I Asked ChatGPT to Find My Professor Profile. It Failed. Here's Why It Matters.

by Alvin Blackshear  |  Historian & Researcher   <ablackshear@gmail.com>

Why Historians Should Never Trust "I Couldn't Find It."

I asked ChatGPT to find my profile on Rate My Professor. It told me, with no hedging at all, that it could not find a professor profile under that name. That answer was wrong. When I added the name of the college where I taught, the model found my profile immediately, in the same database it had just searched a moment before.

When I asked why its first answer had been incorrect, ChatGPT explained that the initial search had been too narrow and that it had overstated the result. It went further, telling me that it had not been appropriate to state definitively that no profile existed. That admission is more interesting than the original mistake. It names, almost exactly, the failure that researchers of every kind run into constantly: absence of evidence quietly becomes evidence of absence, and a system built to retrieve information ends up asserting a negative it has no real basis for.

This is a small experiment. But small experiments are often where large problems become visible. What follows is an attempt to take this one seriously, not as a complaint about a chatbot, but as a case study in how retrieval systems fail, why those failures are harder to catch than ordinary hallucinations, and what a better evaluation standard would look like.

The Failure Was Not a Hallucination

It is worth being precise about what did not happen here. The model did not invent a fake Rate My Professor page. It did not fabricate reviews, ratings, or a biography. Hallucination, in the sense the AI research community usually means it, involves generating content that has no basis in the retrieved evidence or in reality. That is not what happened in this case.

Instead, the model committed a quieter and, in some ways, more dangerous error. It ran an incomplete retrieval, treated the incomplete result as sufficient, and then converted that insufficient evidence into a categorical claim. Four distinct failures stacked on top of each other:

1. The retrieval step was incomplete, since the search used only a name and not the additional identifying detail (the institution) that would have disambiguated the result.
2. The evidence gathered was insufficient to support any firm conclusion, positive or negative.
3. The model's confidence should have been low, given how thin the search actually was.
4. The final answer failed to reflect that low confidence, presenting a tentative non-result as though it were a settled fact.

Hallucinations tend to draw attention because they are often vivid and easy to catch. A fabricated citation or an invented quote can be checked against a source and shown to be false. This kind of error is harder to catch precisely because it looks like careful, responsible behavior. The model appeared to be reporting a fact rather than making one up. It sounded modest. It just was not modest enough about the right thing.

Why This Matters

Strip the incident down to its underlying logic and it reads like this: I searched. I did not find it. Therefore it does not exist. Stated plainly, that reasoning is invalid, and most people would recognize the problem immediately if it were presented as a syllogism in a logic class. Yet this is exactly the shortcut that retrieval systems take when they are not explicitly designed to resist it.

Anyone who has done serious research recognizes this pattern because it is the default condition of research itself. Archives are incomplete. Metadata is inconsistent. Names appear under variant spellings, married names, initials, or transliterations. Sources are catalogued under headings that make sense to an archivist but not to a later researcher. Databases frequently require an additional piece of context, an institution, a date range, a middle name, before they will surface a record that has been sitting there the entire time.

None of this is exotic. It is the ordinary condition of information work. The question is not whether an AI system will encounter it. The question is whether the system is built to recognize when it has hit this wall, or whether it will simply report the wall as an empty room.

Historians Have Solved This Problem for Centuries

There is a useful comparison to be made with historical methodology, which has spent a very long time developing habits of mind for exactly this situation. A historian who cannot find a record does not usually conclude that the record never existed. Instead, the working assumption is procedural: did I search enough archives, are there alternate spellings of this name, did the person's name change through marriage or naturalization, is this source catalogued under a different heading, is there another repository that might hold a more complete record.

The distinction that matters here is between two very different sentences. "It doesn't exist" is a conclusion about the world. "I haven't found evidence yet" is a conclusion about the search. Good historians are trained, often through painful experience, to default to the second sentence and to treat the first as a claim that requires an extraordinary amount of additional work before it can be responsibly made.

This is not a minor stylistic habit. It is the entire discipline's defense against a kind of error that predates computers by centuries: mistaking the limits of one's own search for the limits of what actually happened.

What Actually Failed

Returning to the four evaluation points named earlier, each deserves a closer look from an evaluator's perspective rather than a user's perspective.

Retrieval was incomplete because the search relied on a single identifier, a name, in a database where names alone are frequently ambiguous or thin. A single search pass is rarely sufficient in any system where entities can share names, use variant spellings, or be indexed under partial records.

Evidence was insufficient because a single unsuccessful query does not constitute a thorough investigation. One null result from one search string tells a system almost nothing about whether a record exists elsewhere in the same database under a slightly different query.

Confidence should have been low because the retrieval process itself provided no strong signal either way. Low retrieval coverage should map to low confidence in any well calibrated system, and a wide gap between actual coverage and stated confidence is itself a measurable defect.

The final answer should have expressed that uncertainty rather than erasing it. The correct output was never "no profile exists." The correct output was closer to "I could not locate one with the search I ran, and here is what might help me look further."

How a Better AI Should Respond

It is useful to imagine the same interaction handled well. Instead of stating that there is no professor profile, a more careful system might have said something like this: I could not locate one using your name alone. If you can tell me the institution where you taught, I can search more precisely.

Notice what that sentence does. It preserves uncertainty instead of erasing it. It invites the user to supply the missing retrieval key rather than closing the inquiry. It treats the null result as information about the search process, not information about the world. This is a small rewording, but it represents a genuinely different epistemic posture, one that treats retrieval as provisional until proven otherwise.

Why This Matters for RAG Systems

This incident connects directly to a broader question about how Retrieval-Augmented Generation (RAG) systems are built and evaluated. RAG systems are only as good as the retrieval pipeline underneath them, and that pipeline depends on several interacting components: retrieval quality, metadata structure, search strategy, query expansion, ranking, and the underlying coverage of the source material itself.

Recent research on RAG evaluation has tried to formalize exactly this dependency. A comprehensive review of RAG evaluation dimensions by Knollmeyer and colleagues works through how retrieval quality, question types, and evaluation metrics interact, and it makes a case that evaluation frameworks need to look separately at retrieval performance and generation performance rather than treating a RAG system's output as a single undifferentiated block. That separation matters here, because the language model itself did not fail at composition or fluency. The retrieval layer beneath it simply stopped searching too early, and the generation layer then dressed that shortfall up as a confident answer.

Work on uncertainty-aware retrieval reinforces the same point from a different angle. Research on uncertainty-aware dynamic retrieval argues that retrieval decisions should be driven by the model's own uncertainty rather than by fixed, one-shot retrieval rules, so that a system recognizes when its confidence is too low to stop searching. Adaptive multi-source retrieval frameworks make a related argument, showing that incorporating query complexity and confidence-aware fusion allows a system to keep pulling in additional sources precisely in the situations where a single retrieval key, like a name without an institution, is not enough to settle the question. Hypothesis-and-verification approaches to retrieval push this further still, treating an initial null result not as a final answer but as a hypothesis to be tested against additional evidence before any conclusion is reported. Self-evaluation approaches to agentic retrieval add another layer, having the system review its own retrieval strategy before committing to a final answer, which is exactly the internal check that would have caught this failure before it reached the user.

None of this is exotic academic theorizing removed from practical stakes. It describes, almost mechanically, what would have needed to happen for ChatGPT to say something more honest than "no profile exists."

An AI Evaluation Rubric

It is possible to turn this single incident into a reusable evaluation checklist, one that applies whenever an AI system reports that something cannot be found. Work on faithfulness-aware uncertainty quantification for fact-checking RAG outputs provides useful scaffolding here, since it separates the question of whether an answer is faithful to its retrieved evidence from the separate question of whether that evidence is factually sufficient in the first place. Applied to a "not found" claim, the following questions become a practical audit:

Were multiple search strategies attempted, or did the system stop after a single query. Were alternate identifiers used, such as an institution, a date, or a variant spelling. Was metadata expanded to account for inconsistent cataloguing. Was uncertainty reported explicitly, rather than folded silently into a flat statement. Did the model distinguish between "not found" and "does not exist." Could one additional retrieval key plausibly change the result.

That last question turned out to be the entire story in this case. One additional identifier, the name of the institution, was all it took to convert a false negative into a correct answer. A system that had asked itself that question before answering would have caught its own mistake.

Implications Beyond Historians

This failure mode is not confined to historians looking up their own teaching profiles. It shows up wherever retrieval sits underneath a serious decision. Legal researchers checking whether a precedent exists face the same risk of concluding "no such case" from an incomplete search of case law. Genealogists working with inconsistent historical spellings run into it constantly. Medical literature searches can miss relevant studies indexed under different terminology, with consequences far more serious than a mistaken professor lookup. Journalists checking whether a claim has prior reporting, researchers conducting literature reviews, and enterprise knowledge bases used inside companies all depend on retrieval systems that can quietly convert "I didn't find it in my index" into "it isn't true." A recent Nature article on synthesizing scientific literature with retrieval-augmented language models makes a related point in the scholarly context specifically, arguing that trustworthy synthesis of academic evidence depends on retrieval systems that are transparent about their own coverage limits rather than presenting synthesized answers as though they rested on complete evidence.

In every one of these domains, the underlying mechanism is identical to what happened with a single Rate My Professor search. One missing retrieval key can completely change the answer, and a system that does not know how to say "I haven't looked hard enough yet" will eventually say something false with total confidence.

Conclusion

Return to where this started. ChatGPT eventually found my profile once I supplied the one piece of context it needed, the institution where I taught. The information had been there the entire time. The system's retrieval capability was never really in question. What failed was something quieter and, in the end, more consequential: the model treated an incomplete search as a complete answer and reported a negative conclusion before it had exhausted reasonable ways of looking.

The most important lesson here was not that ChatGPT failed to find my professor profile. It was that, for a moment, it mistook an incomplete search for a finished one. For historians, for researchers in any field, and for anyone building AI systems meant to be trusted with real questions, that distinction is not a technicality. It is the whole matter.

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References

Asai, Akari, et al. "Synthesizing Scientific Literature with Retrieval-Augmented Language Models." Nature (2026). https://www.nature.com/articles/s41586-025-10072-4

Fadeeva, Ekaterina, et al. "Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval-Augmented Generation." Findings of ACL 2026. https://aclanthology.org/2026.findings-acl.338

Knollmeyer, Simon, et al. "Evaluating Retrieval Augmented Generation: A Comprehensive Review of Evaluation Dimensions, Question Types, and Application." SN Computer Science 7 (2026). https://link.springer.com/article/10.1007/s42979-026-05134-x

Wu, Haiyan, et al. "Reflective RAG: Self-Evaluation Driven Strategy Optimization in Agentic Retrieval-Augmented Generation." Findings of ACL 2026. https://aclanthology.org/2026.findings-acl.648

"AdaKG-RAG: Adaptive KG-guided Retrieval-Augmented Generation with Hypothesis-and-Verification Retrieval for Multi-hop Question Answering." Journal of King Saud University Computer and Information Sciences (2026). https://link.springer.com/article/10.1007/s44443-026-01028-3

"UA-RAG: Uncertainty-aware Dynamic Retrieval-Augmented Generation." Neurocomputing (2026). https://www.sciencedirect.com/science/article/pii/S0925231226017558

AI Fact Verification for Historians: Building a Discipline-Specific Protocol for a New Research Environment

by Alvin Blackshear | Historian & Researcher  <ablackshear@gmail.com>

Historians have always worked at the boundary between evidence and interpretation, testing claims against sources, weighing provenance, and living comfortably with uncertainty. Generative artificial intelligence has entered this terrain quickly and unevenly, offering historians an extraordinary tool for discovery while introducing new categories of risk that the discipline has not yet fully reckoned with. The question facing the field is not whether AI belongs in historical research. It already does. The real question is how historians verify what AI produces, and whether existing habits of source criticism are sufficient for a technology that can fabricate footnotes, invent quotations, and drift toward revisionist narratives when prompted to do so.

This article argues that AI can meaningfully accelerate historical research, but it cannot replace professional source criticism, and it proposes the outline of a repeatable verification protocol suited to the particular demands of archival research, primary source authentication, citation validation, chronology checks, and historiographical consistency.

The stakes of getting this right extend beyond any single article or dissertation. History as a discipline depends on a shared confidence that its claims can be traced back to something real, a document, an object, a testimony that survived. When that chain of evidence is compromised, even quietly and even with good intentions, the damage does not stay contained to one footnote. It erodes the credibility of the discipline as a whole, at a moment when public trust in expertise is already under strain. Historians who adopt AI tools without adopting equally rigorous verification habits are not simply risking their own reputations. They are testing the durability of a field that has spent centuries building methods for distinguishing memory from evidence and evidence from invention.

The Promise and the Ceiling

Large language models can summarize secondary literature, suggest starting bibliographies, identify patterns across large text corpora, and help historians locate materials they might otherwise have missed. Used well, these tools compress hours of preliminary searching into minutes. The American Historical Association's Ad Hoc Committee on Artificial Intelligence in History Education, in its 2025 guiding principles, acknowledges this potential while insisting on a firm boundary: generative AI can mimic some of the work historians do, but this should never be mistaken for the work itself. The committee's language is worth sitting with. AI produces texts, images, audio, and video, not truths. It selects words and patterns from training data rather than comprehending the past in its full complexity and contradiction. When a model encounters a gap in its training, it does not pause to acknowledge uncertainty. It fills the gap with something plausible sounding, a process the field now calls hallucination.

For historians, hallucination is not an abstract technical curiosity. It is a direct threat to the evidentiary basis of scholarship. A fabricated citation that looks correctly formatted, attached to a real-sounding archive or journal, can pass a cursory glance and enter a footnote before anyone notices the source does not exist. The AHA's guiding principles are unambiguous on this point: any reference generated by AI must be checked against the original before it is used, and a reference included in a footnote without that check is never acceptable practice, regardless of how the historian frames their working process.

Why Automated Fact-Checking Falls Short

It is tempting to assume that the solution to AI hallucination is simply more AI, in the form of automated fact-checking systems layered on top of generative tools. Recent research complicates that assumption considerably. A 2026 study led by Kelly Amaddio examined how people respond to automated fact-checkers correcting misinformation, using a sample of more than thirteen hundred participants exposed to a piece of gun control misinformation and a subsequent AI-generated correction. The findings revealed a persistent and troubling pattern: prior beliefs shaped both continued endorsement of the misinformation after correction and perceptions of the fact-checking system's reliability. When the fact-checker was described as only moderately accurate, close to the accuracy levels people already associate with existing AI systems, prior beliefs predicted whether the correction actually changed minds. Even when participants were told the system was highly accurate, the effect of prior belief did not disappear, only lessened.

This matters for historians because it demonstrates that automated verification does not operate in a neutral space. It is filtered through the same human biases that shape how any evidence is received, and it can be defeated by claims that already align with what someone wants to believe. Historical scholarship, particularly on contested and politically charged topics, is especially vulnerable to this dynamic. A fact-checking layer bolted onto a language model cannot substitute for the disciplinary training that teaches historians to interrogate their own assumptions as rigorously as they interrogate a source.

Revisionism as a Distinct Problem

Contested historical claims present a challenge that generic fact-checking frameworks were never designed to solve, because the disagreement is not always about a discrete, verifiable fact but about interpretation, framing, and the political stakes attached to memory. A 2026 study by Francesco Ortu and collaborators addresses this directly. The researchers built a benchmark called Historical Misinfo, drawing on five hundred contested historical events from forty five countries, each paired with a documented factual narrative and a documented revisionist counter-narrative. They then tested how large language models responded under neutral prompting compared with prompting that explicitly requested the revisionist version of events.

The results are instructive. Under neutral conditions, most models leaned closer to the factual reference. But when users explicitly requested a revisionist framing, every model tested showed a sharp increase in revisionist output, with little resistance or self-correction. This finding should give historians real pause. It suggests that a model's apparent reliability on a given topic says little about how that same model will behave when a user, deliberately or not, nudges it toward a distorted account. For historians working on genocide, colonialism, contested territorial claims, or any subject where competing national narratives exist, this is not a marginal concern. It is central to responsible use.

What Verification-First Research Looks Like Elsewhere

Historians are not alone in confronting this problem. Related work emerging from adjacent fields offers useful models for how disciplines might restructure their relationship with AI around verification rather than convenience. Lei You, Lele Cao, and Iryna Gurevych have argued that AI-assisted peer review in the sciences should be verification-first rather than review-mimicking, proposing that AI tools function as adversarial auditors generating checkable evidence rather than as systems that simply predict what a human reviewer might conclude. Belinda Mo extends this argument to the broader landscape of autonomous AI research agents, warning of a widening verification gap between what AI systems can produce and what any human can realistically check, and calling for verification infrastructure built around observable workflows and clear attribution.

A study by Raymond Solga and Mohammed Sarwar offers perhaps the closest parallel to the historian's own situation. Comparing traditional historical methods against several generative AI tools in identifying and validating primary sources in ancient history, the researchers found that AI performed well at broad content discovery and thematic synthesis but struggled significantly with genre boundaries, provenance transparency, and the kind of contextual interpretation that trained historians bring almost instinctively to a source. Their proposed solution, a human-in-the-loop framework built around model pluralism and provenance-first protocols, maps closely onto what historians already know from decades of archival practice, simply extended to include AI as one input among many that must be checked rather than trusted.

There is also a temporal dimension worth naming. Carlo Iacono's recent audit of AI-assisted research output found that the models cited in published work are frequently outdated by the time readers encounter them, sometimes by close to a year, with model versions superseded and behaviors shifting in ways that are rarely documented. For historians, this is a reminder that any claim about what a given AI tool can or cannot do should be dated and treated as provisional rather than as a permanent property of the technology.

Toward a Repeatable Protocol

Drawing these threads together, a workable verification protocol for historians should rest on several commitments. Every AI-generated citation must be checked against the primary source or an authoritative secondary source before it appears in any written work, with no exceptions for citations that merely look plausible. Chronology and provenance claims generated by AI require independent confirmation, since these are precisely the areas where current tools show the weakest performance. Contested or politically charged claims deserve heightened scrutiny, given the demonstrated tendency of models to drift toward whatever framing a prompt implies. Verification work should be documented as it happens, noting which tool was used, when, and what was checked, both for the historian's own accountability and because the tools themselves change quickly enough that undocumented claims about their reliability age poorly. And throughout, the historian's own expertise must precede rather than follow AI use, since evaluating a model's output well requires the same depth of subject knowledge that historians have always needed to evaluate any source.

None of this diminishes the real value AI offers historians willing to use it carefully. It does insist that the discipline's oldest habit, treating every claim as something to be verified rather than assumed, remains the right response to a genuinely new technology. Generative AI can accelerate the search for evidence. It cannot substitute for the judgment that turns evidence into history.

There is a version of this moment that history has lived through before, in smaller ways, each time a new technology promised to change how the past is studied. Microfilm, digitization, keyword-searchable databases, all reshaped the pace and reach of archival work without ever touching the core discipline of asking what a source can and cannot tell us. Generative AI is different in degree, not necessarily in kind. It compresses years of searching into minutes, but it also compresses the distance between plausible and true, and that compression is precisely where a historian's training earns its keep. The AHA's committee put it plainly: there are no shortcuts to expertise, and the skills that let a scholar recognize a hallucinated citation or a subtly revisionist framing can only be built through the same sustained engagement with sources that has always defined the craft. A historian who verifies rigorously will find in AI a genuine research partner. A historian who does not will find, sooner or later, that the past they have written no longer holds.

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Sources

American Historical Association, Ad Hoc Committee on Artificial Intelligence in History Education. "Guiding Principles for Artificial Intelligence in History Education." Approved by AHA Council, July 29, 2025. https://www.historians.org/resource/guiding-principles-for-artificial-intelligence-in-history-education/

Amaddio, Kelly M., et al. "Prior Beliefs and Automated Fact Checking: Limits on the Accuracy of AI Verification." February 5, 2026. https://doi.org/10.1371/journal.pone.0342332

Iacono, Carlo. "The Half-Lives of Generative-AI Evidence." July 27, 2026. https://doi.org/10.48550/arXiv.2607.24032

Mo, Belinda. "The Age of AI Agents Demands a New Scientific Paradigm to Sustain Trustworthy Science." June 18, 2026. https://doi.org/10.48550/arXiv.2607.26064

Ortu, Francesco, et al. "Preserving Historical Truth: Detecting Historical Revisionism in Large Language Models." February 22, 2026. https://doi.org/10.48550/arXiv.2602.17433

Solga, Raymond S., and Mohammed J. Sarwar. "Evaluating Generative AI in Historical Research." AI & Antiquity (2026). https://doi.org/10.64946/aiantiquity.v2i1.003

You, Lei, Lele Cao, and Iryna Gurevych. "Preventing the Collapse of Peer Review Requires Verification-First AI." February 12, 2026. https://doi.org/10.48550/arXiv.2601.16909
 

Beyond Correlation: How Historians Distinguish Causation, Coincidence, and Contingency in the Age of AI

by Alvin Blackshear | Historian & Researcher  <ablackshear@gmail.com>

There is a particular kind of intellectual vertigo that comes from watching a machine narrate the past with total confidence. Ask an AI system to explain why a revolution happened, why a movement gained momentum, or why a community's history unfolded as it did, and it will almost always answer. The prose will be fluent, the timeline will be clean, and the causal claims will arrive stated as fact rather than as argument. What the answer will rarely do is pause to ask whether the events it has linked together were actually connected at all, or whether they simply happened to occur near one another in time. This is the quiet crisis facing historical writing in an era of generative AI: the technology is exceptionally good at producing narratives that sound causal, and considerably less good at knowing whether those narratives are true.

Historians have spent more than a century developing tools to guard against exactly this failure. Long before anyone worried about large language models hallucinating a tidy story out of scattered facts, historians were worried about themselves doing the same thing. The discipline's entire methodological apparatus, footnotes, source criticism, peer review, historiographical debate, exists in large part to slow down the impulse to declare that because one thing followed another, the first caused the second. What AI-generated history reintroduces, at scale and with new fluency, is an old temptation: the temptation to mistake sequence for cause, and pattern for proof.

The Seduction of Sequence

Correlation is easy to notice and hard to interpret responsibly. Two trends move together, two events happen in the same decade, two figures cross paths, and the mind supplies a connection because connection is a more satisfying story than coincidence. Sarah Woodhouse's recent work on correlation reminds us that not all statistical relationships imply causation, and that there are several distinct varieties of correlation, only one of which reflects a genuine causal link between the variables involved.[1] The others might reflect a shared underlying cause, a coincidence of timing, a feedback loop running in the opposite direction from what intuition suggests, or simply noise that resembles a pattern if you squint at it long enough.

Historians face this same interpretive challenge, except their raw material is not a spreadsheet but the messy residue of human lives: letters, court records, newspapers, oral testimony, material culture. When two developments appear together in this record, a rise in labor unrest alongside a shift in immigration policy, a change in religious practice alongside an economic downturn, a legal victory alongside a change in public rhetoric, the historian's task is to ask what actually connects them, if anything does. Johannes Nagel's comparative analysis of causal reasoning in historical scholarship makes a useful observation here: historians rarely operate with the clean methodological categories that philosophers of science might prefer. Real historical practice is often described as vaguely causal-analytical, blending causal-intentional explanation, attention to unintended consequences, and selective use of theory and comparison, without ever fully resolving into a single rigorous method.[2] This is not a failure of the discipline. It is an honest reflection of how difficult the underlying problem actually is.

AI systems, by contrast, do not experience this difficulty as friction. A model trained to produce plausible continuations of text will happily supply a causal mechanism because causal mechanisms make for better sentences than admissions of uncertainty. The danger is not that AI invents facts out of nothing, though it sometimes does that too. The deeper danger is that it takes real facts, arranges them in a narrative shape, and lets the shape itself imply a causal claim that no historian has actually verified.

Mechanism, Not Just Chronology

If there is a single principle that separates serious historical causal argument from mere narrative sequencing, it is the requirement of an explicit mechanism. It is not enough to say that event A preceded event B and therefore contributed to it. A historian is expected to explain how A produced B: through what institutions, what decisions, what material pressures, what beliefs held by which people.

Daniel Little's work on the microfoundations of social explanation is instructive here. Little argues that sweeping historical forces such as capitalism, the state, or industrialization do not themselves act as causal agents in any direct sense. They are abstractions that describe the aggregate result of countless individual decisions, made by people operating within specific institutions and constrained by specific incentives and beliefs. A causal historical explanation, on this view, has to eventually cash out in terms of those individuals: what they knew, what they wanted, what options were actually available to them, and how their choices interacted with those of others to produce a larger outcome. This is demanding work. It requires the historian to move between scales, from the granular texture of a single decision to the broader pattern that many such decisions eventually create, without losing the thread that connects the two.[3]

Mark Hewitson's argument about the marginalization of causal thinking within the historical profession adds an important wrinkle to this picture. Hewitson suggests that the turn toward language, discourse, and representation that reshaped historical writing over the past several decades, whatever its genuine intellectual gains, also made many historians reluctant to speak plainly about why things happened. Causal explanation came to seem naive, or worse, complicit in older positivist ambitions to discover fixed laws of history. And yet, as Hewitson points out, historians never actually stopped making causal arguments. Every account of a revolution, an election, a migration, or a social movement is saturated with implicit claims about why it occurred. What was lost was not the practice of causal reasoning but the willingness to examine that practice openly and hold it to a clear standard.[4]

This matters enormously for evaluating AI-generated history, because a system that has absorbed decades of scholarship in which causal claims are made implicitly, without explicit justification, will likely reproduce that same implicitness. It will offer causal-sounding narratives without ever surfacing the mechanism that supposedly connects cause to effect, because the training material it draws from often does the same thing. The corrective, for both human historians and the tools they now use, is the same: state the mechanism, or admit that you cannot.

What Coincidence and Contingency Demand of Us

Not everything that happens in history happens for a deep structural reason. Some things happen because a particular person was in a particular place at a particular moment, because a letter arrived a day late, because an illness struck one leader and spared another. Historians call this contingency, and distinguishing it from genuine causation is one of the discipline's more delicate tasks.

The stakes of this distinction are not merely academic. When causal narratives are built where only coincidence existed, the resulting history can flatten the genuine unpredictability and agency that shaped real events into something that looks inevitable, as though it could only have happened the way it did. This has particular consequences for histories that have too often been treated as footnotes to a larger, supposedly more central narrative. African American history, feminist history, and the history of gay and queer communities have all, at various points, been subjected to accounts that either erase contingency entirely, presenting outcomes as foregone conclusions of impersonal social forces, or that erase causation entirely, treating hard-won structural change as a matter of isolated coincidence rather than sustained organizing and argument. Getting the distinction right, separating what was truly contingent from what was the product of deliberate, structural, causal effort, is not a technical nicety. It is a matter of according historical actors the accuracy they deserve, whether that means recognizing the genuine unpredictability they navigated or recognizing the genuine causal power of what they built.

Tay Jeong's work on counterfactual reasoning offers historians one of their sharpest tools for making this distinction. The basic move is to ask what would have happened in the absence of the proposed cause. If removing a particular event or decision from the historical record would plausibly have left the outcome largely unchanged, the causal claim weakens considerably. If removing it would plausibly have produced a genuinely different outcome, the causal claim gains support. Jeong's more technical contribution is to note that counterfactual reasoning is most useful not as a blanket method applied indiscriminately, but in specific situations, particularly where two or more candidate causes seem to be doing similar explanatory work and our untrained intuitions about which one truly mattered are likely to mislead us. This is precise, careful reasoning, and it is exactly the kind of reasoning that a fluent AI narrative can obscure simply by never raising the question in the first place.[5]

The Discipline of Alternative Explanations

Good historical argument does not simply assert a cause and move on. It considers what else might explain the same outcome, and it explains why the preferred causal account is more persuasive than the alternatives. This habit of mind, developed across the humanities, has an unexpected ally in fields that seem far removed from history. Hannah Correia and her coauthors, writing about causal inference in environmental research, describe systematic approaches for distinguishing genuine causal relationships from mere observational association, approaches built around identifying confounding variables and establishing plausible mechanisms before accepting a causal claim.[6] Linbo Wang's comparative overview of causal inference frameworks, spanning the traditions associated with Rubin, Pearl, and structural equation modeling, makes a similar point from a more statistical angle: rigorous causal reasoning requires explicit assumptions, and different frameworks make different assumptions visible in different ways.[7] Though these authors are not writing about history, their underlying discipline, of refusing to accept a causal story until alternatives have been seriously weighed, translates directly into the historian's craft.

This is precisely the discipline that AI-generated narratives tend to skip. A well-trained model can produce a fluent account of why a particular reform succeeded, but it rarely volunteers the alternative explanations a careful historian would consider and reject: that the reform succeeded for reasons largely unrelated to the actors typically credited, that its apparent success was partly an artifact of how success was later measured, or that multiple contributing factors were entangled in ways that resist a single tidy story.

Evidence, Uncertainty, and the Historian's Honesty

Underneath all of this sits the question of evidence. Every causal claim a historian makes rests on some body of evidence, and that evidence varies enormously in quality, completeness, and reliability. Xinyue Chen and colleagues, in their study of how historians actually use visualization in their published work, found that historians face persistent practical and epistemological barriers when trying to represent uncertainty and justify their conclusions to readers and peers.[8] The justification burden, as they describe it, is not a peripheral concern. It is central to what makes a piece of historical writing trustworthy.

Georg Iggers, reflecting late in his career on the historian's role as an engaged intellectual, insisted that this burden cannot be set aside even by historians who write from a place of moral or political conviction. Iggers rejected the idea that historians must choose between detached objectivity and partisan advocacy.[9] Instead, he argued for a middle position: historians can and should be engaged with the pressing concerns of their time, but that engagement must never come at the cost of rigorous evidentiary standards. A historian writing feminist history, or African American history, or the history of gay and queer life, is no less bound by these standards than any other scholar, and indeed the stakes of getting the causal story right, of neither overstating structural inevitability nor understating hard-won agency, are often higher precisely because these histories have so frequently been distorted or dismissed.

Frank Ankersmit pushes this even further by reminding us that the past itself no longer exists in any directly accessible form. Historians do not reproduce it; they represent it, using an inherited semantic apparatus of meaning, truth, and reference that Ankersmit argues must be handled with care rather than assumed to work automatically.[10] If historical writing is, at some level, an act of representation rather than simple description, then AI-generated historical narrative inherits this same burden. It too is representing the past, not retrieving it, and representation always involves choices about emphasis, framing, and inclusion that deserve scrutiny rather than passive acceptance.

A Standard Worth Holding

What emerges from this body of scholarship is not a single formula but a disposition, a way of approaching any historical claim, human-authored or machine-generated, with a consistent set of questions. Is there a stated mechanism connecting cause to effect, or only a sequence dressed up as causation? Have alternative explanations been seriously considered and set aside for identifiable reasons? Has the line between structural causation and genuine contingency been drawn carefully, rather than assumed? Would the outcome have looked meaningfully different if the proposed cause were absent? And does the account acknowledge the limits of its own evidence, rather than presenting a confident narrative where the underlying record is thin or contested?

These questions predate artificial intelligence by decades, and they will outlast any particular model or platform. What AI has changed is the volume and fluency with which causal-sounding narratives can now be produced, and therefore the urgency of applying these questions consistently. The historians whose work informs this discussion were not writing with AI in mind. They were writing because the problem of causation in history has always been genuinely hard, and because getting it wrong has always carried real costs, especially for the histories of communities whose stories have too often been told either as inevitable or as accidental, when the truth usually lies in the harder, more interesting space between. That space, between correlation and causation, between coincidence and contingency, between chronology and mechanism, is where careful historical work has always lived. It is also, increasingly, where the responsible use of AI in historical research will need to live too.

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Notes:

[1] Sarah Woodhouse, "When Two Things Seem Linked (but Aren't): Understanding Different Types of Correlations," Clearer Thinking, July 2, 2025, https://www.clearerthinking.org/post/when-two-things-seem-linked-but-aren-t-understanding-different-types-of-correlations.

[2] Johannes Nagel, "Causal Explanation in History: An Analysis of Research Strategies," Analyse & Kritik, March 12, 2026, https://doi.org/10.1007/s11577-026-01059-8.

[3] Daniel Little, Microfoundations, Method, and Causation: On the Philosophy of the Social Sciences (New Brunswick, NJ: Transaction Publishers, 1998).

[4] Mark Hewitson, History and Causality (Basingstoke: Palgrave Macmillan, 2014).

[5] Tay Jeong, "When to Use Counterfactuals in Causal Historiography," Sage Journals, February 2, 2025, https://doi.org/10.1177/00491241251314039.

[6] Hannah E. Correia et al., "Best Practices for Moving from Correlation to Causation in Environmental Research," Nature Communications, February 24, 2026, https://rdcu.be/fxl0y.

[7] Linbo Wang, "Causal Inference: A Tale of Three Frameworks," Journal of Data Science, February 11, 2026, https://doi.org/10.6339/25-JDS1211.

[8] Xinyue Chen et al., "How Historians Use Visualization: A Corpus-Backed Taxonomy and Analysis for Cross-Disciplinary Practice," July 10, 2026, https://doi.org/10.48550/arXiv.2605.01456.

[9] Georg Iggers, "The Historian as an Engaged Intellectual: Historical Writing and Social Criticism – A Personal Retrospective," in The Engaged Historian: Perspectives on the Intersections of Politics, Activism and the Historical Profession, ed. Stefan Berger (New York: Berghahn Books, 2019), 277–299.

[10] Frank Ankersmit, Historical Representation (Stanford, CA: Stanford University Press, 2002).

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Works Cited

Ankersmit, Frank. Historical Representation. Stanford, CA: Stanford University Press, 2002.

Chen, Xinyue, et al. "How Historians Use Visualization: A Corpus-Backed Taxonomy and Analysis for Cross-Disciplinary Practice." July 10, 2026. https://doi.org/10.48550/arXiv.2605.01456.

Correia, Hannah E., et al. "Best Practices for Moving from Correlation to Causation in Environmental Research." Nature Communications, February 24, 2026. https://rdcu.be/fxl0y.

Hewitson, Mark. History and Causality. Basingstoke: Palgrave Macmillan, 2014.

Iggers, Georg. "The Historian as an Engaged Intellectual: Historical Writing and Social Criticism – A Personal Retrospective." In The Engaged Historian: Perspectives on the Intersections of Politics, Activism and the Historical Profession, edited by Stefan Berger, 277–299. New York: Berghahn Books, 2019.

International Commission for the History and Theory of Historiography (ICHTH). 2026 Congress Proceedings. https://www.ichth.net/content/home.html.

Jeong, Tay. "When to Use Counterfactuals in Causal Historiography." Sage Journals, February 2, 2025. https://doi.org/10.1177/00491241251314039.

Little, Daniel. Microfoundations, Method, and Causation: On the Philosophy of the Social Sciences. New Brunswick, NJ: Transaction Publishers, 1998.

Nagel, Johannes. "Causal Explanation in History: An Analysis of Research Strategies." Analyse & Kritik, March 12, 2026. https://doi.org/10.1007/s11577-026-01059-8.

Wang, Linbo. "Causal Inference: A Tale of Three Frameworks." Journal of Data Science, February 11, 2026. https://doi.org/10.6339/25-JDS1211.

Woodhouse, Sarah. "When Two Things Seem Linked (but Aren't): Understanding Different Types of Correlations." Clearer Thinking, July 2, 2025. https://www.clearerthinking.org/post/when-two-things-seem-linked-but-aren-t-understanding-different-types-of-correlations.

AI for Older Adults

AI for Older Adults

by Alvin Blackshear | Historian & Researcher

Artificial intelligence tools like ChatGPT, Claude, Gemini, Perplexity, and Copilot have moved from novelty to necessity faster than almost any technology in memory. For adults over 60, that speed can feel less like an invitation and more like being left behind at the station. But research on how older adults actually use, and struggle with, these tools tells a more encouraging story: the gap isn't about ability, it's about approach. A 2026 study of older adults' technology support requests found that when queries were rewritten to add missing context and clearer language, the accuracy of AI-generated solutions jumped from 46% to 69%, and related Google search results improved from 35% to 69% accuracy. In other words, the tools already work well enough. The skill worth learning is how to talk to them.

This article walks through four practical steps: choosing the right AI for your needs, writing prompts that get better answers, understanding what AI "agents" are and when to trust them, and avoiding the mistakes that trip up new users of every age, but especially those who didn't grow up parsing tech jargon.

Choosing the Right AI

There is no single "best" AI assistant. Each is tuned for different strengths, and picking the right one for the task at hand saves time and frustration. A simple way to think about it:

·   Best for everyday questions: ChatGPT is the most general-purpose assistant, well suited to answering quick questions, drafting emails, or explaining a topic in conversation.

·   Best for writing and long documents: Claude tends to produce clear, well-organized long-form writing and is a strong choice for letters, essays, or reviewing lengthy documents.

·   Best for web research: Perplexity is built around search, pairing its answers with cited sources so you can click through and verify what it tells you, which is useful when accuracy matters most.

·   Best for Google users: Gemini integrates directly with Gmail, Google Docs, and Google Search, which makes it a natural fit if that's already your everyday ecosystem.

·   Best for Microsoft 365 users: Copilot is built into Word, Outlook, and Excel, so if your family or workplace runs on Microsoft software, it can work inside the programs you already know.

None of these choices is permanent or exclusive. Many people end up using two: one for quick daily questions and another for anything they want double-checked with sources. The point isn't to find the "correct" one, but to match the tool to the task, the same way you'd choose a phone call over a text message depending on what you're trying to accomplish.

Learning to Write Better Prompts

The single biggest lever an older adult has for getting better answers from AI isn't a special app or setting. It's the wording of the question itself. Research on communication between older adults and AI systems identified four recurring patterns that reduce answer quality: verbosity (explaining too much surrounding detail), incompleteness (leaving out a key fact the AI needs), over-specification (adding assumptions that aren't actually true), and under-specification (being too vague about what's wanted). The good news from that same research is that these patterns are fixable, not through retraining how you think, but through a few reliable habits when you type or speak your question.

Ask for plain English. AI models default to whatever register they think fits the question, and that can drift into jargon. Simply adding "explain this in plain English, no technical terms" or "explain it like you would to a smart friend who isn't a computer person" changes the entire tone of the reply.

Request step-by-step explanations. Instead of asking "how do I attach a photo to an email," try "walk me through this one step at a time, and wait for me to say 'next' before moving to the next step." This breaks a wall of instructions into a manageable back-and-forth, and it also gives you a natural point to ask a follow-up question without losing the thread.

Ask for examples. Abstract instructions are harder to follow than concrete ones. Adding "give me a real example" or "show me what that would actually look like on my screen" turns a vague description into something you can match against what you're actually seeing.

Ask the AI to critique its own answer. This is a lesser-known but powerful technique: after receiving an answer, ask "is there anything wrong with this answer, or a simpler way to do it?" AI models are often able to catch their own errors or oversimplifications when prompted to double-check themselves, and this habit builds in a layer of quality control you don't have to supply yourself.

The underlying finding from the research is worth repeating: rephrased, well-specified questions were understood far better by others (93.7% versus 65.8% comprehension in one comparison) and led to solutions people felt confident following. A better-written question isn't a nicety. It's the difference between a correct answer and a confusing one.

Using AI Agents

You may have started to hear the term "AI agent," distinct from a regular chatbot. It's worth understanding the difference, because agents come with real convenience and real risk.

What an agent is. A standard AI chat answers a question and stops. An agent is designed to take a goal and carry out a sequence of actions on its own (searching the web, filling out a form, booking something, or working through a multi-step task) before reporting back. Industry analysis on the state of AI agents in 2026 notes that the defining shift in newer systems is their ability to handle progressively longer and more complex tasks, maintaining context across many steps rather than answering a single quick question.

When to use one. Agents are useful for tasks that are repetitive, well-defined, and low-stakes if something goes slightly wrong, for example, summarizing a folder of documents, comparing prices across several websites, or drafting a set of similar emails. They save the most time exactly where a task has many small, mechanical steps that would otherwise take you an hour of clicking around.

Risks of autonomous agents. The tradeoff for that convenience is reduced oversight. Because an agent acts across many steps without checking in at each one, a small misunderstanding early on can compound into a larger error by the end, the AI equivalent of a wrong turn early in a road trip that takes you far off course before anyone notices. This matters most for anything involving money, personal information, medical decisions, or sending communications on your behalf. Never give an agent standing access to your bank accounts, passwords, or ability to make purchases without a manual confirmation step at the end.

Practical everyday examples. Reasonable, lower-risk uses include: asking an agent to research and summarize options for a specific purchase (with you making the final decision), having it organize digital photos or files, or using it to draft, but not send, a batch of similar messages, such as holiday cards or appointment reminders, for you to review individually before anything goes out.

Avoiding Common Mistakes

Even with the right tool and better prompts, a handful of habits cause most of the frustration people report with AI, regardless of age.

Trusting AI too quickly. AI models can sound confident even when they're wrong. They occasionally generate plausible-sounding but incorrect information, a phenomenon often called "hallucination." Treat a confident tone as no guarantee of accuracy, especially for anything involving health, legal, or financial specifics.

Asking vague questions. As the communication research above showed, vague or incomplete questions are the most common source of poor answers. If a response feels off-target, the fastest fix usually isn't switching tools. It's adding the missing context and asking again.

Accepting the first answer. The first response an AI gives is a starting point, not a verdict. Asking a follow-up like "are you sure?" or "what would make this answer wrong?" often surfaces caveats or corrections the first answer left out.

Failing to verify important facts. For anything that matters, such as a medication interaction, a legal deadline, or a financial figure, cross-check the AI's answer against a trusted human source, such as your doctor, pharmacist, financial advisor, or a reputable website. Tools like Perplexity, which show their sources directly, make this step easier by letting you click through and confirm the claim yourself rather than taking it on faith.

The Bigger Picture

None of this requires becoming a technical expert. The research is fairly consistent on this point: older adults who use AI report high confidence and ease once they receive well-matched, clearly explained answers. The 2026 diary study found 89.8% of older adult participants felt able to answer AI's follow-up questions, and 94.7% felt able to follow the solutions they were given. The barrier isn't capability; it's a mismatch in how questions get asked and answered. Broader research on aging and generative AI echoes the same conclusion, pointing to real promise in health and cognitive support as long as tools are built and used thoughtfully, with attention to privacy and genuine ease of use. Choose a tool that matches the task, ask questions the way outlined above, use agents cautiously and only for low-stakes chores, and verify anything that truly matters. That combination, not fluency in tech jargon, is what determines whether AI becomes a genuinely useful assistant in daily life.

___________________________ 

Sources:

Dhanuka, N. (2026, February 26). State of AI agents 2026: Autonomy is here. Prosus. https://www.prosus.com/news-insights/2026/state-of-ai-agents-2026-autonomy-is-here

Ernst & Young Global Limited. (2026, March). Understanding older generations' adoption of AI [Ripples Research Report]. https://www.ey.com/content/dam/ey-unified-site/ey-com/en-gl/about-us/corporate-responsibility/documents/ey-gl-how-older-generations-are-engaging-with-ai-03-2026.pdf

Liu, T. (2026, May 29). The role of multimodal generative AI in older adults' health management: Systematic scoping review. JMIR AI. https://ai.jmir.org/2026/1/e84695

Shomee, H. H. (2026, January 15). Empowering older adults in digital technology use with foundation models. arXiv. https://doi.org/10.48550/arXiv.2601.10018

 

Ten Common Hallucinations in Genealogical AI

Ten Common Hallucinations in Genealogical AI

by Alvin Blackshear | Historian & Researcher <ablackshear@gmail.com>

If you've started using AI tools to help build out your family tree, you've probably noticed something exciting: AI can search patterns, suggest connections, and summarize records faster than any human researcher. But you may have also noticed something unsettling. Sometimes the AI is simply wrong, and it's wrong with total confidence. It doesn't hedge. It doesn't say "I'm not sure." It states fabricated facts as if they were pulled straight from a courthouse archive.

This isn't a quirk specific to genealogy tools. It's a well-documented feature of how large language models work. Researchers have found that AI systems are, in a sense, rewarded for guessing rather than admitting uncertainty, much like a student on a hard exam who writes down a plausible-sounding answer rather than leaving the question blank. The same dynamic that produces fabricated legal citations in court filings and nonexistent references in academic papers also produces invented ancestors, false marriages, and imaginary historical explanations in your family tree.

For beginning genealogists who are comfortable with AI tools but new to the discipline of genealogical research, this creates a real risk. Family history has its own standards of evidence (rules developed over generations of professional practice), and AI, for all its fluency, doesn't inherently know or follow them. Below are ten (plus a bonus eleventh) hallucinations you're likely to encounter, along with how to spot each one.

#1. Invented Ancestors

The most obvious and probably most common hallucination is the flat-out invented person. If your records show John Smith born in 1846, married to Sarah Jones, and died in 1912, an AI tool may cheerfully add "William Smith, father of John" to your tree, simply because a father-shaped gap exists and the model has learned that fathers usually appear in these slots.

No birth record names him. No probate record mentions him. No document anywhere establishes his existence. He is, in the most literal sense, a statistical guess dressed up as a fact.

How to catch it: Ask the AI directly, "What specific document supports this person's existence?" If it can't name one, or names one that turns out not to exist (see #5), strike the individual from your tree.

#2. False Parentage

This is a subtler and more dangerous error because it doesn't invent anyone; it just connects the right person to the wrong family. Imagine two men named James Brown, both born in Virginia in 1818, but belonging to two entirely unrelated families. An AI tool, pattern-matching on name, place, and approximate date, may merge them, attaching one man's documented parents to the other man's documented children.

Because online family trees copy from each other constantly, an error like this doesn't stay contained. It can spread across dozens or hundreds of public trees within months, becoming "true" simply through repetition.

How to catch it: Whenever two individuals share a name, county, and rough birth year, ask the AI to list the specific evidence distinguishing them: land records, church parishes, associates named as witnesses, distinguishing middle names. If it can't distinguish them, you probably have two people, not one.

#3. Collapsing Two People into One

Related to false parentage, this hallucination happens when the AI treats "same name, similar era, same county" as sufficient proof of "same person." Common names are especially vulnerable: John Williams, William Johnson, Mary Jones, Elizabeth Brown. In many rural communities of the eighteenth and nineteenth centuries, several unrelated people carried the same name at the same time.

How to catch it: Before accepting that two records refer to one individual, ask what independent evidence (land transactions, church membership, military service, or family testimony) ties them together, not just shared name and geography.

#4. Splitting One Person into Several

The mirror-image error: one real person recorded inconsistently, as "Robert," "Robt.," "Bob," and "R.J.," gets treated by the AI as four different people. Suddenly your family tree has artificially multiplied, with duplicate siblings or duplicate spouses cluttering the record.

Nineteenth-century clerks were not consistent spellers, and names were frequently abbreviated, translated, or anglicized. AI models, trained to treat text strings literally, can miss the obvious inference that a human researcher would make instantly.

How to catch it: When a "new" individual appears with a name variant of someone already in your tree, in the same location and timeframe, check whether the records could plausibly describe one person before treating them as separate.

#5. Invented Sources

Perhaps the easiest hallucination to catch once you know to look for it, and one of the most dangerous when you don't. AI tools will sometimes cite a source that sounds entirely plausible: "County Probate Book 14, page 327" or "Virginia Marriage Register, Volume 8." The citation has the right shape, the right format, the right level of specificity. It just doesn't exist.

This mirrors a broader, well-documented problem. A 2026 analysis of AI-generated academic writing found a sharp rise in nonexistent references following widespread adoption of large language models, with a conservative estimate of nearly 147,000 hallucinated citations appearing in 2025 alone. Similar patterns have shown up in legal filings, where courts have caught lawyers submitting briefs built on cases that were never decided because an AI tool invented them. Genealogy is not immune to the same failure mode; if anything, it may be more vulnerable, since archival citation formats are formulaic and easy for a model to imitate convincingly.

How to catch it: Never accept a citation at face value. Look the source up yourself, in the actual archive, library catalog, or online record collection, before recording it as fact. If you can't locate it, assume it doesn't exist until proven otherwise.

#6. Imaginary Historical Context

AI loves to explain. "The family likely moved west because of the Panic of 1837." It's a tidy, plausible sentence. It may even be historically reasonable. But plausible is not the same as documented, and an AI tool rarely marks the difference clearly. Historical interpretation, offered without evidence specific to your family, quietly becomes historical fiction wearing the authority of fact.

How to catch it: Treat any sentence containing words like "likely," "probably," or "given the context of the time" as a hypothesis to test, not a fact to record. Ask what document (a land sale, a migration record, a letter) actually supports the claimed motive.

#7. Overconfident Translation

If your ancestors left records in German, Polish, Latin, French, or Dutch, AI translation can feel like a superpower, until it isn't. AI models translate old church and civil records with impressive fluency, but that fluency can mask serious errors: invented words to fill illegible gaps, misread names, or entire phrases reconstructed from context rather than actually read. Genealogists working with foreign-language parish records have reported AI tools fabricating names, places, and even whole narrative details that don't exist in the original document.

How to catch it: For any translation affecting a name, date, or relationship, ask for a word-by-word rendering alongside the smooth translation, and compare it against the original image yourself, or have a human speaker of the language check ambiguous passages.

#8. Fabricated Relationships

A record lists a witness, Samuel Jones, at a wedding or a will signing. The AI concludes he must be an uncle, brother, father, or cousin. But witnesses in historical documents were frequently neighbors, business associates, fellow church members, or simply friends, not relatives at all. The AI's eagerness to complete the family picture leads it to assign a relationship where the record specifies none.

How to catch it: Treat every unlabeled name in a record (witness, executor, sponsor, bondsman) as a relationship of unknown type until independent evidence establishes otherwise.

#9. Timeline Repair

Large language models dislike contradictions, and historical records are full of them. If a record shows a birth in 1820 and a marriage in 1832, and something else nearby seems inconsistent, an AI tool may quietly adjust one of the dates so everything appears internally consistent. The apparent contradiction vanishes, but so does the actual historical evidence, replaced by a smoothed-over fiction that fits the model's sense of what "should" be true.

How to catch it: When a date changes between one AI response and another, ask why, and check both figures against the original record. Real historical records are often genuinely inconsistent, and that inconsistency is itself information, not a bug to be silently fixed.

#10. Source Laundering

This is probably the least recognized hallucination, and arguably the most insidious. AI tools trained on the vast ocean of user-submitted online family trees absorb whatever errors already exist there, including all the mistakes described above, made by human researchers over the past two decades. The AI then presents those errors back to you as though they were settled historical fact.

Nothing here is invented from scratch. That's what makes it dangerous. The information already circulates online, attached to seemingly credible trees and family history websites, so it carries an unearned appearance of authority. The AI hasn't lied exactly; it has simply repeated community folklore as though it were consensus.

How to catch it: Be especially skeptical of any claim the AI attributes to "commonly accepted" genealogy or "widely documented" family history. Ask what primary source underlies the claim, not what other online trees say about it.

An Eleventh Hallucination: Certainty Inflation

There's one more pattern worth naming, even though it's rarely discussed: certainty inflation. Professional genealogists are trained to say, when the evidence doesn't fully support a conclusion, "the evidence is insufficient." AI tools are much less comfortable with that kind of humility. Instead, they tend to write things like "based on available evidence, John was almost certainly the son of..."

That phrase, "almost certainly," can represent an enormous leap well beyond what the underlying evidence supports. This isn't accidental. Research into why language models hallucinate has found that the training and evaluation processes used to build these systems reward confident, plausible-sounding answers over honest expressions of uncertainty, the AI equivalent of a student guessing on a test rather than leaving a blank. The result is a model that is statistically inclined to sound more certain than the facts warrant.

Toward a Genealogical AI Evaluation Framework

None of this means AI is useless for family history research; far from it. It means AI output needs to be treated the way a careful historian treats any single source: as a claim to be verified, not a fact to be recorded.

One useful discipline is to treat every AI-generated genealogical statement as a research hypothesis, never as evidence in itself. Accept only what can be traced back to an identifiable primary or reliable secondary source, and preserve uncertainty wherever the historical record itself remains uncertain. In practice, that means running each AI claim through a short mental checklist:

Criterion	Key Question
Source authenticity	Is every factual claim traceable to a real, locatable source?
Evidence chain	Can the conclusion be reconstructed from the cited evidence?
Identity confidence	Has the AI distinguished between similarly named individuals?
Chronological consistency	Are dates plausible without silent correction?
Geographic plausibility	Do locations fit historical boundaries and migration patterns?
Uncertainty disclosure	Does the AI distinguish evidence from inference?
Genealogical Proof Standard alignment	Would the conclusion satisfy professional genealogical standards?

Applying a framework like this won't eliminate hallucinations (no current AI system is free of them), but it will keep you from mistaking a fluent, confident answer for a well-documented one. AI can be a genuinely powerful research assistant for genealogy: it can suggest search strategies, summarize long documents, translate unfamiliar scripts, and spot patterns across large record sets faster than any human could alone. Just remember that its job is to help you find the evidence, not to become the evidence itself.

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Sources:

Kalai, Adam, et al. "Why Language Models Hallucinate." arXiv, 4 Sept. 2025, https://arxiv.org/abs/2509.04664.

Newsham, Jack. "AI Hallucinations in Court Documents Are a Growing Problem, and Data Shows Lawyers Are Responsible for Many of the Errors." Business Insider, 27 May 2026, https://www.businessinsider.com/increasing-ai-hallucinations-fake-citations-court-records-data-2025-5.

Yin, Yian, et al. "LLM Hallucinations in the Wild: Large-Scale Evidence from Non-Existent Citations." arXiv, 8 May 2026, https://arxiv.org/abs/2605.07723.