Some beginners in a controlled study struggled to understand code generated with code-focused language models. Separately, a benchmark found that even the best tested model did not answer every question about static program semantics correctly. Those findings point to real limits—but they do not show that AI code is generally unreadable, or that professional developers cannot understand it.
What does “AI code humans can’t understand” actually mean?
It can refer to at least three different questions: whether a person can read generated code, whether a model can identify what a program does, and whether generated code behaves correctly when run. These are related, but they are not interchangeable. A code-generation result does not by itself measure human readability, and a model’s ability to answer a semantics question is not a test of runtime correctness.
The studies available here use different populations, tasks, and methods. Their results should be read separately, not combined into a single estimate of how often AI code is hard to understand.
What have studies found about people reading AI-generated code?
Beginners struggled in a controlled study
A 2024 CHI study examined 120 beginning programmers across three academic institutions as they prompted, edited, and interacted with Code LLMs. The authors reported that participants often struggled to understand generated code and evaluate whether it was correct. This is direct evidence that AI-generated code can be difficult for some readers in that setting, not evidence that all developers—or even all beginners—cannot understand it.
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The study’s population matters: beginning programmers may have less experience recognizing unfamiliar patterns, tracing execution, or spotting subtle errors. The result does not establish how experienced developers would perform, nor does it provide a universal rate of unreadable AI code. Read the CHI 2024 study.
Comprehension depends on the reader and the task
“Readable” is not a property that can be inferred just from whether code was generated by AI. It depends on such factors as the reader’s experience, the code’s language and complexity, project context, and what the reader is asked to explain or verify.
A 2024 ACM study used eye-gaze data from 27 participants completing 16 short code-comprehension tasks to predict comprehension and perceived difficulty. It illustrates that comprehension can be studied as a task-specific outcome; it does not show that AI-written code is inherently harder to read, or supply a general readability score for generated programs. Read the eye-gaze study.
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Can an AI understand the code it writes?
A 2026 benchmark, SemBench, tested whether language models could answer questions about static properties of C programs. It contains 1,000 programs and 15,404 semantic questions, and the study evaluated 16 models across seven model families. Questions cover properties such as data dependencies, function reachability, dead code, dominators, and variable liveness.
The best tested model scored 80.42% overall accuracy on this benchmark. Across the evaluated models, the authors report failure rates ranging from 19.58% to 86.01%. These are results on SemBench’s particular programs and questions—not a general code-correctness rate, a measure of human readability, or proof that a model either fully understands code or understands nothing.
Performance also varied substantially by semantic category. That matters because a model may handle one kind of program property better than another; an overall benchmark score can conceal these uneven abilities. Read the SemBench study.
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What do AI coding studies measure—and what do they not?
| Evidence | What was studied | What the result supports | What it does not establish |
|---|---|---|---|
| CHI 2024, 120 beginning programmers at three institutions | Prompting, editing, and interaction with Code LLMs | Some beginners in the study struggled to understand generated code and judge its correctness. | That professional developers cannot read AI code, or a universal frequency of unreadable output. |
| SemBench, 2026 | 16 models answering 15,404 static-semantics questions about 1,000 C programs | Model performance on the benchmark’s questions was imperfect and varied by semantic category. | Human readability or the runtime correctness of AI-generated code. |
| Google Research / ICSE 2024, 32 participants | An IDE conversational interface intended to help users understand code | A particular AI-assisted interface aided task completion more than web search in the study; results differed between students and professionals. | That AI explanations are always correct or that every code assistant improves comprehension. |
| ACM 2024, 27 participants and 16 short tasks | Eye-gaze data during code-comprehension tasks | Comprehension and perceived difficulty can be studied as task-specific outcomes. | That generated code is inherently more difficult to read. |
The distinction is useful when evaluating claims about AI coding. Generation quality, semantic reasoning, runtime behavior, and human comprehension require different tests. A result from one category should not be presented as a result from another.
Can an AI explanation help you understand code?
A 2024 Google Research / ICSE study evaluated an IDE conversational interface based on GPT-3.5-turbo. It could explain selected code, APIs, domain terms, and API usage. With 32 participants, the authors reported that the interface aided task completion more than web search, with benefits and use differing between students and professionals.
That finding is evidence about one assistance design in one study, not a guarantee that a generated explanation is accurate. An explanation can make unfamiliar code easier to approach, but it is still another model output to check against the source, project context, and observed behavior. Read the Google Research study.
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How to check code from an AI coding assistant
The cited studies do not establish one review checklist as best. Still, because generated code and explanations can be wrong—and beginners in one study found correctness difficult to judge—treat AI output as a proposed change that needs ordinary engineering verification.
- Read the change in context. Trace what the code receives, what it changes, and what it returns. Check how it fits the surrounding functions, types, conventions, and requirements rather than reviewing an isolated snippet.
- Ask for a focused explanation when useful. Have the assistant explain a specific function, dependency, or control-flow decision. Then compare the explanation with the actual code; do not treat confident wording as proof.
- Run relevant tests. Use the project’s existing tests and add cases for expected behavior and important edge cases. Passing tests provide evidence for the cases covered, not a proof that all behavior is correct.
- Use static analysis where appropriate. Linters, type checkers, and static-analysis tools can surface issues without relying on the model’s explanation. Their findings are another verification input, not a substitute for understanding project requirements.
- Review the final diff before merging. Confirm that the change does only what was requested, handles errors and boundary conditions appropriately, and introduces no unexplained dependencies or behavior.
These are practical verification steps, not a workflow whose superiority was tested by the studies summarized above. SemBench’s focus on static program properties underscores why deterministic analyses can be useful for some questions, while human review remains necessary for intent and project context.
What the evidence does—and does not—say
The strongest grounded conclusion is narrower than the headline: some beginning programmers struggled with generated code in a controlled study, and tested language models made measurable errors on a benchmark of static-semantic questions. A separate study found that a particular conversational IDE tool helped participants complete code-understanding tasks more than web search, while an eye-gaze study shows that human comprehension is task-dependent and measurable.
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Together, these findings justify checking AI-generated code rather than trusting it on appearance or explanation alone. They do not establish that AI code is generally beyond human understanding, that professional developers cannot read it, or that models have no understanding of programs.
Further reading on algorithm understanding
An AAAI 2025 paper proposes a hierarchical scale for evaluating human and AI understanding of algorithms. It offers a broader way to frame levels of understanding, but it is not direct evidence about the readability of AI-generated source code. Read the AAAI paper.
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