01 · The Question
If Both Are Software, Why Treat Generative AI Differently?
Researchers have trusted software with important work for decades. Statistical packages estimate models, reference managers organize citations, spreadsheets calculate values, qualitative analysis programs manage coding, and specialist applications process everything from genomic sequences to satellite images.
Then generative AI arrives, also as software, and researchers are repeatedly told to verify its outputs, protect confidential information, disclose certain uses, and remain cautious about what it produces. Why should a generative AI assistant be treated differently from the other software already sitting on a researcher's computer?
The answer is not simply that generative AI is newer. The more important difference lies in what the software is designed to produce and how predictable, reproducible, and evidentially grounded that output is.
03 · What You Need to Know
What Makes Generative AI Different From Familiar Research Software?
Generative AI Is Defined by Its Capacity to Generate
Generative artificial intelligence is part of the broader category of artificial intelligence used in research. The U.S. National Institute of Standards and Technology describes generative AI in terms of models that emulate the structure and characteristics of input data to generate derived synthetic content. That content can include text, images, video, audio, and other digital material.
For a researcher, “generate” is the operative word. A generative system does not merely retrieve a stored sentence or apply a formula supplied by the user. Depending on the system, it can produce a new paragraph, computer program, image, summary, translation, proposed classification, or response to a question.
This makes generative AI remarkably flexible. The same general-purpose system may help brainstorm terminology in one interaction, explain statistical code in another, revise prose in a third, and summarize a document in a fourth. Traditional research applications are often much more tightly coupled to particular operations.
Traditional Software Usually Gives You a More Explicit Computational Relationship
Suppose you enter a set of numbers into statistical software and ask it to calculate their mean. The operation is defined mathematically. Given the same numbers and the same operation, you expect the same answer. You can independently calculate the mean and verify the result.
Or suppose you ask a reference manager to format a stored bibliographic record according to a particular citation style. The software applies rules to information in its database. Errors can certainly occur, especially when the stored metadata are wrong, but the software is not ordinarily inventing a new article because its title seems statistically plausible.
A general-purpose generative AI system behaves differently. Ask it to explain why a result occurred, draft a literature summary, propose hypotheses, or generate code, and it produces a response based on patterns represented in its model and the context supplied to it. The response may be useful, but the relationship between your request and its answer is not equivalent to executing a conventional deterministic formula.
| Feature |
Traditional research software |
Generative AI |
| Typical purpose |
Perform defined operations or support specialized workflows |
Generate or transform content in response to input |
| Output |
Often constrained by explicit functions, algorithms, rules, or stored data |
Often newly generated text, code, images, audio, or other content |
| Repeatability |
Often designed to return the same result from the same operation and inputs |
May return different outputs to the same or similar prompts, depending on the system and settings |
| Source relationship |
Often operates directly on data or records supplied to it |
Generated content may not provide a transparent path to the information underlying each claim |
| Typical error |
May reflect bad input, software defects, incorrect settings, or misuse of a method |
Can additionally produce plausible but unsupported or false generated content |
| User interaction |
Often requires commands, menus, parameters, formulas, or defined workflows |
Can accept open-ended natural-language instructions |
| Verification |
Often possible against documented procedures, formulas, data, or known expected results |
Frequently requires checking generated claims against independent evidence or authoritative sources |
These are tendencies, not clean technological borders. Some traditional software contains probabilistic methods. Some AI systems can behave predictably under controlled conditions. Many contemporary applications now combine conventional functions with generative AI. The useful distinction therefore concerns the particular function being used rather than the product label.
Probabilistic Does Not Mean Random
Generative AI is often described as probabilistic. That does not mean it simply produces arbitrary answers. A generative model has learned statistical structure from data and uses that learned structure, together with the user's input and other system context, to produce an output.
For text-generating systems built around large language models, generation involves predicting tokens based on preceding context. The resulting output can be highly coherent because the model has learned extensive patterns in language and other represented information.
But coherence is not the same thing as evidential verification. A sentence can be an excellent continuation of a linguistic pattern while still making a false claim.
Generative AI Can Produce Something That Looks Like Evidence Without Being Evidence
This is one of the most consequential differences for researchers.
If a generative AI system is asked for references, it may sometimes provide genuine publications. It may also produce incorrect bibliographic details or entirely nonexistent citations. If asked to summarize a paper it has not actually been given or reliably retrieved, it may generate a plausible account that does not accurately represent that paper.
The problem is subtle because the output can have all the surface features researchers associate with credible scholarship: author names, dates, technical vocabulary, methodological terminology, journal titles, even apparently precise explanations.
Watch Out
A generated citation is not a retrieved citation. A generated summary is not necessarily a source-grounded summary. A generated explanation is not evidence that the explanation is true. Verify scholarly claims against the underlying sources.
The European Commission's living guidelines on responsible generative AI use in research explicitly identify hallucinations, inaccuracies, invented citations, incorrect summaries, bias, confidentiality, and related concerns as issues researchers need to manage.
Traditional Software Can Be Wrong Too
It would be a mistake to turn this comparison into “ordinary software is reliable; generative AI is unreliable.” Research software can produce seriously misleading results.
A statistical package will faithfully execute an inappropriate model if the researcher specifies one. Spreadsheet errors have affected published work. Reference managers propagate incorrect metadata. A programming error can corrupt an analysis while producing perfectly tidy output. Software does not rescue a researcher from poor methodological decisions.
The difference is the error profile. With conventional research software, researchers often know the intended operation and can inspect the inputs, parameters, formulas, or procedures involved. Generative AI can introduce another category of problem: it may generate an apparently meaningful answer whose factual or evidential basis is unclear.
Natural-Language Interaction Changes the Relationship Between Researcher and Software
Most traditional research software exposes its boundaries rather quickly. Statistical software expects variables, commands, models, or menu selections. A reference manager manages references. A spreadsheet expects formulas and structured data.
Generative AI can answer questions across domains using ordinary language. That flexibility lowers the technical barrier to asking for sophisticated work. You can request Python code without knowing much Python, ask for a statistical interpretation without mastering the statistical method, or request a literature synthesis without knowing the literature well enough to notice what is missing.
That is simultaneously useful and dangerous. It can support learning and accelerate work, but it can also create an evaluation gap: the system may produce an output more sophisticated than the user is capable of independently assessing.
This is one reason some research responsibilities should not be delegated completely to AI. If you cannot determine whether a consequential output is valid, asking a fluent system to produce it does not solve the underlying expertise problem.
Generative AI Is Often General-Purpose Rather Than Method-Specific
Traditional research software is frequently built around a relatively bounded purpose. A structural equation modeling package, qualitative coding environment, reference manager, or geographic information system may be complex, but its intended domain constrains what users reasonably expect from it.
A general-purpose generative AI assistant has much broader apparent scope. It may discuss epidemiology, generate R code, revise a theoretical framework, explain regression assumptions, translate an interview excerpt, and suggest a manuscript title within the same conversation.
That versatility does not mean it possesses equal reliability across those tasks. Researchers need to evaluate capability at the level of the task, not infer competence in one domain from impressive performance in another.
The Boundary Is Becoming Harder to See
The distinction between “AI tool” and “traditional software” is increasingly blurred because generative AI features are being incorporated into familiar applications. Writing software may generate text. Search interfaces may provide generated answers. Coding environments may propose entire functions. Reference and literature platforms may add conversational interfaces.
As a result, classifying the entire application is less useful than asking what a particular feature does.
Ask what product you are using
This tells you where the feature lives.
Ask what the feature actually does
This tells you what kind of scrutiny the output requires.
A familiar interface should not cause researchers to overlook an embedded generative function. Conversely, the presence of AI somewhere inside a product does not mean every operation performed by that product is generative AI.
Different Output Characteristics Require Different Safeguards
Generative AI does not require additional scrutiny because researchers should fear unfamiliar technology. It requires scrutiny proportionate to its characteristics and the task for which it is being used.
If a system can generate plausible unsupported claims, then factual verification becomes important. If prompts or uploaded files are processed by an external service, data governance matters. If generated text enters a manuscript, authorship and disclosure policies may matter. If outputs can vary, reproducibility may require attention to the system, version, settings, prompts, and workflow.
This is why generative AI may require safeguards that differ from those used with traditional software. The safeguards should follow the risk rather than the fashionable label attached to the technology.
07 · A Quick Checklist
Before Relying on a Generative AI Tool, Check the Function
Before relying on the output, check:
Determine whether the feature is generating content, retrieving information, calculating a result, or combining several of these functions.
Identify what evidence, data, or sources the output is actually grounded in.
Do not interpret polished language, citations, numerical precision, or technical terminology as proof of correctness.
Verify consequential factual claims against authoritative sources rather than asking the same generative system to confirm itself.
Check whether confidential, personal, proprietary, unpublished, or otherwise protected information may be entered into the system.
Consider whether variation in generated outputs affects reproducibility or documentation of your workflow.
Verify current institutional, funder, journal, and publisher policies relevant to your intended use.
Use consequential generated outputs only when you can independently evaluate whether they are appropriate.
