03 · What You Need to Know
How AI Can Support Questionnaire Adaptation Across Populations
What Does It Mean to Adapt a Questionnaire for Another Population?
Questionnaire adaptation involves evaluating and, when necessary, modifying an instrument so that it can be used appropriately with respondents who differ from the population for which it was originally developed or evaluated.
Differences may involve age, educational level, occupation, institutional environment, language, cultural background, or familiarity with the subject being investigated.
For example, a questionnaire measuring technology acceptance among university students might contain statements about completing coursework, accessing learning materials, or communicating with instructors. When used among teachers, some of these activities may no longer represent the intended construct in the same way.
AI can suggest replacements, but researchers must determine whether the proposed activities represent the same theoretical attribute.
Adaptation therefore concerns the suitability of the instrument for a new context, not simply its grammatical readability.
First Determine Whether the Original Construct Applies to the New Population
Before modifying individual items, examine whether the underlying construct has the same intended meaning in the new population.
Consider a scale measuring academic self-efficacy among undergraduate students. The original items might focus on completing assignments, understanding lectures, and preparing for examinations.
If the scale is adapted for postgraduate research students, some original tasks may remain relevant, but research design, scholarly writing, and independent inquiry may become more salient. Whether these activities belong within the intended construct depends on its definition and the instrument's measurement scope.
Simply replacing "undergraduate" with "postgraduate" would not resolve questions about content coverage.
Content-validity guidance associated with COSMIN emphasizes relevance, comprehensiveness, and comprehensibility in relation to the construct, target population, and context of use. Although COSMIN originated primarily in health measurement, these principles offer a useful framework for examining questionnaire adaptation in educational and social research.
AI can help identify possible mismatches, but the researcher should establish the construct definition and evaluate its applicability independently.
What Can AI Help Change During Population Adaptation?
AI can support several types of revisions, but they differ in methodological significance.
| Adaptation Area |
Possible AI Assistance |
What Requires Verification |
| Terminology |
Replace unfamiliar occupational or educational expressions. |
Whether the revised terms preserve the intended meaning. |
| Contextual examples |
Suggest situations familiar to the new respondents. |
Whether the examples represent comparable activities or experiences. |
| Instructions |
Clarify directions for the new population. |
Whether the response task remains consistent. |
| Item relevance |
Flag questions that may not apply to the new respondents. |
Whether the items remain necessary for construct coverage. |
| Response options |
Suggest categories suited to the new population. |
Whether changes affect scoring or interpretation. |
| Reading demands |
Suggest more accessible wording. |
Whether simplification alters the construct or response process. |
The distinction between a minor wording adjustment and a substantive measurement change is important. A replacement that seems natural in everyday language may alter the behavior, experience, or attribute represented by the item.
Distinguish Surface Adaptation From Conceptual Adaptation
Some changes primarily concern how an item is expressed. Others change the content respondents are asked to evaluate.
Suppose an original item reads, "Using this platform helps me complete my coursework efficiently."
Replacing "coursework" with "academic assignments" may be a relatively modest wording change when both expressions refer to the same activities in the intended population.
Changing the statement to "Using this platform helps me teach students effectively" is more consequential. It replaces student academic work with instructional performance.
Surface adaptation
Adjusts wording, examples, or instructions while aiming to preserve the original measurement content.
Conceptual adaptation
Changes the activities, experiences, or indicators used to represent the construct in the new population.
These are practical distinctions rather than rigid categories. Even a seemingly minor change can affect interpretation, while a more substantial contextual revision may sometimes be necessary to preserve the intended construct.
Researchers can use AI to improve questionnaire wording, but adaptation requires examining whether the revised content still supports the intended measurement.
How Should You Prompt AI to Adapt a Questionnaire?
Provide the original item, construct definition, original population, new population, and intended purpose. Ask AI to identify possible mismatches before suggesting replacements.
Illustrative AI Prompt
Reviewing a Questionnaire for a New Population
"I am considering adapting a questionnaire originally developed for undergraduate students for use among postgraduate research students. The instrument measures academic self-efficacy, defined as perceived capability to perform specified academic tasks. Review each item for terminology, task relevance, assumptions about student experience, and possible changes in construct meaning. For every item, identify whether it may be retained, requires wording clarification, needs substantive adaptation, or may be inappropriate. Suggest candidate revisions, explain the conceptual differences, and identify issues requiring expert or respondent evaluation. Do not claim that the revised questionnaire is validated or equivalent to the original."
This prompt encourages AI to identify potential problems rather than automatically rewrite every item.
For an existing published instrument, researchers should also check modification permissions before providing its content to an AI service or distributing an adapted version.
Why the Original Validity Evidence May Not Transfer Automatically
An instrument may have supporting validity and reliability evidence from its original population, but that evidence does not necessarily establish the suitability of the same scores in another population.
Respondents may interpret items differently, encounter different situations, or use response categories in ways that were not represented in the original evaluation.
For example, an item about confidence in classroom participation may function differently among undergraduate students and experienced teachers. Even when both populations understand the words, the activity may have different relevance to their respective roles.
The International Test Commission's guidance on translating and adapting tests emphasizes evaluating suitability for the intended population, examining relevant equivalence evidence, and documenting adaptations.
This does not mean that every population change requires developing a completely new instrument. The necessary evaluation depends on the nature of the differences and the intended interpretation of the results.
When Is Measurement Invariance Important?
Measurement invariance concerns whether a measurement model operates comparably across groups. It becomes especially important when researchers intend to compare scores between populations.
For example, suppose a researcher adapts a technology acceptance scale originally used with university students for use among teachers. If the researcher intends to compare latent mean scores across the two populations, evidence of an appropriate level of measurement invariance would ordinarily be needed.
Without such evidence, observed differences may reflect differences in item functioning rather than genuine differences in the construct.
The relevant procedures depend on the instrument, measurement model, type of data, and comparison being made. Configural, metric, and scalar invariance are commonly discussed within confirmatory factor analysis, but their applicability and interpretation require methodological judgment.
The International Test Commission's adaptation guidance addresses the importance of evidence supporting score comparability across populations.
Importantly, measurement invariance is not automatically required for every descriptive questionnaire adaptation. It is particularly relevant when comparable construct scores and between-group interpretations are central to the study.
Population Adaptation Is Not Always Language Translation
Researchers sometimes use adaptation and translation interchangeably, but they describe different activities.
A questionnaire can require adaptation even when both populations use the same language. For instance, a questionnaire developed for American university students may contain institutional references unfamiliar to students in the Philippines, despite both versions being written in English.
Conversely, translating an instrument into another language does not necessarily make its concepts, examples, or response categories appropriate for the new population.
AI may assist with questionnaire translation, but language equivalence and contextual suitability must be considered separately.
When adaptation involves both language and culture, cross-cultural instrument adaptation requires broader evaluation than replacing words with their closest equivalents.
Why Expert Review and Cognitive Interviewing Matter
Researchers should obtain evidence that the adapted items are relevant and understandable to the new population.
Expert reviewers may assess whether the revised content appropriately represents the intended construct and whether important dimensions have been omitted or changed.
Cognitive interviewing with members of the target population can reveal how respondents interpret the questions, understand the response options, and connect the items to their experiences.
For example, postgraduate research students may interpret "academic assignments" as coursework, thesis-related tasks, or both. An AI system may consider the phrase sufficiently clear without recognizing this contextual ambiguity.
COSMIN's content-validity methodology emphasizes the importance of evaluating relevance, comprehensiveness, and comprehensibility with reference to the intended population.
AI can help prepare interview probes and organize feedback, but it cannot replace the evidence provided by cognitive interviewing and pilot testing.
Document the Adaptation Decisions
Researchers should preserve a record of the original items, proposed changes, reasons for modification, and evidence obtained during the adaptation process.
A practical adaptation matrix can include the original wording, intended construct, population-specific concern, proposed revision, reviewer decision, and testing outcome.
This documentation is especially valuable when an adapted instrument is later published, reused, or compared with the original.
If AI contributed to the process, researchers should record its substantive role and follow applicable institutional, journal, and funder requirements for disclosure.
The final instrument should be described accurately as an adapted version when substantive modifications have been made, rather than presented as an unchanged original scale.
