Can Generative AI Identify Ambiguous Cases During Data Coding?

Can AI Recognize When a Research Response Is Too Ambiguous to Code?

You ask generative AI to code several hundred open-ended survey responses. Most answers are straightforward, but one participant writes, "The university should have done more."

Does this refer to technical support, teacher training, institutional policies, or something else? The statement expresses dissatisfaction without identifying precisely what the institution failed to provide.

An AI system may nevertheless assign a specific code and present it confidently. Could researchers instead use AI to recognize uncertain cases, explain what makes them difficult, and identify which responses require closer examination?

This capability could be useful when researchers need to prioritize human review without assuming that every apparently clear AI classification is correct.

Yes, AI Can Flag Ambiguous Cases, but It Will Not Detect Every One

How AI Can Recognize Difficult and Ambiguous Coding Decisions

What Makes a Research Response Ambiguous?

A response is ambiguous when its meaning or appropriate classification cannot be established confidently from the available evidence and coding rules. Ambiguity may arise because the respondent's wording supports several interpretations, important context is missing, or category definitions overlap.

Consider a study investigating barriers to online learning. A student writes:

"I couldn't participate because things were difficult at home."

The statement may concern household responsibilities, inadequate study space, financial hardship, family conflict, or another experience. Without additional information, assigning a specific explanation would involve an unsupported inference.

The researcher might classify the response under a sufficiently broad category such as "home-related participation barriers," if that category is defined in the codebook. However, a narrower classification would require evidence that the statement does not provide.

Ambiguity is therefore relative to the research question and coding framework. A response may be sufficiently clear for one analytical purpose while remaining ambiguous for another.

What Different Types of Coding Ambiguity Can AI Help Identify?

Not all difficult coding cases arise for the same reason. Distinguishing their sources helps researchers decide how to respond.

These situations do not always require the same solution. Some can be addressed through multiple coding, others through clearer codebook definitions, and some must remain unresolved because the data do not contain enough information.

Can AI Distinguish Ambiguous Data From an Ambiguous Codebook?

Potentially, but the distinction requires careful examination.

Ambiguity in the data occurs when a participant's statement does not provide enough information to support a particular interpretation. Ambiguity in the codebook occurs when the classification rules do not adequately distinguish categories.

For example, "My internet connection disconnects during every lecture" is reasonably clear as a description of connectivity problems. If AI hesitates between "technical infrastructure" and "internet access," the difficulty may reflect overlapping category definitions rather than unclear participant meaning.

Researchers should examine whether the difficulty can be resolved by refining the human-developed codebook before attributing uncertainty to the respondent.

Can AI Identify Multiple Plausible Codes?

Yes. A model can be instructed to identify more than one potentially applicable code and explain the textual evidence supporting each possibility.

Suppose a teacher says, "We were never shown how to use the software, and when something went wrong, nobody could help."

The statement supports two distinguishable concerns: insufficient training and unavailable technical support. If the codebook permits multiple labels, the appropriate response may be to assign both rather than mark the statement as ambiguous.

This illustrates an important distinction: multiple valid codes do not necessarily indicate uncertainty. A passage can clearly express several concepts.

Researchers should reserve ambiguity flags for cases in which the available evidence or coding rules do not support a defensible decision. Otherwise, a multi-label coding framework may generate unnecessary review requests.

Can AI Recognize When It Should Not Assign a Code?

AI can be instructed to abstain from classification when the available evidence is insufficient. This is sometimes called selective classification or abstention.

For example, a coding instruction could require the model to select an approved category only when the response contains sufficient supporting evidence. Otherwise, it should return "requires review" and identify the missing information.

However, including an abstention option does not guarantee that the model will use it appropriately. Models may still produce confident-looking classifications when uncertainty is substantial.

Research on large language model abstention supports this caution. Cocchieri and colleagues (2026), examining medical question answering, found that models frequently committed to answers despite uncertainty. Although medical question answering differs from research coding, the findings illustrate why an explicit option to abstain should be evaluated rather than assumed to work. The published study examines this behavior directly.

For research coding, the practical implication is to test whether AI correctly flags difficult cases and whether it also overlooks cases that human reviewers consider ambiguous.

Should AI Explain Why a Response Is Ambiguous?

Providing a brief explanation can make ambiguity flags more useful. Instead of returning only "uncertain," the model might identify which category definitions overlap or what contextual information is missing.

Consider the statement, "The instructor did not help when I couldn't access the activity."

An AI explanation might identify two possibilities: the participant experienced a platform access problem, and the instructor did not provide assistance. The researcher can then determine whether both codes are supported or whether additional context is necessary.

Such explanations should be treated as claims to examine, not transparent records of the model's internal reasoning. A plausible explanation can accompany an incorrect classification.

One useful safeguard is to require the model to reproduce the exact words supporting each proposed code. Researchers can then check whether the evidence actually supports the interpretation.

Can AI Confidence Scores Identify Ambiguous Cases?

Confidence scores may help prioritize uncertain cases, but they should not automatically be interpreted as probabilities that a coding decision is correct.

A model might assign a code and report 95% confidence. Unless the score has been evaluated and appropriately calibrated for the task, that number does not establish a 95% probability of correctness.

Research on uncertainty estimation demonstrates that confidence measures differ in how effectively they identify errors. The LM-Polygraph benchmarking study, published in Transactions of the Association for Computational Linguistics, examined uncertainty estimation and confidence calibration across multiple language-model tasks and datasets. Its findings emphasize the importance of evaluating uncertainty measures rather than assuming that a numerical score is inherently meaningful. The benchmark study provides further methodological detail.

Researchers considering AI confidence scores for coding decisions should distinguish a model's self-reported certainty from empirically demonstrated classification reliability.

What Does Research Evidence Suggest About Difficult Text Annotation?

A 2026 study published in Amplifying Research, titled "Evaluating large language models on a hermeneutically complex text annotation task," examined whether language models could classify nuanced expressions concerning depression in online discussions.

The researchers evaluated GPT-4o mini and Llama 3.3 70B using different prompting strategies. They found that models performed better on material that was also easier for human annotators, while more difficult passages remained challenging. Model confidence was positively associated with annotation accuracy in their evaluated setting, suggesting potential value for human-review prioritization.

However, the findings do not establish that confidence scores reliably identify every ambiguous research response or that the same performance will occur across different coding frameworks. The published article is particularly relevant because it examines a text-annotation task involving complex interpretation rather than only straightforward topic classification.

Another 2026 study, "Meeting the machines half-way: Testing LLMs for qualitative coding of values in texts," found that human annotations were preferred in a difficult qualitative coding task, even though agreement among human coders was low. The researchers' follow-up interviews highlighted the role of linguistic nuance, cultural experience, and implicit assumptions in coding decisions. These findings reinforce the need to examine why a case is difficult rather than treating disagreement as an error that must always be eliminated.

How Should Researchers Evaluate AI Ambiguity Detection?

Researchers should first define what counts as a review-worthy case for their particular coding task. The reference criteria might include insufficient textual evidence, unresolved category overlap, missing context, or disagreement that cannot be resolved through existing decision rules.

A human-reviewed evaluation set can then be used to examine whether AI correctly identifies such cases.

Two questions are especially useful:

• How many genuinely review-worthy cases does AI identify? This concerns the recall of ambiguity detection.
• How many AI-flagged cases genuinely require review? This concerns the precision of ambiguity detection.

A model that flags nearly every response may identify most ambiguous cases but create an impractical review workload. A model that flags only a handful may appear efficient while overlooking important uncertainty.

Researchers should therefore evaluate the balance between review workload and missed ambiguous cases. The appropriate balance depends on the study's purpose and the consequences of incorrect classifications.

They should also consider that some apparently unambiguous AI classifications may be wrong. Ambiguity detection is not a substitute for evaluating coding accuracy.

How Should Ambiguous Cases Be Managed During Large-Scale Coding?

A practical workflow should preserve the original response, proposed code, ambiguity flag, reason for review, and final researcher decision.

When possible, the researcher should distinguish between cases resolved by applying existing rules and cases requiring changes to the coding framework.

If several flagged responses reveal the same category-boundary problem, the issue may be systematic rather than isolated. The researcher should consider whether the codebook needs revision and whether previously processed responses require reassessment.

In large-scale text coding, a structured review queue can help researchers prioritize difficult material while preserving traceability.

However, researchers should not rely exclusively on flagged cases. An independently selected sample of unflagged responses is also important for estimating how often the system fails to recognize ambiguity or makes incorrect assignments without signaling uncertainty.

Can Some Ambiguity Remain Unresolved?

Yes. Not every ambiguous statement can be resolved through better prompting, additional model runs, or discussion among coders.

If a respondent writes, "It was not what I expected," and provides no further explanation, the available evidence may not establish what was expected or why the experience differed.

Researchers should not manufacture specificity merely to complete a classification table. Depending on the analytical design, the response may receive a broad descriptive code, remain unclassified for a particular variable, or be retained as an unresolved case.

Where participant follow-up is feasible and ethically appropriate, clarification may be possible. Otherwise, acknowledging the limits of the data is more defensible than presenting an unsupported interpretation as a finding.

Using AI to Flag Ambiguous Student Survey Responses

Step 1: Define What Should Trigger a Review

The researcher establishes four review criteria: insufficient information, unclear references, unresolved category overlap, and possible meanings that depend on missing context.

The instructions also clarify that a response should not be flagged merely because it supports more than one code.

Step 2: Examine the Proposed AI Outputs

The fifth response demonstrates why ambiguity flags alone are insufficient. AI may fail to recognize its own misunderstanding and assign an incorrect code without requesting review.

Step 3: Evaluate Ambiguity Detection

Suppose the researcher independently reviews 200 responses and determines that 40 require additional coding review under the predefined criteria.

The AI flags 50 responses. Of these, 30 genuinely require review, while 20 are sufficiently clear under the codebook. The system also misses 10 review-worthy cases.

The researcher concludes that the flags may help organize review work, but they cannot be used as the sole mechanism for identifying difficult responses.

Step 4: Revise the Review Procedure

The researcher examines the missed cases and identifies whether the problem concerns sarcasm, missing context, category overlap, or another recurring difficulty.

The instructions may be refined, but the revised procedure must be evaluated using material not used to develop the changes.

The final workflow includes review of flagged cases and a separate selection of unflagged responses. Unresolved cases remain documented rather than being assigned unsupported labels.

The objective is not to make AI appear certain about every response. It is to identify where coding decisions require stronger evidence while recognizing that the model may fail to detect its own errors.

Misconceptions About AI Detection of Ambiguous Coding Cases

How to Use AI Ambiguity Flags Without Overtrusting Them

AI ambiguity detection is most useful as a way to direct researcher attention toward potentially difficult coding decisions. It should not be treated as a guarantee that all other classifications are correct.

Researchers should document why cases were flagged, how they were resolved, and whether the review revealed broader problems with the coding framework.

Where uncertainty remains unresolved, it may be appropriate to preserve that status rather than manufacture a definitive classification. A transparent unresolved decision is often more defensible than a confident but unsupported label.

The broader question of whether AI coding can proceed without human review should be considered separately. Even a useful ambiguity-detection procedure does not eliminate the need to establish coding quality.

Before Using AI to Flag Ambiguous Coding Decisions

Frequently Asked Questions About Ambiguity in AI Research Coding

AI Can Flag Uncertainty, but It Cannot Guarantee That It Recognizes Its Own Errors

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