Empirical questionnaire design has undergone a profound transformation in recent years. DIY survey tools and artificial intelligence (AI) are increasingly shaping how questionnaires are developed in practice. At the same time, this shift has reignited fundamental debates about methodological quality, question validity, and the researcher’s responsibility for the integrity of empirical data.
Against this backdrop, an increasingly differentiated debate has emerged around the benefits and limitations of AI in empirical research. Here in particular, the use of AI is still met with considerable reservations. This skepticism can be explained by the specific sensitivity of empirical research. Here, quality is not defined solely by linguistic clarity but by measurement accuracy, theoretical grounding, and methodological consistency.
AI as a Supportive Tool for Study Design
AI can meaningfully support the questionnaire development process. At present, AI’s strength lies primarily in linguistic optimization (as tools like ChatGPT explicitly define themselves as language models) and in the structural organization of questionnaires. For example, AI can help systematize response categories, propose alternative question formulations, or generate initial drafts. Especially in the early phases of questionnaire development, this can lead to noticeable efficiency gains.
However, the use of AI becomes problematic when its output is adopted uncritically and methodological oversight is reduced. In such cases, there is a significant risk that questions may be well worded on the surface but conceptually vague or methodologically flawed. These weaknesses often remain undetected during questionnaire design and only become apparent during data analysis—potentially compromising the validity and interpretability of the results.
Prompting: Steering AI in Questionnaire Design
One of the central application areas of AI in questionnaire design lies in so-called prompting, that is, the deliberate steering of AI systems through carefully formulated task instructions. Instead of developing survey questions entirely manually, AI can be used to generate initial drafts, alternative wordings, or structural suggestions.
The quality of AI-generated outputs depends heavily on the context provided. The more clearly the research objective, target population, and methodological requirements are specified, the more usable and relevant the results tend to be. In practice, however, AI has proven to be unsuitable as an autonomous designer of questionnaires.
A key limitation lies in the frequent misinterpretation of AI as being substantively “knowledgeable” or methodologically competent. AI generates text based on statistical probabilities and linguistic patterns rather than on an intrinsic understanding of research logic, target populations, or measurement concepts (as explicitly acknowledged by ChatGPT itself).
Typical prompting errors include vague or overly concise task descriptions. In such cases, AI often produces generic, superficial, or methodologically inadequate questions. Equally problematic is the uncritical adoption of AI-generated formulations without systematically reviewing them for neutrality, clarity, scale logic, or potential response biases.
An additional risk factor lies in the seemingly high linguistic quality of AI outputs. Well-written questions may appear convincing at first glance, yet still be conceptually ambiguous, leading, or empirically unsuitable. This fluent, generic style of text generation increases the risk that methodological weaknesses remain unnoticed.
AI should therefore never be understood as a substitute for methodological expertise. Rather, it should be used exclusively as a supportive tool within a controlled and reflective development process. Responsibility for the quality, validity, and interpretability of a questionnaire remains entirely with the researcher.
A Self-Test on the Use of AI in Survey Design
Based on my own professional experience, I conducted a self-test on the use of AI in questionnaire design. As a potential source of bias, it should be stated transparently that I am not a novice user of AI tools. I have been using them professionally for an extended period, am familiar with their underlying logic, and generally appreciate their potential, but I am at the same time remaining highly aware of the risks associated with AI-generated outputs (e.g., factual errors).
The outcome of this self-test was sobering. Revealing is AI’s own response to my methodological critique. It commented on its performance as follows:
“When it comes to questionnaire design based on empirical standards, capturing the finer methodological details can be quite challenging. Scale construction, differentiating between rare and frequent behaviors, ensuring consistent response options across categories—this is where human experience truly excels. […] Your assessment highlights that practical expertise remains essential—something AI can complement but not replace.” (ChatGPT)
This self-test does not indicate a fundamental weakness of AI but rather highlights its current limitations. The ability to implement methodological nuances consistently, context-sensitively, and in line with research logic remains, for now, a domain of human expertise.
DIY Questionnaires: Opportunities, Limitations, and Empirical Risks
Market research tools designed for questionnaire development are expected to meet two competing requirements: a high degree of universality on the one hand and sufficient specificity with regard to research questions and subject matter on the other. Whether standardized templates can successfully balance these demands is a key issue in the current debate.
A Self-Test Using DIY Questionnaire Templates
Given the highly dynamic developments of recent years, I conducted a brief self-test in this area as well. Using several widely adopted DIY survey platforms, I attempted to create a simple standard questionnaire, including demographic questions. From an empirical perspective, the outcome was once again sobering.
For example, respondents based in Germany were asked about their veteran status in the U.S. military. In addition, response options frequently consisted predominantly of open-ended questions rather than standardized scales with valid response categories. Such design choices are not only confusing for respondents but can also substantially compromise data quality.
Across the various tests, a recurring pattern became apparent: even in cases where templates were presented as having been developed by experts, methodological weaknesses were repeatedly observable. These weaknesses manifested themselves both in the selection of inappropriate or illogical question types and in the formulation of individual items, as well as in the use of unsuitable scale levels.
Moreover, many elements in the German-language versions appeared to be direct translations from English. This suggests that the underlying templates originate primarily from a U.S.-centric context. Given the predominantly international and especially U.S.-focused user base of many survey tools, it is reasonable to assume that questions are collected in centralized databases and that those templates are favored that are most frequently used or selected.
However, this approach does not necessarily promote methodological quality. Instead, it tends to reinforce existing patterns, including their empirical shortcomings. In this sense, it is the frequency of use and not the empirical rigor that determines the dissemination of templates. Consequently, many thematic standard templates appeared strikingly schematic and insufficiently thought through.
Methodological Weaknesses Despite “Expert Templates”
Many of the survey tools currently available on the market aim to enable virtually anyone to create online questionnaires, even without prior methodological training. Numerous providers explicitly advertise that professional surveys can be produced easily by non-experts through the use of preconfigured question blocks, design templates, and automated settings.
While these tools undoubtedly simplify the formal construction of a survey, methodological rigor, such as the formulation of discriminating questions, the construction of valid scales, the use of appropriate filter logic, or the prevention of systematic bias, remains a professional domain. Standardized templates often fail to meet the requirements of empirical research and, when used uncritically, may even generate flawed or biased data.
Limitations of Templates for Online Surveys
Templates provide a fast and convenient starting point for creating online surveys, but they cannot replace sound methodological planning. As a rule, they are designed for average use cases and do not adequately account for specific research questions or the characteristics of particular target groups. Moreover, they frequently allow only limited customization, especially when more complex filter logic, randomization procedures, or stimulus-based elements are required.
DIY surveys enable the rapid, cost-efficient, and technically supported implementation of relatively simple data collections. They provide a low-threshold entry into online research and can yield valuable initial insights in certain application contexts.
Relying exclusively on standard templates therefore entails the risk of distorted results and reduced data validity. In such cases, online surveys fail to meet their own empirical aspirations and effectively lose their explanatory power.
Methodological quality is not created by technology, but by judgment.
Alexander Raulfs, M.A.
Recommendations for the Responsible Use of DIY Survey Tools
Based on these observations, several general recommendations can be derived for the responsible use of DIY questionnaires and survey tools:
- Ensure basic methodological competence: Even when using templates, a fundamental understanding of questionnaire design, scale types, and filter logic is essential.
- Critically review and adapt templates: Every standard question should be assessed for its relevance to the specific research objective and adjusted, if necessary, both conceptually and formally.
- Integrate quality control mechanisms: Plausibility checks, control questions, and minimum completion times can help improve data quality and identify invalid or careless responses.
- Conduct a pilot study before full deployment: Even simple DIY surveys benefit from pretesting in order to evaluate clarity, technical functionality, and the logical consistency of the questionnaire.
- Develop awareness of methodological limitations: DIY questionnaires are primarily suitable for exploratory, internal, or preparatory purposes. For scientifically robust studies, professional methodological expertise remains indispensable.
Their limitations, however, lie in methodological depth, and systematic quality assurance. Responsible users therefore treat these tools as aids rather than as substitutes for empirical expertise. Only sound methodological knowledge and professional experience allow AI- and DIY-based tools to be applied effectively, correctly, and purposefully. Anyone aiming to collect valid, comparable, and practically relevant data must continue to take personal responsibility for the design of questions, scales, and survey logic. Many of the issues discussed in this article—ranging from the development of appropriate measurement concepts and the formulation of valid questions to the systematic avoidance of common methodological errors—are addressed in more detail in the revised and updated second edition of my eBook “Introduction to Questionnaire Design for Surveys” in German. The book is intended for readers who seek not merely to apply questionnaires, but to develop them on a sound methodological basis.
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