Can Synthetic Data Replace Real Participant Data in Research?

Can Researchers Use Synthetic Data Instead of Recruiting Real Participants?

Recruiting research participants can be difficult. Some populations are hard to reach, sensitive information may be restricted, and data collection can require substantial time and resources.

Generative artificial intelligence can produce artificial survey responses, interview transcripts, and behavioral records that resemble information researchers might obtain from real people.

Could these generated records replace participant data? If an AI system produces plausible responses from hundreds of hypothetical students, teachers, or patients, can researchers use those responses to answer questions about the corresponding human populations?

The answer depends on what the research seeks to establish. Synthetic data may replace real records for certain computational tasks, but replacing the data file is not the same as replacing the empirical evidence supplied by actual participants.

Synthetic Data Can Replace Real Records for Some Tasks, but Not Automatically Real Human Evidence

When Synthetic Data Can and Cannot Substitute for Human Participant Data

What does replacing participant data actually mean?

The phrase can refer to several different activities.

A researcher may replace an identifiable dataset with a synthetic version for software testing or controlled data sharing.

Another researcher may generate artificial responses before collecting genuine participant data to test a questionnaire or analytical workflow.

A third may attempt to replace participant recruitment entirely by asking an AI system to simulate how people would respond.

These uses have different methodological implications.

Replacing the data used by software does not necessarily mean replacing the empirical evidence needed to answer a research question.

The distinction is particularly important when the study concerns human experiences, attitudes, or behavior.

Why are synthetic participants not equivalent to real participants?

A genuine participant contributes information through an actual research interaction or observation.

For example, a teacher interviewed about digital learning describes experiences arising from professional practice. A student completing a questionnaire provides responses influenced by personal circumstances and interpretation of the questions.

An AI-generated participant does not independently possess those experiences or participate in the study as a human respondent.

Its answers are generated through computational processes conditioned on training data, prompts, and model behavior.

Even when synthetic responses resemble empirical observations, they do not establish that the corresponding people participated or expressed those views.

When can synthetic data replace real participant records?

Synthetic data may substitute for genuine records when the task does not require new evidence about actual participants.

The appropriate substitution depends on the evidentiary requirements of the task.

Synthetic data can be highly useful for developing a method without being suitable for answering the substantive empirical question that the method will eventually investigate.

Can AI-generated survey responses replace actual respondents?

Not when the research intends to report genuine survey participation or establish observed population attitudes.

For example, generating 300 artificial student responses does not mean that 300 students completed a questionnaire.

The artificial records may be useful for testing survey logic, data-processing code, or analytical procedures.

However, they cannot independently establish how actual students perceive online learning.

Research examining language-model simulations of human responses has explored whether models can reproduce selected population-level patterns.

Such findings should not be interpreted as demonstrating that AI-generated respondents reliably replace actual participants across populations, contexts, and question types.

The question of whether AI can predict population survey responses requires independent validation and attention to generalizability.

Can synthetic interview transcripts replace real qualitative interviews?

Not when the objective is to document actual human experiences.

Generative AI can produce plausible narratives, quotations, and conversational responses, but these outputs are not testimony from people who participated in the study.

For example, an AI-generated account of a teacher struggling with internet connectivity may sound credible. It does not establish that any particular teacher experienced the described situation.

Such material may support interviewer training, pilot testing, or coding exercises when clearly identified as simulated.

However, presenting fictional transcripts as genuine interviews constitutes fabrication under applicable research-misconduct definitions.

The distinction involving AI-generated fictional participants and interview transcripts is especially important in qualitative research because the origin and context of testimony are central to interpretation.

Can synthetic data replace sensitive participant records for data sharing?

Potentially. Synthetic datasets may allow researchers to share artificial records that reproduce selected characteristics of sensitive empirical data without distributing the original participant-level observations.

For example, a research team might release a synthetic version of a restricted dataset to support preliminary software development or methodological exploration.

However, the artificial dataset may not preserve every property needed for downstream analysis.

It may also retain information that creates privacy risks, particularly when the generation model closely reproduces distinctive source records.

Researchers should evaluate both statistical utility and disclosure risk.

The use of synthetic data for participant privacy should not be interpreted as a guarantee that artificial records are anonymous or safe to release.

Can synthetic data help when participant recruitment is difficult?

They may help with methodological preparation, but they do not automatically resolve the absence of empirical participants.

Researchers may use artificial responses to test instruments, develop analysis code, or investigate possible study designs while recruitment continues.

However, if the research question requires actual participant evidence, recruitment challenges remain a methodological limitation.

Researchers may need to revise the study design, extend recruitment where feasible, adjust the scope of the claims, or report the achieved sample transparently.

Generating fictional participants to satisfy a recruitment target would misrepresent the study if those records were reported as genuine participation.

The legitimacy of synthetic data in research depends on whether the artificial observations are appropriate for the task rather than whether they are convenient.

Can synthetic data establish the reliability or validity of a questionnaire?

Not in the same way as evidence obtained from actual respondents.

Researchers may calculate reliability coefficients or examine factor structures using synthetic questionnaire data, but those results describe the generated records and their underlying assumptions.

They do not independently establish that the instrument measures the intended constructs reliably among real participants.

For example, an AI-generated dataset may exhibit high internal consistency because the generation process was instructed to create strongly related items.

That does not demonstrate that actual students would interpret and respond to the questionnaire in the same way.

Evidence concerning response processes, construct validity, and score reliability generally requires appropriate empirical evaluation.

Synthetic data may support preliminary methodological testing without replacing that validation.

Can synthetic data replace real data for prediction-model training?

Under some circumstances, synthetic data may supplement or substitute for empirical records during particular stages of model development.

For example, artificial records may help test a model architecture or examine behavior under rare hypothetical conditions.

However, models trained on synthetic data may learn artifacts of the generation process rather than relationships that generalize to real populations.

Performance on artificial test data may therefore overestimate real-world effectiveness.

Independent validation using appropriate empirical data is generally important when the intended claim concerns real-world predictive performance.

The use of synthetic data in prediction-model development should be assessed in relation to the target population, deployment conditions, and potential harms from inaccurate predictions.

Can AI-simulated participants support behavioral research?

AI-simulated participants can be studied as outputs of computational models.

For example, researchers may investigate how language models respond to different demographic prompts or whether generated responses reproduce selected patterns found in human studies.

Such work can contribute to understanding model behavior and the limitations of simulation methods.

However, a simulated participant is not automatically an empirically representative human respondent.

Model outputs may depend on prompt wording, training data, model version, and the assumptions embedded in the simulation.

The question of AI-generated versus real participants in behavioral research requires distinguishing studies of AI behavior from studies making claims about actual people.

What happens if synthetic participants are reported as real?

The research record misrepresents the data collection process.

The United States Office of Research Integrity (ORI) defines fabrication as making up data or results and recording or reporting them.

For example, a manuscript claiming that 250 students completed a questionnaire when all 250 responses were generated by AI falsely represents the empirical sample.

Disclosing that AI assisted with analysis would not resolve the false claim about participant recruitment.

Researchers should distinguish synthetic observations from genuine data throughout storage, analysis, and reporting.

Formal findings of research misconduct require the applicable evidentiary and culpability standards to be satisfied, but the underlying misrepresentation remains a serious integrity concern.

How should researchers validate synthetic data intended to substitute for real records?

Validation should examine whether the artificial dataset preserves the properties necessary for the intended application.

For example, a synthetic dataset intended for software testing may need to reproduce variable formats, missing-data conditions, and relevant edge cases.

A dataset intended to approximate empirical observations may require evaluation of distributions, relationships among variables, subgroup characteristics, and downstream analytical performance.

Where the data are derived from sensitive source records, privacy risk should also be assessed.

However, validation against selected empirical properties does not establish that artificial records are equivalent to new human observations.

The process of synthetic-data validation should therefore address fitness for purpose rather than assume universal interchangeability.

Can a study be published without any real participants?

Yes, if the research question and design do not require genuine human participation.

For example, a study may evaluate statistical methods through simulation, investigate AI-generated behavior, or compare synthetic-data generation techniques.

Such research can make legitimate methodological or computational contributions.

However, the manuscript must accurately describe the artificial data and avoid implying that actual participants were recruited.

Findings about the behavior of an AI model should not automatically be generalized to human populations.

The issue is not whether a study contains real participants, but whether its evidence supports the claims it makes.

What should researchers report when synthetic data replace real records?

Researchers should explain why synthetic data were used and which functions they replaced.

Relevant documentation may include the generation method, reference data where applicable, model or software version, parameters, assumptions, validation procedures, and limitations.

The manuscript should distinguish artificial record counts from genuine participant numbers.

If synthetic data were used only for software testing, that role should be described without implying that the generated records contributed empirical evidence.

Where synthetic data were used in model development, researchers should explain how the resulting model was evaluated and whether independent empirical validation was performed.

Appropriate reporting of synthetic data in research papers helps readers understand both the value and limits of the artificial material.

Can Synthetic Students Replace Actual Students in an Online Learning Study?

The synthetic dataset may support methodological preparation, but it cannot replace the human evidence required by the original research question.

If the researcher instead chooses to study how AI simulates student responses, that would be a different research design requiring different claims and validation.

Common Misconceptions About Replacing Human Data With Synthetic Records

Distinguish Replacing a Data File From Replacing Empirical Evidence

Researchers should first determine whether the intended task requires actual human observations.

When the purpose is computational or methodological, synthetic data may provide an appropriate substitute for certain data-processing activities.

When the purpose is to understand real human experiences or behavior, artificial records generally cannot provide equivalent evidence.

The distinction should also guide decisions about preventing AI-simulated participants from being mistaken for empirical evidence.

Synthetic data may reduce dependence on genuine records for particular tasks, but they do not eliminate the need for empirical validation when the research question requires it.

Before Replacing Real Participant Records With Synthetic Data

Frequently Asked Questions About Synthetic Data Replacing Human Participants

Synthetic Data Can Replace Some Research Inputs, but Not Human Experience

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.