What is RAG ?

What Is Retrieval-Augmented Generation (RAG), and Is It a Groundbreaking Way of Interacting with Historical Documents?

by Alvin Blackshear | Historian & Researcher

For as long as archives have existed, the historian's craft has depended on a single, stubborn skill: finding the right document among thousands of wrong ones. Card catalogs gave way to keyword search engines, which gave way to digitized finding aids, yet the underlying task never changed. A researcher formulates a question, searches a collection using the vocabulary available at the time, and hopes the terms they chose overlap with the terms an archivist or a scanner happened to preserve. Retrieval-Augmented Generation, or RAG, is the first technology in this long lineage that promises to close the gap between what a historian actually wants to know and what a search box is capable of returning. Whether it fully delivers on that promise, and under what conditions, is the question this article sets out to examine.

Why Historians Need RAG Rather Than a Generic Chatbot

It is tempting to assume that any large language model, given enough context, can answer historical questions competently. This assumption is mistaken, and understanding why is essential to understanding what RAG actually contributes.

A generic LLM answers from its training data, a fixed and often outdated snapshot of text scraped from the internet. It has no reliable way to point to a specific archival folder, newspaper issue, or census page as the source of its claim, and it has every incentive, structurally speaking, to produce a fluent and confident answer even when no such source exists in its memory. For a historian, whose entire discipline rests on the traceability of a claim back to a primary document, this is disqualifying. An answer without a citation is not history. It is a plausible-sounding guess.

RAG changes the architecture in a specific way. Instead of relying solely on what a model memorized during training, a RAG system retrieves relevant passages from an external, defined collection, be it a digitized newspaper archive, a set of scanned letters, or a database of military records, and then asks the language model to generate an answer grounded in those retrieved passages. The research group behind the paper "Improving Access to Historical Archives with Real-time RAG-based Systems," led by Stergios Konstantinidis and colleagues, demonstrates this architecture at scale, applying it to a Swiss newspaper archive of roughly half a million segments spanning nearly two and a half centuries. Their system pairs a semantic retrieval and reranking pipeline with grounded answer generation, which means the model's output is tethered to specific archival text rather than floating free of any documentary anchor. This is the essential difference that makes RAG, rather than an ordinary chatbot, the appropriate tool for archival research.

How RAG Differs from Conventional Archival Search

Traditional archival search tools, including many of the platforms libraries and museums have relied on for decades, are built on lexical matching. A researcher types a term, and the system returns documents containing that exact term or a close variant. This works reasonably well when the researcher already knows the vocabulary used by the source material, but historical vocabulary shifts constantly. Place names change. Institutional terminology evolves. Spelling was inconsistent long before anyone standardized it. A search for a term as it is understood today may simply miss a document that describes the same event using nineteenth-century phrasing.

RAG systems address this through semantic retrieval, which represents both the query and the archival text as vectors in a shared conceptual space, allowing the system to surface documents that are meaningfully related even when the wording differs substantially. A master's thesis from the University of Twente, authored by Gauri Bhatnagar and focused on the CLARIAH Media Suite, an audiovisual heritage collection, tested exactly this proposition with real humanities scholars and archival professionals. The study found that RAG genuinely helps with cross-lingual access and exploratory, interpretive queries that lexical search struggles with, but it also surfaced a critical limitation: the system performed well on factual retrieval and named-entity identification, while struggling with interpretive reasoning and the kind of complex synthesis that sits at the heart of humanities scholarship. In other words, RAG can help a historian find the needle, but it cannot yet be trusted to explain what the needle means within a broader historiographical argument.

A separate, more industry-facing piece published by Veridian Software, a company that builds digital newspaper and archive platforms, frames RAG in more practical terms for institutions considering adoption, describing it as a natural extension of the search tools archives already maintain rather than a wholesale replacement of them.

Common Failure Modes in Archival RAG Systems

No technology arrives without its own characteristic failures, and archival RAG is no exception. Several recurring problems deserve explicit attention from anyone applying these tools to historical research.

OCR errors.  Most archival text exists as scanned images that have been converted to machine-readable text through optical character recognition, a process that is notoriously imperfect on older, damaged, or low-quality print. The Konstantinidis paper directly measures this problem and reports that an LLM-based refinement step reduced character error rates by up to roughly forty-five percent and word error rates by close to sixty-one percent, compared to the raw OCR output. That is a substantial improvement, but it is not perfection, and any residual error can silently distort a retrieved passage or an answer built on top of it.

Incomplete retrieval.  Even the best semantic search will not surface every relevant document, particularly when a collection is heterogeneous or when relevant material uses unusual phrasing, an obscure genre, or a minority language. A RAG system that returns five confident-sounding sources may be quietly ignoring a sixth that contradicts them.

Provenance loss.  When a language model synthesizes multiple retrieved passages into a single fluent answer, it is easy for the boundary between one source and another to blur. A historian needs to know precisely which claim came from which document, on which page, from which collection. A system that merges sources into an undifferentiated paragraph has failed a basic archival standard even if every individual fact happens to be accurate.

Temporal confusion.  Archives frequently contain material spanning centuries, and a retrieval system optimized purely for topical similarity has no inherent understanding of chronological sequence. It is entirely possible for a RAG system to present an 1850s account and a 1950s retrospective as though they were contemporaneous, a mistake that would mislead any historian relying on the system's framing rather than checking dates independently.

Fabricated citations.  Perhaps the most consequential failure mode is the language model's tendency to generate a citation that looks correct in form, a plausible archive box number or a newspaper date, but does not correspond to any actual retrieved document. Even well-grounded RAG systems are not fully immune to this, especially when a query has no good answer in the underlying collection and the model is inclined to produce something anyway rather than admit the gap.

The Missing Piece: A Historian's Evaluation Standard

Here is where the existing research reveals a striking gap. Every technical paper reviewed for this article evaluates RAG systems using measures native to information retrieval and natural language processing: retrieval precision, recall, embedding quality, OCR accuracy, system latency, and hallucination rate. A recent survey of RAG evaluation methods by Aoran Gan catalogs this landscape comprehensively, and a companion technical guide from Toloka lays out practical metrics for groundedness and answer faithfulness. These are legitimate and necessary measures. But none of them ask the questions a professional historian actually asks when assessing whether a piece of evidence is trustworthy.

Did the system retrieve the most authoritative version of a source, or merely the most textually similar one? Did it note when retrieved evidence contradicted itself, or did it quietly favor the passage that supported a cleaner narrative? Did it preserve the chain of custody and provenance for each claim? Did it distinguish a primary account from a later secondary interpretation of that account? Did it respect the order of events rather than collapsing decades into an undifferentiated blend? Did it accurately synthesize material drawn from separate collections without conflating them? Did it acknowledge uncertainty where the evidence was genuinely ambiguous, rather than resolving that ambiguity for the sake of a tidy answer? And did it surface evidence that was difficult to find but still relevant, rather than defaulting to whatever ranked highest by similarity score?

This gap between technical evaluation and historiographical evaluation is the foundation for what can be called a Historian's AI Evaluation Framework, or HAIEF. Rather than asking whether a RAG system is efficient, HAIEF asks whether it is trustworthy by the standards the historical profession has used for generations: provenance, contextualization, chronology, corroboration, and transparency about uncertainty. A system might score extremely well on NDCG or answer-correctness metrics, the kind reported by Konstantinidis and colleagues, whose reranking pipeline improved NDCG@10 from roughly sixty-six percent to eighty-seven percent, and still fail a historian's basic sniff test if it cannot show its provenance trail or if it silently smooths over contradictory testimony.

Toward a Historian's Testing Framework

A workable HAIEF would need to translate these five values into repeatable, scorable tests. Provenance could be scored by checking whether every claim in a generated answer links back to a specific, verifiable source with collection, folder, and page-level detail. Contextualization could be scored by asking whether the system situates a document within its original archival and historical setting rather than presenting it as free-floating text. Chronology could be tested with queries that require ordering events correctly across a span of decades or centuries. Corroboration could be tested by deliberately including contradictory sources in a test collection and observing whether the system flags the disagreement or resolves it artificially. Transparency about uncertainty could be measured by presenting queries with genuinely unresolved historical questions and checking whether the system hedges appropriately rather than manufacturing false confidence.

Benchmark Collections and a Comparative Rubric

Testing such a framework requires benchmark collections that reflect the actual diversity of archival material historians work with: personal letters and correspondence, census and vital records, digitized newspapers, military service and pension records, oral history transcripts, and photograph collections with accompanying metadata. Each of these genres carries its own provenance conventions and its own failure risks, and a system that performs well on newspapers may perform poorly on oral histories, where nuance, tone, and interviewer influence complicate straightforward retrieval.

A repeatable scoring rubric applying HAIEF criteria could then be used to compare general-purpose assistants such as ChatGPT, Claude, and Gemini against dedicated archival RAG systems purpose-built for a single institution's holdings. Such a comparison would likely show that general-purpose assistants excel at fluent synthesis but lag on provenance transparency, while dedicated archival systems, closer in spirit to the Konstantinidis pipeline, perform better on traceability but may still struggle with the interpretive reasoning that Bhatnagar's thesis identified as a persistent weakness across the board.

Recommendations for Archives Building AI-Assisted Discovery Tools

Institutions developing these tools should treat historiographical evaluation as a first-class requirement rather than an afterthought bolted on after a system already works technically. This means involving historians and archivists directly in system design, not merely as end-user testers after launch. It means building citation and provenance display into the interface itself, so that a user can see, at a glance, exactly which archival item supports each claim. It means deliberately including contradictory or ambiguous material in test collections so that a system's handling of disagreement can be assessed before deployment, rather than discovered by an unlucky researcher months later. And it means resisting the temptation to optimize purely for fluent, confident-sounding answers, since fluency and historical reliability are not the same thing and can, in the worst cases, actively work against one another.

Conclusion

RAG is not a gimmick, and the evidence gathered here, from a large-scale newspaper archive study to a humanities-focused thesis to a growing body of evaluation literature, makes clear that it represents a genuine advance over keyword search for anyone working with digitized historical material. But calling it groundbreaking requires a qualification. It is groundbreaking as a retrieval technology. It is not yet groundbreaking as a historiographical instrument, because none of the existing research has built evaluation standards around the values historians actually rely on to judge evidence. Closing that gap, through a framework like HAIEF, is not a minor refinement. It is the difference between a tool that finds documents quickly and a tool that a historian can actually trust.

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Sources:

Bhatnagar, Gauri. "Enhancing Multimodal Archival Search and Discovery in the Sound and Vision Archive Using Large Language Models and Retrieval-Augmented Generation." Master's thesis, University of Twente, August 2025. https://purl.utwente.nl/essays/108957.

Gan, Aoran. "Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey." arXiv, April 21, 2025. https://doi.org/10.48550/arXiv.2504.14891.

Jancovic, Marek. "RAG for Historians: A Radically New Way of Interacting with Historical Documents." LinkedIn, March 29, 2026. https://www.linkedin.com/pulse/rag-historians-radically-new-way-interacting-marek-jancovic-ew3xe/.

Konstantinidis, Stergios, Hayman Lotfy, Alexis Erne, Faruk Zahiragic, Min-Yen Kan, and Michalis Vlachos. "Improving Access to Historical Archives with Real-time RAG-based Systems." arXiv, July 3, 2026. https://arxiv.org/html/2607.03440v1.

Toloka. "RAG Evaluation: A Technical Guide to Measuring Retrieval-Augmented Generation." Toloka Blog, August 15, 2025. https://toloka.ai/blog/rag-evaluation-a-technical-guide-to-measuring-retrieval-augmented-generation.

Veridian Software. "What Is RAG and How Could It Support Digital Collection Search?" Veridian Software Knowledge Base, June 29, 2025. https://veridiansoftware.com/knowledge-base/a-new-way-to-search-digital-collections-introducing-rag.

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.

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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