availability for 45–60 minute research sessions.
View templateResearch
AI Tool Adoption in Research Teams
A survey studying how research teams evaluate, adopt, and integrate AI tools for data collection, analysis, and reporting. This instrument measures current tool usage, evaluation criteria, adoption barriers, training experiences, data quality perceptions, and team collaboration patterns.
View templateResearch
AI Literacy Self-Assessment for Undergraduates
A validated self-assessment instrument measuring undergraduate AI literacy across five dimensions: conceptual understanding, practical skills, critical evaluation, ethical reasoning, and awareness of limitations. Estimated completion time: 10-12 minutes.
View templateResearch
AI Monitoring of Work Quality: Employee Attitudes Survey
Measures how employees feel about AI-based monitoring and quality-scoring tools at work — covering comfort, trust, fairness, and behavior change — with an AI follow-up that reconstructs one specific moment monitoring affected them instead of abstract opinions. Built for HR, legal, and workplace-technology teams evaluating or rolling out AI monitoring practices.
View templateResearch
AI Ethics Awareness Survey for Students
An academic instrument measuring student awareness of, exposure to, and attitudes toward artificial intelligence ethics. Covers concept familiarity, training exposure, ethical dilemma responses, regulation views, and willingness to prioritize ethics over convenience. Estimated completion: 8–12 minutes.
View templateResearch
AI Moderator Effectiveness by Demographic Group
A post-experience research survey evaluating the effectiveness of AI interview moderators across demographic groups. Measures overall experience quality, perceived empathy and understanding, cultural sensitivity, accessibility, and direct comparisons with human moderators. Designed to detect demographic moderation effects.
View template