Can Generative AI Infer Missing Values in Research Data?

Can AI Work Out What a Participant Probably Would Have Answered?

You have collected academic self-efficacy questionnaires from 400 university students. Most participants answered every item, but some skipped one or two questions. One student answered 11 of the 12 items, leaving a single response blank.

Could generative AI examine the student's other answers and estimate the missing response? If the student consistently selected 4 or 5 on similar items, would entering 4 be a reasonable correction?

The difficulty is that a statistically plausible response is not necessarily the response the participant would have given. Estimating missing information and recovering an actual observation are fundamentally different activities.

Yes, but an Inferred Value Is Not an Observed Value

What AI Can Actually Infer From Missing Research Data

What Does It Mean to Infer a Missing Value?

Inferring a missing value means using available information to estimate an observation that was not recorded. In statistical research, replacing missing observations with estimated values is commonly called imputation.

Suppose a student responds to four academic self-efficacy items with scores of 4, 5, 4, and a missing response. A prediction model might estimate that the missing score is likely to be relatively high because of its association with the observed responses.

That estimate does not establish whether the student would actually have selected 3, 4, or 5. The missing response remains unobserved.

Confusing these two can lead researchers to present model-generated information as though participants actually supplied it.

Is Generative AI the Same as Statistical Imputation?

No. Generative AI, particularly large language models, can help explain and implement imputation procedures, but statistical imputation is an established methodological field with its own assumptions, estimation techniques, and inferential requirements.

A chatbot might suggest that a missing questionnaire response should be 4 because neighboring responses are high. Such a suggestion is not equivalent to a validated statistical imputation procedure.

By contrast, a specified statistical model estimates missing values using relationships in the observed dataset. More sophisticated methods can also account for uncertainty about those estimates.

Generative AI is often most useful as an assistant for selecting, coding, and checking these procedures rather than independently inventing replacements.

What Methods Can AI Help Researchers Implement?

Van Buuren's Flexible Imputation of Missing Data explains why simple replacement procedures often operate under restrictive conditions and why multiple imputation can provide a more principled treatment of uncertainty.

AI can generate code for these methods, but researchers must still justify the chosen model and evaluate its suitability for the dataset.

Why Does the Missing-Data Mechanism Matter?

The reason information is missing can influence whether a proposed estimation method produces defensible results.

Rubin's foundational framework distinguishes missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).

• MCAR: Missingness is unrelated to observed and unobserved data values.
• MAR: After conditioning on relevant observed information, missingness does not additionally depend on the missing values.
• MNAR: Missingness continues to depend on unobserved information after accounting for observed variables.

For example, students with lower academic self-efficacy might be less willing to answer a question about their academic confidence. If this tendency remains related to their unobserved responses after accounting for available predictors, an imputation model assuming MAR may be inadequate.

AI cannot reliably determine the missing-data mechanism simply by examining a dataset. The assumptions require consideration of the study design, collection process, and subject-matter knowledge.

The prior task of identifying missing values and their recorded meanings should therefore be completed before deciding whether estimation is appropriate.

Why Is Multiple Imputation Different From Guessing One Value?

Multiple imputation acknowledges that an unobserved value is uncertain. Instead of creating one supposedly correct replacement, it generates several plausible completed datasets under a specified model.

Each dataset is analyzed separately, and the resulting estimates and uncertainty measures are combined using appropriate pooling rules.

For example, a missing academic self-efficacy response might receive different plausible values across imputed datasets. These differences reflect uncertainty rather than an attempt to identify one definitive answer.

The method is not automatically valid merely because multiple values are generated. Its assumptions, model compatibility, and diagnostics still matter. A collection of arbitrary chatbot guesses does not constitute statistically valid multiple imputation.

Can AI Predict Missing Likert-Scale Responses?

Technically, AI-assisted models can estimate missing ordinal questionnaire responses. However, a five-point Likert item has a restricted set of ordered categories, and the imputation method should respect that measurement structure.

A model that predicts 3.67 for an item permitting only responses from 1 to 5 has produced a numerical estimate, not an observed response category. Rounding it to 4 may appear convenient but introduces another modeling decision.

Depending on the analytical approach, ordinal regression or other compatible imputation models may be more suitable than unrestricted continuous prediction.

Researchers must also distinguish individual item responses from composite scale scores. The most defensible handling of missing items may depend on the instrument's scoring instructions and psychometric properties.

Can AI Validate an Imputation Model?

AI can help generate diagnostic procedures, summarize missingness patterns, and compare alternative modeling approaches. One useful exercise is to temporarily mask selected observed values, estimate them using the proposed method, and compare predictions with the known values.

This can reveal predictive weaknesses, but it does not prove that the model correctly represents the actual missing-data process. Artificially masked observations may differ systematically from observations that were genuinely missing.

Researchers should also examine imputed distributions, relationships among variables, convergence diagnostics where applicable, and sensitivity to plausible alternative assumptions.

Scikit-learn's official documentation describes iterative imputation, which models variables with missing entries using other observed features. It also documents options for posterior sampling, illustrating the difference between deterministic prediction and procedures capable of generating multiple estimates.

The choice of model should follow the intended analysis rather than simply whichever method produces the smallest prediction error.

What Should Researchers Preserve?

Original missing entries should remain identifiable. Researchers should preserve the unmodified dataset, missingness indicators, imputation model specification, predictor selection, software versions, random seeds where relevant, diagnostics, and resulting analytical outputs.

Where AI assisted in choosing or implementing a method, retain the reviewed code and the substantive decisions made by the research team.

The distinction between proposing an estimate and automatically inserting missing values is especially important. An estimate should not silently replace an unobserved response in the master dataset.

Estimating a Missing Academic Self-Efficacy Response

The AI suggests that Item 8 might receive a high score because nearby responses are high. However, the researcher recognizes that item order alone does not establish predictive relationships or item equivalence.

The researcher reviews the instrument's scoring instructions, examines the extent and patterns of missingness, and considers whether the intended analysis requires item-level imputation at all.

If multiple imputation is selected and justified, the researcher uses an appropriate model incorporating relevant observed variables. Several completed datasets are generated, analyzed, and pooled. No single imputed value is presented as the participant's actual response.

Interpretation: AI may help design and implement a missing-data procedure, but the resulting estimates remain model-dependent. The purpose is to support defensible statistical inference, not to reconstruct an answer that was never recorded.

Common Misconceptions About AI-Inferred Missing Values

When Should You Consider AI-Assisted Missing-Value Inference?

Begin by asking whether estimating missing values is necessary for your intended analysis. Some procedures can use incomplete observations directly under appropriate assumptions, while others require a more explicit missing-data strategy.

Do not use AI-generated replacements simply to make the spreadsheet appear complete.

For consequential decisions, document the missing-data rules that require researcher specification, including model choice, predictors, assumptions, and diagnostic criteria.

AI can assist with the technical work, but the methodological responsibility remains with the research team.

Before Using AI to Infer Missing Research Values

Frequently Asked Questions About AI Missing-Value Inference

AI Can Estimate Missing Values, but It Cannot Recover Unobserved Answers

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