Can Generative AI Make an Interview Guide Too Generic for the Actual Research Context?

Can an AI-Generated Interview Guide Look Appropriate but Miss What Makes Your Study Important?

You are conducting a qualitative study about how university instructors navigate institutional expectations concerning generative AI. You ask ChatGPT to prepare a semi-structured interview guide.

The system produces familiar questions: "What are the benefits of AI?" "What challenges have you experienced?" "How has AI affected your teaching?" and "What recommendations would you make?"

Nothing appears obviously wrong. The questions are open-ended, understandable, and relevant to generative AI in education. You could use nearly the same questions in a study involving secondary school teachers, university students, academic administrators, or employees in another profession.

That apparent versatility may be the problem. The guide discusses the general topic but barely addresses the specific circumstances that make your research worth conducting.

Can generative AI produce an interview guide that is methodologically plausible yet insufficiently grounded in the actual research context?

Yes, AI Can Produce Relevant but Contextually Shallow Interview Questions

Why AI-Generated Interview Guides Can Lose the Research Context

What Makes an Interview Guide Too Generic?

An interview guide becomes too generic when its questions remain at such a broad level that they do not adequately explore the phenomenon, participants, or circumstances central to the research inquiry.

Generic questions are not necessarily poorly written. They may be grammatically clear, open-ended, and apparently neutral. Their weakness lies in what they allow the researcher to investigate.

For example, "What challenges have you experienced with generative AI?" could be relevant to almost any study involving AI users.

However, if the study specifically investigates how instructors exercise professional discretion under institutional AI policies, the question does not explicitly create an opportunity to examine that relationship.

A more contextually relevant question might be: "Can you describe a situation when university expectations about generative AI influenced a decision you made about teaching?"

The revision addresses a particular phenomenon without assuming that institutional expectations necessarily created difficulties.

The important distinction is not between broad and narrow questions in isolation. It is between questions that support the intended inquiry and those that remain disconnected from its substantive concerns.

Why Does Generative AI Produce Generic Questions?

When researchers provide a broad prompt, generative AI may produce questions based on familiar associations with the topic.

For example, a request for interview questions about generative AI in education may produce categories involving benefits, challenges, ethical issues, adoption, and recommendations.

These topics are common in discussions of educational technology. They may be relevant, but their familiarity does not establish that they are central to the particular study.

The model may also lack information about the institution, participants' responsibilities, local policies, or methodological approach. Without these details, it may rely on general formulations that could apply across many settings.

Researchers should not assume that generic output always reflects a technical failure. Sometimes the prompt itself does not sufficiently define the research problem.

Nevertheless, even a detailed prompt cannot guarantee that AI will recognize every contextual distinction that matters.

Distinguish Topic Relevance From Contextual Relevance

A question can address the correct topic while missing the particular research problem.

Suppose a study examines university instructors' experiences of implementing generative AI under inconsistent institutional guidance.

Asking "What are your opinions about generative AI?" is relevant to the topic. However, asking participants to describe how they encountered, interpreted, or responded to institutional expectations may be more directly relevant to the phenomenon.

Both questions could have a place in the interview, but they contribute different kinds of information.

Researchers should determine whether the guide provides adequate opportunities to explore the distinctive circumstances identified in the research questions.

What Contextual Information Does AI Need?

AI may generate more useful candidate questions when the researcher supplies information about the study's purpose and setting.

Researchers should distinguish verified contextual information from assumptions. If an institution's AI policy has not been examined, AI should not be instructed to treat it as restrictive, ineffective, or widely contested.

Providing context should improve the relevance of the questions without predetermining participants' experiences.

Use the Research Questions as the Basis for Contextualization

A useful interview guide should connect its questions to the substantive inquiry without simply repeating the language of the research proposal.

Consider the research question: "How do university instructors negotiate professional autonomy when responding to institutional expectations concerning generative AI?"

A generic interview question might ask: "What challenges do you face when using AI?"

A more relevant question could be: "Can you tell me about a situation when you had to decide how to respond to a university expectation concerning AI use?"

The researcher might then explore how the decision was made, who was involved, and whether the participant felt able to exercise professional judgment.

Castillo-Montoya's (2016) Interview Protocol Refinement framework emphasizes alignment between research questions and interview questions, together with the development of an inquiry-based conversation.

AI can help translate research questions into open-ended interview questions, but the researcher must evaluate whether the resulting questions meaningfully address the study.

Why Benefits-and-Challenges Questions May Be Insufficient

Questions about benefits and challenges are common in qualitative studies of technology use.

They can be useful when researchers seek general evaluations or want participants to identify favorable and unfavorable experiences.

However, relying almost exclusively on these categories may reduce a complex phenomenon to a familiar comparison of advantages and disadvantages.

For example, instructors' decisions about generative AI may involve professional responsibility, institutional authority, uncertainty about permitted practices, or disagreements over what constitutes appropriate use.

These experiences may not fit neatly into benefits or challenges.

Researchers should examine whether the interview guide allows participants to explain processes, relationships, and meanings rather than requiring them to classify every experience as positive or negative.

The issue is not that benefits-and-challenges questions are inherently inappropriate. It is that they should not automatically define the boundaries of every qualitative inquiry involving technology.

Contextualization Does Not Mean Adding Local Terminology Everywhere

A questionnaire or interview guide can appear context-specific simply because it mentions the name of a university, a national policy, or a local educational agency.

However, inserting contextual labels does not necessarily improve the substantive quality of the questions.

For example, changing "What challenges do teachers experience with AI?" to "What challenges do teachers at University X experience with AI?" adds a location but does not substantially change the inquiry.

A more meaningful revision would explore a circumstance that matters within the institution, such as how instructors interpret documented policies or navigate differences between departmental expectations.

Researchers should therefore distinguish superficial localization from genuine contextualization.

Contextual relevance concerns what the question enables participants to discuss, not simply whether it contains familiar institutional terminology.

Do Not Invent Context to Make Questions More Specific

One risk of requesting highly contextualized AI-generated questions is that the model may introduce institutional practices, policies, or participant experiences that have not been established.

For example, AI might suggest: "How did your university's mandatory AI training change your teaching?"

If the university has no mandatory training program, the question introduces a false premise.

Even when such a program exists, the question assumes that it changed the participant's teaching.

A more defensible question might be: "What opportunities, if any, have you had for institutional training or support related to generative AI?"

Follow-up questions can then explore the participant's actual experiences.

Researchers should verify contextual claims before incorporating them into the guide. AI-generated specificity is not necessarily factual specificity.

How Should You Prompt AI to Produce Contextually Relevant Questions?

A useful prompt should identify the phenomenon, participants, methodological approach, and verified contextual conditions. It should also require the model to distinguish known circumstances from assumptions.

This prompt encourages AI to examine the substantive contribution of each question rather than merely improve its phrasing.

However, the researcher should not assume that every broad question needs revision. Some may serve a useful introductory or exploratory purpose.

Preserve Broad Questions That Allow Unexpected Experiences

Contextual specificity is important, but an interview guide can also become excessively restrictive.

If every question refers to institutional AI policies, participants may have little opportunity to discuss experiences that fall outside that framing but remain relevant to the research.

For example, an instructor may describe a professional decision influenced primarily by student expectations or disciplinary conventions rather than formal university policy.

A broader opening question can allow such experiences to emerge before the interviewer explores particular contextual factors.

Researchers may therefore combine broad invitations with more focused questions and responsive probes.

The purpose is to maintain an inquiry that is contextually informed but not contextually predetermined.

How Can Generic Questions Affect the Depth of Qualitative Data?

Generic questions may encourage participants to provide familiar opinions rather than detailed accounts of their experiences.

For example, "What are the challenges of using AI in education?" may elicit broad statements about misinformation, plagiarism, or dependence on technology.

These concerns may be relevant, but they do not necessarily reveal how a particular instructor encountered or responded to them.

A question such as "Can you describe a situation when you had to decide whether an AI-generated output was appropriate for your teaching?" may invite a more detailed account of a decision.

Follow-up probes can then explore what the instructor considered, how the situation developed, and what meaning the experience held.

AI can help prepare possible interview probes, but effective probing depends on what participants actually say.

Researchers should not equate longer responses with better data. The relevant concern is whether the accounts contribute meaningfully to the research inquiry.

Can Generic Questions Conceal Important Differences Between Participants?

Yes. Broad questions may treat participants as though they encounter the phenomenon in similar ways.

For example, university instructors may differ in employment status, disciplinary background, institutional responsibilities, and familiarity with generative AI.

A question such as "How has AI changed your teaching?" assumes that participants use AI and have experienced change.

Some may have chosen not to use AI, while others may have limited opportunities to incorporate it into their work.

Researchers should ensure that the guide accommodates these differences rather than requiring participants to fit a single expected experience.

When several stakeholder groups are involved, AI can help adapt interview questions for different participant groups while preserving the overarching research inquiry.

Why Methodological Orientation Matters

A contextually appropriate interview guide should also reflect the qualitative approach used in the study.

For example, a phenomenological inquiry may prioritize participants' descriptions of lived experiences and the meanings they attribute to them.

A grounded theory study may place greater emphasis on actions, interactions, circumstances, and processes that can inform developing theoretical explanations.

A case study may examine how experiences unfold within a bounded institutional or organizational context.

These distinctions should not be reduced to rigid question templates. Qualitative traditions contain methodological variations, and the appropriate interview design depends on the specific approach adopted.

AI may generate questions that sound suitable for qualitative research generally while failing to reflect the epistemological and analytical commitments of the particular study.

Researchers should therefore provide the methodological orientation and evaluate the questions against its requirements rather than assuming that all open-ended questions are interchangeable.

Use a Question-to-Context Matrix

A practical way to identify generic questions is to map each item against the research question, intended participant experience, and relevant contextual considerations.

The matrix helps researchers examine what each question contributes and whether important aspects of the inquiry remain unexplored.

AI can assist with preparing the matrix, but it should not determine which experiences are theoretically important without reference to the study's framework and actual context.

Why Pilot Interviewing Is Especially Important for Contextual Relevance

A question may appear contextually appropriate to the researcher but be unfamiliar or irrelevant to participants.

For example, a guide might ask instructors about institutional AI governance. During a pilot interview, participants may explain that decisions are made informally within departments and that the term "governance" is not part of their everyday professional language.

This feedback may reveal a need to revise both the terminology and the assumptions underlying the question.

Kallio and colleagues (2016) identify pilot testing as part of systematic semi-structured interview guide development. Castillo-Montoya (2016) likewise emphasizes piloting within the Interview Protocol Refinement framework.

AI can anticipate possible mismatches, but pilot interviewing with actual participants provides evidence about how the questions function in the intended context.

Do Not Confuse Contextualization With Predetermined Findings

Researchers may inadvertently make an interview guide more specific by embedding anticipated explanations into the questions.

For example, "How did inconsistent institutional AI policies undermine your professional autonomy?" appears highly contextualized.

However, it assumes that the policies were inconsistent and that they undermined autonomy.

A more open question might ask: "Can you describe whether institutional expectations concerning AI have influenced your professional decisions?"

If participants describe inconsistency or reduced autonomy, the interviewer can explore those experiences through responsive follow-up questions.

The distinction matters because qualitative research should allow participants to confirm, complicate, or challenge the researcher's initial understanding.

Maintaining substantive researcher judgment in AI-assisted question design requires recognizing when contextual specificity becomes an unsupported assumption.

Turning a Generic AI Interview Guide Into a Contextually Relevant One

The revised guide does not simply add institutional terminology to familiar questions. It creates opportunities to explore how participants make decisions within their professional circumstances.

At the same time, it does not assume that institutional policies necessarily influence every participant or that all instructors experience the phenomenon similarly.

Common Misconceptions About Generic AI-Generated Interview Guides

Make the Questions Specific to the Inquiry Without Making the Answers Predictable

When reviewing an AI-generated interview guide, examine whether the questions could be transferred unchanged to a substantially different study. If so, consider whether the guide sufficiently addresses what distinguishes the actual research.

However, do not rewrite every broad question merely to make it appear more specialized. A useful guide may combine open introductory questions with contextually focused follow-ups.

Use a question-to-context matrix to examine the contribution of each item. Review the guide against the methodological approach, then pilot it with suitable participants.

The objective is not to make the interview sound more specialized. It is to ensure that the questions create opportunities to understand the phenomenon as it actually occurs in the research setting.

Before Using an AI-Generated Interview Guide in Your Research Context

Frequently Asked Questions About Generic AI Interview Guides

AI Can Generate Relevant Questions Without Understanding What Makes Your Study Distinctive

How to Cite This Guide

This guide is intended to be read, shared, and used in research, teaching, and academic work. If you draw on its ideas, explanations, or other content, please acknowledge the source by citing the guide. Doing so gives appropriate credit and helps your readers locate the original resource.