by Alvin Blackshear | Historian & Researcher
A historian asks ChatGPT a seemingly straightforward question: “Who was the
first African American elected to public office in Delaware after
Reconstruction?”
Within
seconds, the system produces a polished answer. It supplies a name, an election
date, a description of the office, and perhaps even several citations. The
prose is orderly, confident, and specific. Nothing about it appears careless.
The historian copies the response into a research notebook and proceeds to the
next question.
Only
later does the answer begin to unravel. One citation does not exist. A
quotation attributed to a nineteenth-century newspaper is actually a modern
paraphrase. The date belongs to another officeholder. An appointment has been
confused with an election. Details from two people have been combined into a
single biography.
Nothing
in the original response sounded implausible. That is precisely the danger.
The
greatest mistake historians make when using ChatGPT is not merely trusting an
occasional incorrect answer. It is treating AI-generated prose as though it
were historical evidence. ChatGPT produces narratives. Historians require
evidence. Those activities may sometimes overlap, but they are not the same.
The Abandonment of Historical Method
Historians already
possess a sophisticated method for evaluating claims. When examining a letter,
newspaper, memoir, photograph, government record, or oral history, they ask
familiar questions. Who created the source? When was it created? For what
purpose? Under what circumstances? What interests shaped its contents? Can its
claims be corroborated? Has another scholar interpreted the evidence
differently?
These questions
constitute more than an academic ritual. They are the means by which historical
knowledge is distinguished from rumor, memory, advocacy, folklore, and
invention.
Yet something curious
happens when many researchers begin using generative artificial intelligence.
The normal habits of source criticism are suspended. A response is accepted
because it sounds reasonable. A citation is trusted because it resembles an academic
citation. A quotation is repeated because the language seems appropriate to the
period.
The historian who
would never accept an unsigned reminiscence without examining its provenance
may accept an AI-generated paragraph without asking where any of its claims
originated.
ChatGPT should
therefore be treated as an intelligent research assistant, not as a documentary
source. It can suggest search terms, identify possible lines of inquiry,
compare concepts, organize notes, formulate questions, and help clarify
difficult prose. It can also point a researcher toward archives, books, people,
and events that merit investigation. Its usefulness, however, does not convert
its output into evidence.
The distinction is
fundamental. A research assistant may tell a historian that a document probably
exists. The historian must still locate and examine it.
Why Fluency Is So Persuasive
Large language models are
especially dangerous when they are almost correct. An obviously absurd answer
invites skepticism. A plausible answer, containing real names, accurate
background information, and one fabricated detail, may pass unnoticed.
ChatGPT’s authority is
largely rhetorical. It writes in complete sentences, arranges events
chronologically, supplies transitions, and often presents conclusions without
visible hesitation. Readers naturally associate these qualities with knowledge.
Confidence, specificity, and coherence become substitutes for provenance.
Recent research suggests that
this problem is not simply the result of careless prompting. Kalai and his
colleagues argue that common accuracy-based evaluations may reward models for
guessing instead of acknowledging uncertainty. If a benchmark gives credit only
for correct answers and imposes no meaningful cost for plausible errors, the
model is encouraged to answer even when abstention would be more responsible.
The researchers propose open scoring rubrics that disclose how errors and
abstentions will be evaluated.
This observation has
important consequences for historians. A model that always produces an answer
may appear more useful than one that frequently says, “I do not have enough
evidence.” In historical research, however, an admission of uncertainty may be the
more accurate and intellectually responsible response.
The historian must therefore
evaluate not only what the system says, but whether the system should have
answered at all.
Five Forms of Historical
Hallucination
Historical
hallucinations frequently assume recognizable forms.
The first is the
invented citation. ChatGPT may produce a realistic book title, journal article,
archival collection, author, volume number, or page range. Every component may
appear academically credible even though the source does not exist.
The second is the
misquotation of a primary source. A model may modernize, compress, or
reconstruct a statement and then present the resulting language inside
quotation marks. The general sentiment may be accurate, but the words are not
documentary evidence.
The third is the
composite biography. Details belonging to several individuals may be joined
into one apparently coherent life. This is especially dangerous when
researching people who share names, occupations, institutions, military units,
or geographic locations.
The fourth is
incorrect chronology. A model may identify real events but arrange them in the
wrong order, confuse the date of an appointment with the date of an election,
or place a person at an institution before that person arrived there.
The fifth is false
causation. ChatGPT may connect two events with phrases such as “therefore,” “as
a result,” or “this led to,” even when the evidence establishes only sequence
or correlation.
These errors are
not equally visible. An invented person may be discovered quickly. A subtle
chronological error or unsupported causal inference may survive several rounds
of editing because it fits an expected narrative.
Why Better Prompts Are Not Enough
Prompt design can
improve an AI response. A historian can request citations, ask the system to
distinguish facts from interpretations, require expressions of uncertainty, or
instruct it not to invent missing information. Such practices are worthwhile.
They are not
verification.
A carefully written
prompt may reduce the probability of an error, but it cannot transform
generated language into historical evidence. Even a response that includes
citations must be checked against the cited material. Even a quotation
accompanied by a page number must be located on that page. Even a claim
presented as certain must be corroborated.
Research on
hallucinations in academic writing identifies fabricated citations, factual
distortion, named-entity errors, logical inconsistency, and propagation errors
as threats to scholarly integrity. The final responsibility remains with the
human author. An historian cannot excuse a false statement by explaining that
ChatGPT supplied it.
Better prompting is
therefore a research technique, not an evidentiary standard.
Testing AI as Historians Test
Evidence
Historians need a
repeatable method for testing AI-generated research. The evaluation should move
beyond impressions such as “the answer seemed good” or “the model performed
well.”
A practical scoring
rubric might assess eight dimensions:
1.
Citation existence: Does every cited work
or archival collection exist?
2.
Citation accuracy: Does the cited source
support the specific claim?
3.
Quotation fidelity: Are quoted words
reproduced exactly and in context?
4.
Identity accuracy: Have people with
similar names or careers been distinguished?
5.
Chronological accuracy: Are dates and
sequences correct?
6.
Geographic and institutional accuracy:
Are places, offices, organizations, and jurisdictions correctly identified?
7.
Provenance: Can each important assertion
be traced to accessible evidence?
8.
Historiographical consistency: Does the
answer acknowledge significant scholarly disagreement?
Each category could
be scored from zero to four. A zero would indicate fabrication or complete
failure. A score of one would reflect major errors. Two would indicate partial
support or substantial ambiguity. Three would represent generally accurate work
with minor deficiencies. Four would require full and independently verified
support.
A weighted rubric
would assign greater penalties to invented citations, false quotations, and
merged identities than to minor stylistic or contextual omissions. This matters
because not all errors have the same scholarly consequence.
Benchmark testing
should also use a fixed set of questions rather than memorable anecdotes.
Researchers could create a collection of historical questions at several
difficulty levels: well-documented national events, obscure local events,
disputed interpretations, biographical identity problems, chronological
puzzles, and questions whose correct answer is genuinely unknown.
The same questions
could then be submitted to several AI systems under controlled conditions.
Evaluators would record accuracy, citation validity, unsupported assertions,
appropriate abstentions, and changes across repeated runs. Such testing would
reveal not only whether a model produces correct answers, but how it fails.
What RAG Can and Cannot Do
Retrieval-augmented generation,
commonly known as RAG, offers one method of improving reliability. A RAG system
retrieves documents from a designated collection before generating an answer.
For historians, that collection might contain archival finding aids, digitized
newspapers, oral-history transcripts, government records, or scholarly
articles.
This approach can make AI
responses more transparent and more closely connected to identifiable sources.
It may reduce fabricated citations and permit researchers to inspect the
evidence used in producing an answer.
Yet RAG does not eliminate the
need for historical judgment.
Recent research distinguishes
factuality from faithfulness. A statement may be faithful to a retrieved
document while the document itself is inaccurate, biased, incomplete, or
misinterpreted. Conversely, a statement may be factually correct but unsupported
by the particular documents retrieved. A serious evaluation must test both
dimensions.
For historians, retrieval is
only the beginning. The fact that a system retrieved a newspaper article does
not establish that the article was truthful. The fact that a claim appears in a
memoir does not establish that memory was reliable. The fact that several
sources repeat a statement does not prove they were independent.
RAG can retrieve evidence. It
cannot perform the full intellectual work of source criticism.
The Historian’s Continuing
Responsibility
ChatGPT does not
threaten historical scholarship simply because it occasionally invents facts.
Historical scholarship has always confronted error, forgery, propaganda,
partial memory, and misleading testimony.
The greater
danger arises when historians allow the fluency of artificial intelligence to
replace the discipline’s oldest habit: skepticism.
For centuries,
historians have questioned manuscripts, memoirs, newspapers, photographs,
statistics, and archives. Artificial intelligence deserves no exemption from
that tradition. Its output should be tested, scored, corroborated, and traced
to evidence.
The historian’s
task remains unchanged. It is not merely to discover information or compose a
persuasive narrative. It is to determine what the surviving evidence permits us
to say, what remains uncertain, and why the distinction matters.
References
- Kalai,
Adam Tauman, Ofir Nachum, Santosh S. Vempala, and Edwin Zhang. “Evaluating
Large Language Models for Accuracy Incentivizes Hallucinations.” Nature
653 (2026): 1047–1051.
- Rahman,
Subhey Sadi, et al. “Hallucination to Truth: A Review of
Fact-Checking and Factuality Evaluation in Large Language Models.” Artificial
Intelligence Review 59 (2026), article 70.
- Fadeeva,
Ekaterina, et al. “Faithfulness-Aware Uncertainty Quantification for
Fact-Checking the Output of Retrieval-Augmented Generation.” Findings
of the Association for Computational Linguistics: ACL 2026 (2026):
6814–6836.
- Giray,
Louie, Md Emdadul Islam, and John Arvin Glo. “AI Hallucinations in
Academic Writing: Implications for Research Integrity.” Naunyn-Schmiedeberg’s
Archives of Pharmacology (2026). https://pubmed.ncbi.nlm.nih.gov/42217043
