Can Generative AI Explain Existing Research Code?

Can AI Help Researchers Understand Code They Did Not Write?

A researcher inherits a Python script from a former project assistant. The code imports survey data, removes certain observations, calculates summary statistics, and generates figures for a manuscript.

The script appears to work, but the researcher does not fully understand its functions, conditions, or data transformations.

Instead of examining every line manually, the researcher asks generative AI to explain what the code does.

The model provides a clear summary, identifies the main functions, and describes the apparent analytical workflow.

But how can the researcher know whether the explanation is correct?

A language model may describe what code appears intended to do rather than what it actually does under particular conditions. It may overlook subtle errors, misunderstand library behavior, or infer a methodological purpose that the original programmer never documented.

The question is whether generative AI can reliably explain existing research code and how researchers should verify those explanations before relying on them.

Yes, AI Can Explain Code, but Its Explanations Must Be Checked

How Generative AI Can Help Researchers Understand Existing Scripts

Why Research Code Can Be Difficult to Understand

Research code is often developed incrementally. A script may begin as a short analysis and gradually acquire additional functions, data cleaning rules, and adjustments introduced during a project.

Documentation may be incomplete, especially when the original programmer assumed that the code would be used only by a small research team.

Researchers may also encounter unfamiliar programming languages, library functions, or analytical conventions.

Even when a script executes correctly, understanding its behavior can require tracing how information moves through several operations.

Generative AI can help make these operations more accessible by translating programming constructs into ordinary language.

However, explanation is not equivalent to independent verification of correctness.

What Can AI Explain About Research Code?

Generative AI may help researchers understand both individual statements and the broader structure of a computational workflow.

It can explain unfamiliar syntax, identify function inputs and outputs, describe conditional operations, and summarize how a script processes data.

These explanations can provide a useful starting point for code review, but they should not be treated as definitive descriptions of runtime behavior.

Explaining What Code Does Is Different From Explaining What It Was Intended to Do

A script may contain operations that do not match the programmer's original intention.

For example, a comment might state that the code removes only incomplete survey responses. However, the actual filter may exclude every record containing any missing value, including responses that remain usable for the intended analysis.

An AI system may repeat the comment's stated purpose without examining whether the implementation matches it.

Researchers should therefore distinguish the code's observable behavior from its apparent purpose.

These elements may align, but they should not be assumed to be identical.

AI can help identify possible discrepancies, while the research team must establish the intended methodology and verify the implementation.

AI May Misinterpret Library Functions

Research scripts often depend on external libraries such as pandas, NumPy, SciPy, statsmodels, or R packages.

Generative AI may explain common functions correctly, but its interpretation can be inaccurate when behavior depends on parameters, library versions, or unusual input conditions.

For example, a statistical function may use a particular default method for handling missing values or calculating standard deviations.

An AI explanation that ignores those defaults could misrepresent the resulting calculation.

Researchers should consult the documentation corresponding to the actual library version and inspect the relevant function arguments.

When the behavior remains uncertain, a small executable test may provide more reliable evidence than another verbal explanation.

AI Can Help Trace Data Transformations

Data cleaning and preparation often involve multiple operations performed in sequence.

A script may import raw data, remove records, recode variables, merge datasets, calculate derived scores, and export an analysis-ready file.

Generative AI can help describe this sequence and identify which variables are affected at each stage.

However, a high-level explanation may omit important details.

For example, the model may state that the script removes duplicate records without explaining how duplicates are defined or which occurrence is retained.

Researchers should inspect row counts, missing-value patterns, variable distributions, and other relevant intermediate outputs.

This helps establish whether the actual transformations match the research team's intended rules.

Can AI Explain Statistical Research Code?

AI can identify statistical functions and explain the general purpose of analytical procedures.

For example, it may recognize code fitting a linear regression model and describe the relationship between predictors and an outcome variable.

However, identifying the function does not establish that the model specification is appropriate.

The code may omit relevant variables, use an unintended reference category, or apply a method that does not account for the study's data structure.

Researchers should distinguish explanation of the implemented computation from evaluation of its scientific validity.

Statistical interpretation requires appropriate methodological expertise and examination of the actual data and model assumptions.

AI Can Help Identify Potentially Destructive Operations

Some scripts modify or delete files, overwrite datasets, or connect to external systems.

Generative AI can help identify statements that appear to perform these operations.

For example, it may flag commands that write to an existing output path or delete files matching a particular pattern.

However, an AI-generated security assessment should not be treated as exhaustive.

Researchers should inspect potentially destructive operations directly and use controlled testing environments when examining unfamiliar code.

Confidential datasets and credentials should not be exposed to untrusted scripts.

How Should Researchers Prompt AI to Explain Existing Code?

A useful prompt should ask the model to explain the actual statements, identify assumptions, and distinguish observed behavior from inferred intention.

It should also request examples that can be tested independently.

This instruction may improve the usefulness of the explanation, but the researcher must still verify consequential statements against actual code behavior.

Should Researchers Ask for Line-by-Line Explanations?

Line-by-line explanations can be useful when learning unfamiliar syntax or investigating a specific operation.

However, they may become cumbersome for long scripts and obscure the broader computational workflow.

A more useful approach may begin with a high-level overview, followed by focused examination of functions that affect important research results.

For example, researchers might first identify the data import, cleaning, analysis, and export stages. They can then examine the cleaning and statistical functions in greater detail.

This approach helps connect individual programming statements to the research procedure as a whole.

How Can Researchers Verify an AI Code Explanation?

Verification should involve evidence beyond the model's own description.

Researchers can inspect the source code, consult official documentation, and execute the script using controlled inputs.

Small test datasets are particularly useful because the expected outputs can be calculated independently.

For example, if AI claims that a function calculates the mean of non-missing observations, researchers can test it using a dataset containing known values and missing entries.

They should also examine edge cases that might produce unexpected behavior.

Where appropriate, automated tests can preserve these checks for future code revisions.

AI Can Help Create Documentation for Existing Research Code

Once a script's behavior has been verified, generative AI can assist with preparing comments, function descriptions, and workflow documentation.

This may be useful when research code must be transferred to another team member or archived for reproducibility.

However, documentation generated before verification may preserve incorrect assumptions about the code.

Researchers should therefore confirm the implementation before accepting AI-generated descriptions as authoritative.

Documentation should explain important inputs, outputs, dependencies, assumptions, and known limitations.

Can AI Explain Code Written by Another Researcher?

Yes, provided the researcher is authorized to share or process the code using the selected AI system.

Research scripts may contain unpublished methods, proprietary algorithms, credentials, confidential file paths, or information about restricted datasets.

Uploading code to an external service may therefore require institutional or contractual authorization.

Where appropriate, sensitive values can be removed and the relevant function isolated for explanation.

However, redaction should be performed carefully because code structure and comments may still reveal confidential information.

The researcher should also preserve attribution and applicable licensing conditions when working with code developed by collaborators or external contributors.

How Does Code Explanation Support Research Reproducibility?

Understanding existing code can help researchers document the analytical workflow and identify operations that affect the results.

For example, a verified explanation may clarify how missing values were handled, which variables were transformed, or how statistical models were specified.

These details can improve the transparency of research methods.

However, an explanation alone does not establish that the code can be executed successfully in another environment or that it produces the same results.

Reproducibility also depends on preserving the tested implementation, relevant data, dependencies, software versions, and execution instructions.

The broader considerations involved in reproducible AI-assisted research programming remain applicable even when AI is used only to explain existing scripts.

Can AI Help Researchers Debug Code They Do Not Understand?

AI explanations may help identify possible causes of errors and clarify how particular functions behave.

However, debugging requires establishing why observed behavior differs from the intended result.

A model may propose a modification that removes an error message while changing the computation.

Researchers should therefore verify that suggested corrections preserve the intended analytical procedure.

This issue is examined further in the reliability of generative AI for debugging research code.

What Are the Limits of AI Code Explanation?

Generative AI may misunderstand complex control flow, external dependencies, dynamically generated behavior, or functions whose implementation is not available.

It may also overlook differences between what the code appears to do and what occurs when it interacts with particular datasets or software environments.

For example, a script may call an external function whose behavior depends on a package version that the model does not know.

Without the relevant documentation or execution environment, the explanation may remain provisional.

Researchers should recognize these limits and avoid treating fluent descriptions as evidence that every operation has been understood.

When AI Misexplains a Data Cleaning Function

The example demonstrates why AI explanations should be evaluated against executable behavior rather than accepted solely because they are clear and plausible.

The script's comment described an intention, while the actual operation had broader consequences for the dataset.

Misconceptions About AI Explanations of Research Code

How to Use AI Explanations Without Mistaking Them for Code Verification

Researchers should use AI explanations as a starting point for understanding unfamiliar scripts.

Begin with a high-level overview, then examine the operations that affect important research results.

Verify the explanation using source inspection, relevant documentation, and controlled tests.

Preserve Verified Explanations as Documentation

Once the code's behavior has been established, researchers may use AI to prepare comments, function descriptions, and workflow documentation.

These materials can help collaborators understand the implementation and support future maintenance.

However, documentation should accurately reflect the tested code and identify any unresolved limitations.

The most useful role for AI is to make code easier to understand while encouraging researchers to verify its actual behavior independently.

Checklist for Verifying AI Explanations of Research Code

Frequently Asked Questions About AI Research Code Explanation

AI Can Make Research Code Understandable, but Its Explanation Is Not Proof

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