The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index; none of the 2 sources its claims cite is checked for support yet.
ELIZA—A Computer Program For the Study of Natural Language Communication Between Man and Machine
Open accessAbstract
Communication Between Man And Machine,JosEPh ~VEIZENBA UM Massach.usclls[nshl-ute qf Tcchnu[ogg,* Cambridge, Mass.ELIZA is a program operating within the MAC time-sharing system at MIT which makes certain kinds of natural language conversation between man and computer possible.Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text.Responses are generated by reassembly rules associated with selected decomposition rules.The fundamental technical problems with which ELIZA is concerned are: (1) the identification of key words, (2) the discovery of minimal context, (3) the choice of appropriate transformations, (4) generation of responses in the absence of key words, and (5) the provision of an editing capability for ELIZA "scripts".A discussion of some psychological issues relevant to the ELIZA approach as well as of future developments concludes the paper.Work reported herein was supported (iu prot) by Project MAC, an _\-lIT research program sponsored by the Advm~ced Research Pr,,jects Aget,cy, l)~l)ar~met~t ~,1" l)ofense, under Office of Naval Research Contract Number Nora'-4102(01).
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
ELIZA is an early natural-language conversation program that scans input for keywords and applies associated decomposition and reassembly rules to generate responses. The paper describes its architecture, script mechanisms, and psychological implications, while stressing that ELIZA simulates understanding rather than possessing it.
Key findings
- ELIZA selects the highest-ranked keyword found in an input text, then tries matching decomposition rules and applies an associated reassembly rule; if no keyword is found, it can use the reserved `NONE` category for content-free remarks or the `MEMORY` mechanism to reuse earlier transformed text.
- Scripts are data rather than part of the program, so ELIZA is not tied to one recognition pattern or language; the paper states that scripts existed in Welsh and German as well as English.
- Implementation details include a keyword dictionary using a `KEY` vector of 128 contiguous computer words, hashing keywords to a 7-bit integer; the largest dictionary attempted contained about 50 keywords, and no list in the `KEY` vector held more than two key list structures.
- The discussion reports that the main serious scripts made ELIZA respond roughly like Rogerian psychotherapists, and that some subjects were hard to convince ELIZA was not human, which the paper calls a striking form of Turing’s test.
Methods
- Design: ELIZA was implemented on MIT’s MAC time-sharing system and written in MAD-SLIP for the IBM 7091 as printed; users typed natural-language statements, and a double carriage return transferred control to ELIZA.
- Data or sample: the paper uses illustrative conversations and script fragments, especially a Rogerian-style script, rather than a formal user study or quantitative dataset.
- Main analysis: technical exposition of decomposition and reassembly rules, keyword ranking via a keystack, substitution rules, equivalence classes, `MEMORY` and `NONE`, and script editing through the MAC `ED` program, followed by discussion of psychological credibility and future directions.
Limitations
- Stated: ELIZA discards its inputs except for a few transformed through the `MEMORY` machinery, lacks the capacity to store selected parts of input and draw valid conclusions, and has often aimed to conceal its lack of understanding.
- Stated: the script contains specific hypotheses about the world despite claims that the psychiatric interview format avoids storing real-world information; the paper also says bounds on ELIZA’s extendability come from the program, not from any script.
- Evident: no controlled evaluation or quantitative outcome data are reported; the evidence is illustrative transcripts and script examples, and the provided text is OCR-noisy and incomplete in places, making some technical details uncertain.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
25 No basis given · 14 Own interpretation · 3 Not checked yet
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ELIZA is a program operating within the MAC time-sharing system at MIT which makes certain kinds of natural language conversation between man and computer possible.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ELIZA in its use so far has had as one of its principal objectives the cot~cetdment of its lack of understanding.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ELIZA performs best when its human correspondent is ilfitially instructed to “taJk” to it, via the typewriter of course, just as one would to a psychiatrist.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“But.there are bounds on the extendability of ELIZA's “understanding” power, which arc a function of the ELIZA program itself and not a function of any script it may be given.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the t?sychbJ:ric interview form of an I';LIZA script has the advantage that it eliminates the need of storing e.~:pUcit inform:tth?n about the real world.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A switch of objectives from the concealmeat to the revelation of misunderstanding is seen as a precondition to making an ELIZA-like program the basis for an effective natural language man machine communication system.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 35
- It may state two separate claims as one. 9
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The largest dictionary so far attempted contains about 50 keywords.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ELIZA scripts exist (at, this writing) in Welsh attd Gernmn as well as in English.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Of course, lhe set of transformation rules can be guaranteed to terminate with a decomposition rule which nmst match.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The decomposition rule (o K o) will match any text in which tile word K appears while (0) will match any text whaiever.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For e>:ample, the tran>formation rules which cause ihc input Everybody hates me to be transformed to Can you think of anyone in particular and other such are based on quite specific hypotheses about the world.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The eurrent version of E L I Z A requires that one keyword be associated with M E M O R Y and that exactly four transformations aceoinpany that word in that context.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Whenever it is the highest ranking keyword of a text one of the transformations on the M E M O R Y list is randomly selected, and a copy of the text is transformed accordingly.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The system described so far is ess(qltially one which selects ~t decomposition rule for the highest ranking keyword found in an input text, attempts to match that text according to that decomposition rule and, failing to make a match, selects the next reassembly rule associated with the matching decomposition rule and applies it.to generate an output texi.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This mechanism makes it possible to, in effect, test on key phrases as opposed to single key words.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“No list named in any of the words of the KEY vector contains more than two key list structures.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The “given language” is English as is the “other language”, expressions of which arc generated.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When a text without keywords is encountered later and a certain counting mechanism is in a particular state and the stack in question is not empty, then the transformed text is printed out as the reply•”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The script that has formed the basis for most of this discussion happens to be one with an overwhehningly psychological orientation.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“~.~: idea ihat the presettt ELIZA s{:rk)t contains no infocmation about tl~e real world i:s ,tot.e~tirely true.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“An important property of ELIZA is that a script is data; i.e., it is not part of the program itself.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Seine subjects'have been very hm'd to convince t h a t E L I Z A (with its present script) is not hunmn.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Suftice it to say that changes, additions and deletions of the script may be made with considerable efficiency and on the basis of entirely contextual cues, i.e., without resort to line numbers or any other artificial devices.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There is, however, another mechanism which causes the s y s t e m to respond more spectacularly in the absence of a key.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Responses are generated by reassembly rules associated with selected decomposition rules.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Tl, Js mechanism h~st,res that the complete set of reassernbly rules ~ssoeiated with a given deeomposh:ion rule is cycled through before any repetitions 0CCUY,”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Then, the input text Am I siek would elicit either ])o you believe you are siek or Why do yotl ask i depending on how many tinles the general form had already occurred.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“At this writing, the only serious ELIZA scripts whieh exist are seine which cause E L I Z A to respond roughly as would certain psychotherapists (Rogerians).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, E L I Z A is not restricted to a particular set of recognition patterns or responses, indeed not even to any specific language.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A large part of whatever elegance may be credited to ELIZA lies in the fact that ELIZA maintains the illusion of understanding with so little machinery.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Seen in the coldest possible light, ELIZA is a translating processor in Gorn's sense; however, it is one which has been especially constructed to work well with natural language text.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is striking to note how well his words/fit ELIZA.”
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“In principle, the given hmguage could as well be the kind of English in which “word problems” in algebr~L am given to high school students and the other language, a machine code allowing a particular computer to “solve” the stated problems.(See Bobrow's program S T U D E N T [3].)”
What the sources say
- Not checked yetcites [3] 3. Bom¢ow, D.G. Natural language input for a computer prob- lem solving system. Doctoral thesis, Math. Dept., MIT. Cambridge, Mass., 1964.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In order for a computer program to be able to do that, it must at least have the capacity to store 8elected parts of its inputs.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: The crucial test of understanding is not the ability to continue a conversation but to draw valid conclusions from what one is being told.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The whole script constitutes, in a loose way, a model of certain aspects of the world.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of course, the problem is more than one of storage.A great part of it is, in fact, subsumed under the word “selected” used just, above.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Given a language which already possesses semantic content, then a translating processor, even if it operates only syntactically, generates correspondittg expressions of another language to which we can attritmte as “meanings” (possibly multiple--the translator may not be one to one) the “semantic intents” of the generating source expressions; whether we find the result consistent or useful.or both”
What the sources say
- Not checked yetcites [2] 2. Goax, S. Semiotic relationships in ambiguously stratified bmguage systems. Paper presented at Int. Colloq. Algebraic Linguistics and Automatic Theory, Hebrew U. of Jerusalem, Aug. 1964.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is useful to remember that the ELIZA program itself is merely a translating processor in the technical programming sense.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“E L I Z A shows, if nothing else, how easy it is to create and nmiutain the illusion of understanding, hence perhaps of j u d g m e n t”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Rests on an assumption: The human speaker contributes much to clothe ELIZA's responses in plausibility.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“But to encourage its conversational partner to offer inputs from which it can select remedial information, it, must ret, eal its mistmderstanding.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An important consequence of the editing facility built into ELIZA is that a given ELIZA script need not start out to be a large, full-blown scenario.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The MAC system parameters generally contribute so little to the overall response time that conversational interaction with the computer need never involve truly intolerable delays.
“These latter parameters generally contribute so little to the overall response time that conversational interaction with the computer need never involve truly intolerable delays.”
The paper relies on this without support.
The determination that a word is not in a dictionary may well require more computation (i.e., time) than the location of a word which is represented.
“The iirst is serious in that the determination that a word is not in a dictionary may well require more computation (i.e., time) than the location of a word which is represented.”
The paper relies on this without support.
Almost none of the words in any given sentence are represented in the keyword dictionary.
“One stems from the fact that almost none of the words in any given sentence are repre.sented in the keyword dictionary.”
The paper relies on this without support.
The transformation rule mechanism of SLIP permits tagging of words in a text and their subsequent recovery on the basis of one of their tags.
“The transformation rule mechanism of SLIP is such that.it permits tagging of words in a text and their subsequent recovery on the basis of one of their tags.”
The paper relies on this without support.
A human speaker will not defend his illusion that he is being understood against all odds.
“But he will not defend his illusion (that he is being understood) against all odds.”
The paper relies on this without support.
In human conversation a speaker makes certain, perhaps generous, assumptions about his conversational partner.
“In tmnutn eonversat:on a speaker will make certain (perhaps generous) assmnl)lions about his conversational partner.”
The paper relies on this without support.
As long as the partner's responses can be interpreted consistently with those assumptions, the speaker's image of his partner remains unchanged and undamaged.
“As long as ~ it remains possible to interpret the latter's response~ consistently with those assumptions, the speaker's image of his partner remains unchanged, in particular, undanmged.”
The paper relies on this without support.
Responses difficult to interpret consistently may enhance the image of the partner through additional rationalizations.
“Responses wlfieh are difficult to so interpret m a y well result in an enhancement of the inmge of the partner, in additional rationalizations which then make more con> plicated interpretations of his responses reasonable.”
The paper relies on this without support.
When rationalizations become too massive and self-contradictory, the entire image may crumble and be replaced.
“When, however, such rationalizations become too inassive and even self-contradictory, the entire image may erumble and be replaeed by another (“He is not, after all, as s m a r t as I thought he was”).”
The paper relies on this without support.
When the conversational partner is a machine, credibility may be substituted for plausibility.
“When the conversational pa.rtner is a machine (the distinction between nmchine and prograin is here not useful) then the idea of credibility may weU be substituted for that. of pla.'uaibiStfl in the above.”
The paper relies on this without support.
The crucial test of understanding is not the ability to continue a conversation but to draw valid conclusions from what one is being told.
“The crucial test of understanding, as every teacher should know, is not the subject's ability to continue a conversation, but to draw valid conclusions h'om what. he is being told.”
The paper relies on this without support.
Important decisions increasingly tend to be made in response to computer output.
“I m p o r t a n t decisions increasingly tend to be made in response to computer output.”
The paper relies on this without support.
The ultimately responsible human interpreter of “What the machine says” is constantly faced with the need to make credibility judgments.
“The uhimately responsible human interpreter of “What the machine says” is, not unlike the correspondent with E H Z A , constantly faced with the need to make credibility judgments.”
The paper relies on this without support.
The act of writing a script is a kind of programming act and has all the advantages of programming, most particularly that it clearly shows where the programmer's understanding and command of his subject leaves off.
“The act of writing a script is a kind of programming act and has all the advantages of programming, most particularly that it clearly shows where the progranuner's understanding and con> mand of his subject leaves off.”
The paper relies on this without support.
The human speaker contributes much to clothe ELIZA's responses in plausibility.
“The human speaker will, a~ has been said, contribute much to clothe EI,IZA'S responses h: vestmen:s of plausibility.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A technological instantiation of transference can be seen in the first chatbot developed by Weizenbaum where he observed many users confiding in ELIZA. Users projected empathy and understanding onto a program that merely mirrored their words.”
From The Psychology of the Eliza Effect: Anthropomorphism, Social Presence, and Projection in Human-AI Interaction (2025) · cites this paper as Weizenbaum (1966)
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The eponymous ELIZA is a simple rules-based chatbot from the 1960's that users were found to anthropomorphize [56].”
From People cannot distinguish GPT-4 from a human in a Turing test (2025) · cites this paper as [56]
The cited source says what the claim attributes to it.
In the citing paper
“This trend has been present in the discourse for decades and has manifested itself in, for example, in the attribution of human-like qualities to the simple chatbot ELIZA (Weizenbaum, 1966;Berry, 2023) or in the anthropomorphisation of the language used to describe AI algorithms, which McDermott (1976) warned against half a century ago, and which remains problematic today (Shwartz, 2020).”
From There is no such thing as conscious artificial intelligence (2025) · cites this paper as Weizenbaum, 1966
The cited source says what the claim attributes to it.
In the citing paper
“However, what Eliza did reveal was that humans are susceptible to projecting onto a chat bot an illusion of understanding, a valuable lesson that we should keep in mind.”
From Large Language Models and the Reverse Turing Test (2023) · cites this paper as Weizenbaum, 1966
In the citing paper
“ELIZA, developed in the 1960s, exemplified these systems by simulating a Rogerian psychotherapist through simple pattern‐matching and substitution rules”
From Charting the evolution of artificial intelligence mental health chatbots from rule‐based systems to large language models: a systematic review (2025) · cites this paper as [10]
In the citing paper
“To Turing’s point, data have been gathered from independent authors, labs, and across time all illustrating the utility and potential of machines in delivering healthcare and therapy [2, 4, 6–8].”
From When ELIZA meets therapists: a Turing test for the heart and mind (2025) · cites this paper as [2]
In the citing paper
“This pattern of findings is consistent with our original hypothesis and previous research: differences in accurate identification will be close to zero [2, 7, 8].”
From When ELIZA meets therapists: a Turing test for the heart and mind (2025) · cites this paper as [2]
In the citing paper
“Furthermore, some potentially relevant conversational agents developed for the treatment of mental health problems were excluded from this review because of not reporting a mental health outcome measure (eg, ELIZA [2,42-44]).”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [2]
In the citing paper
“The majority of reviews focus on studies from 1998 onward, thereby neglecting first papers on CAs such as well-known first text-based agent ELIZA (Weizenbaum, 1966).”
From Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers (2024) · cites this paper as Weizenbaum, 1966
In the citing paper
“Historically, the first chatbot was developed in 1966 by Weizenbaum (1966).”
From Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers (2024) · cites this paper as Weizenbaum (1966)
Citations
Cites
Weizenbaum, J. (1963) · Communications of the ACM · doi:10.1145/367593.367617 · Cited by 196
Self-citationComputer Simulation of Individual Belief Systems
Abelson, R. P., & Carroll, J. D. (1965) · American Behavioral Scientist · doi:10.1177/000276426500800908 · Cited by 108
Research works printed but not linked: 4
- Gorn, S. Semiotic relationships in ambiguously stratified language systems. Paper presented at Int. Colloq. Algebraic Linguistics and Automatic Theory, Hebrew U. of Jerusalem, Aug. 1964.
- 10.5555/889266 · DOI
- Rogers, C. Client Centered Therapy: Current Practice, Implications and Theory. Houghton Mifflin, Boston, 1951.
- Yngve, J. COMIT Programming Manual. MIT Press, Cambridge, Mass., 1961.
Cited by
Sorted by citations
Items 2,601–2,700 of 4,609
The primary source of intentionality
Natsoulas, T. (1980) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00005926 · Cited by 3
Courseware evaluation: where's the intelligence?*
MacKenzie, I. S. (1990) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.1990.tb00376.x · Cited by 3
The Brain as a Computer or Can Computers Think?
Haken, H. (1996) · Springer series in synergetics · doi:10.1007/978-3-642-79570-1_19 · Cited by 3
Interactive Technology: The Role of Passivity
Thimbleby, H. (1979) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/107118137902300120 · Cited by 3
(2014) · Natural language processing · doi:10.1075/nlp.11.06ch6 · Cited by 3
Vernmark, K. (2017) · doi:10.3384/diss.diva-142389 · Cited by 3
Overview of Artificial Intelligence
Hildmann, H., & Hirsch, B. (2018) · Springer eBooks · doi:10.1007/978-3-319-08234-9_228-1 · Cited by 3
Tür, G., & Mori, R. D. (2011) · Wiley eBooks · doi:10.1002/9781119992691.ch1 · Cited by 3
Marzano, G. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2255-3.ch516 · Cited by 3
Assistive Chatbots for healthcare: a succinct reviewPreprint
Bhattacharya, B. S. (2023) · doi:10.36227/techrxiv.23741469.v1 · Cited by 3
Assistive Chatbots for healthcare: a succinct reviewPreprint
Bhattacharya, B. S. (2023) · doi:10.36227/techrxiv.23741469 · Cited by 3
Zafar, A., Parthasarathy, V. B., Van, C. L., Shahid, S., Khan, A. I., & Shahid, A. (2023) · doi:10.36227/techrxiv.24009351 · Cited by 3
Overview of Artificial Intelligence
Hildmann, H., & Hirsch, B. (2024) · Springer eBooks · doi:10.1007/978-3-031-23161-2_228 · Cited by 3
Qiu, W., Chit, L. S., Jamil, N. B., Thway, M., Hwee, S. N. S., Zhang, L., Lim, F. S., & Lai, J. W. (2024) · doi:10.35542/osf.io/7yga3 · Cited by 3
Academic Psychiatry in the Age of Artificial Intelligence
King, D. R., Liu, H. Y., & Brenner, A. M. (2025) · Academic Psychiatry · doi:10.1007/s40596-025-02112-y · Cited by 3
Lukyanenko, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5153894 · Cited by 3
How Not to Collaborate With Large Language Models
Zebrowski, R. L. (2025) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0071 · Cited by 3
AI-Driven IT and Its Potentials – A State-of-the-Art ApproachPreprint
Glintschert, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3576417 · Cited by 3
The Dawn of AI in Mental Health: Balancing Innovation, Risk, and Regulation
Wiederhold, B. K. (2026) · Cyberpsychology Behavior and Social Networking · doi:10.1177/21522715251414068 · Cited by 3
Validation of Context Respectful Counseling Agent
Shinozaki, T., Yamamoto, Y., Tsuruta, S., & Knauf, R. (2015) · 2015 IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.180 · Cited by 2
Alotaibi, M., Alotaibi, M. D., Alamri, L. H., Alkadi, D., Alsahali, S. F., Aljameel, S. S., & Youldash, M. M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-63128-4_56 · Cited by 2
Methodology and Empirical Strategy
Nuccio, M., & Mogno, S. (2023) · Contributions to management science · doi:10.1007/978-3-031-26867-0_3 · Cited by 2
Langer, H., & Merkel, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35921-7_13 · Cited by 2
ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ДІАГНОСТИЦІ ПСИХОЛОГІЧНОГО СТАНУ
ОСАДЧИЙ, О. (2023) · INFORMATION TECHNOLOGY AND SOCIETY · doi:10.32689/maup.it.2023.1.8 · Cited by 2
Neuropedagogy of Communication-Imitation Opportunities for the Ukrainian English Teacher
SAVCHENKO, O., Пристай, Г. В., Hulych, M., Соболь, Л., & Stupnytska, H. (2023) · BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE · doi:10.18662/brain/14.3/468 · Cited by 2
Chen, S., & Huang, Y. (2023) · International Conference on Computer Engineering and Distance Learning (CEDL) · doi:10.1109/cedl60560.2023.00039 · Cited by 2
Conversational artificial intelligence: the interface with the patient concerns inventory
O'Connell, J. E. A., Gilmartin, E., Roche, D., & Rogers, S. N. (2024) · British Journal of Oral and Maxillofacial Surgery · doi:10.1016/j.bjoms.2024.02.002 · Cited by 2
Designing a Large Language Model Based Open Data Assistant for Effective Use
Schelhorn, T. C., Gnewuch, U., & Maedche, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61175-9_27 · Cited by 3
Advancing Accessibility through Automatic Speech Recognition and NLP Integration
Shivaraj, G. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.62443 · Cited by 2
SESAME - self-supervised framework for extractive question answering over document collections
Batista, V. A., Gomes, D. D. S. M., & Evsukoff, A. G. (2024) · Journal of Intelligent Information Systems · doi:10.1007/s10844-024-00869-6 · Cited by 2
Klella, A. S. A., & Mrghem, Z. M. O. (2024) · أطراس · doi:10.70091/atras/ai.27 · Cited by 2
Grasping AI Entanglements: Digital Feminism and Generative AI
Dufva, T. S. (2024) · Springer eBooks · doi:10.1007/978-3-031-66528-8_11 · Cited by 2
Temporal Extraction and Retrieval
Alonso, O., & Spaniol, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674137 · Cited by 2
Artificial Companions and Spiritual Enhancement
Wilks, Y. A. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009031721.019 · Cited by 2
Charanya, P., S, S. R. B., Naveen Kumar, T., & Pradheeban, A. (2024) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces63552.2024.10859659 · Cited by 2
Artificial intelligence integration in three iOS pronunciation apps
Kaiser, D. (2024) · Journal of Second Language Pronunciation · doi:10.1075/jslp.24052.kai · Cited by 2
Alhaydar, A. (2025) · Journal of AI · doi:10.61969/jai.1613027 · Cited by 2
Designing a Large Language Model Based Conversational Agent for Language Acquisition
Neis, N., Spleth, P., Kudlek, C., Zarnekow, R., & Winkelmann, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93976-1_15 · Cited by 2
Classification of imbalanced user reviews using a generative approach
Javid, B., & Mashayekhi, H. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01477-0 · Cited by 2
ChatbotLLM - Training Educational Chatbots on the Materials Uploaded by Teachers
Silva, S. D., & Chang, M. (2025) · International Conference on Information and Education Technology (ICIET) · doi:10.1109/iciet66371.2025.11046253 · Cited by 2
A Chatbot for Frequently Asked Questions using TF-IDF and Query Expansion Techniques
Ibrahim, M. S., Hadi, A. A., Sallibi, A. D., & Nafea, A. A. (2025) · doi:10.24271/psr.2025.487515.1805 · Cited by 2
Automating the conformity assessment of Cyber-Physical Systems software
Nguyen, G. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3731468 · Cited by 2
Alebachew, K., Sharma, D. P., & Samuel, M. (2025) · Discover Data · doi:10.1007/s44248-025-00077-9 · Cited by 2
Dwivedi, M., Saxena, V., & Pandey, B. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02707-6 · Cited by 2
O DESAFIO DA AUTONOMIA COGNITIVA HUMANA NA ERA DA INTELIGÊNCIA ARTIFICIAL
Arrabal, A. K., & França, M. E. B. (2025) · Convergências estudos em Humanidades Digitais · doi:10.59616/cehd.v1i8.2272 · Cited by 2
Large Language Models: Evolution, Architecture, Applications, and Future Horizons
Patel, S., & Dholakiya, R. A. (2025) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa66339.2025.11170884 · Cited by 2
Rusanov, V. (2025) · Psychosomatic Medicine and General Practice · doi:10.26766/pmgp.v10i3.648 · Cited by 2
Real-Time Wildlife Intrusion Detection System Using IoT and YOLOv8
Patkar, A., Hole, P., & Salvi, L. S. (2025) · Journal of Smart Sensors and Computing · doi:10.64189/ssc.25209 · Cited by 2
The Effect of REACT Strategy Integrated with Artificial Intelligence on Academic Achievement
Güneş, L., & Yılmazlar, M. (2025) · International Journal of Education in Mathematics Science and Technology · doi:10.46328/ijemst.5698 · Cited by 2
A Generative AI Perspective on the Turing Test: Passed but Not Vindicated
Berghel, H. (2025) · Computer · doi:10.1109/mc.2025.3600889 · Cited by 2
Implementing a RAG-Based Chatbot in Industrial Operations: A Real-World Case Study
Bahçeci, A. Z., Demirok, F., Kayaoğlu, M., & Topçu, B. (2025) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu67174.2025.11208259 · Cited by 2
Kian, M. J., Zong, M., Fischer, K., Velentza, A., Singh, A., Shrestha, K., Sang, P., Upadhyay, S., Browning, W., Faruki, M. A., et al. (2025) · IEEE International Symposium on Robot and Human Interactive Communication · doi:10.1109/ro-man63969.2025.11217887 · Cited by 4
A large language model-based chatbot system framework for urban planners
Zhou, X., Jeong, B., & Chapple, K. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130307 · Cited by 2
Assessing Machine Learning Models for Enhancing Intent Detection in Tourism Chatbots
Ouaddi, C., Benaddi, L., Jakimi, A., Chehri, A., & Saadane, R. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.441 · Cited by 2
Neuhaus, R., Laschke, M., Ringfort-Felner, R., & Hassenzahl, M. (2025) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/3757980.3758001 · Cited by 2
Implementation of an AI‐Based Expert System for Functional Safety of Machinery
Iyenghar, P. (2025) · Risk Analysis · doi:10.1111/risa.70151 · Cited by 2
Liu, C., Wang, W. Y. C., & Khan, G. F. (2025) · Industrial Management & Data Systems · doi:10.1108/imds-05-2025-0609 · Cited by 2
Pütz, O. (2025) · Journal of Linguistic Anthropology · doi:10.1111/jola.70036 · Cited by 2
Attig, C., Winzer, L., Schrills, T., Zoubir, M., Mortaga, M., Wollstadt, P., Wiebel-Herboth, C. B., & Franke, T. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100246 · Cited by 5
One-Sided Parasociality with AI
Degen, J. L. (2025) · Palgrave Studies in Cyberpsychology. · doi:10.1007/978-3-032-07438-6_5 · Cited by 2
Sawant, D. S. M. A. D. N. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-30718 · Cited by 2
The Evolution of AI Chips: From Cloud to Edge, Powering the Future of Artificial Intelligence
Long, N. V., Vinh, T. N., Quyen, H. V., Duyen, N. T. M., Hoa, P. V., & Tuan, M. A. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-14055-5_14 · Cited by 3
Prashant, Poriye, M., Mittal, P., & Sharma, N. (2025) · AIJR Proceedings · doi:10.21467/proceedings.7.6.36 · Cited by 2
Gestión del cambio para la sostenibilidad socioeconómica de las naciones
(2025) · Fondo Editorial Universitario Servando Garcés de la Universidad Politécnica Territorial de Falcón Alonso Gamero / Alianza de Investigadores Internacionales S.A.S. eBooks · doi:10.47212/gestiondelcambiosostenibilidad2025 · Cited by 2
Wei, H., Xiang, B., Zheng, X., & Yang, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2630076 · Cited by 2
Du, L., Li, Y., Long, Y., & Chen, S. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1733384 · Cited by 2
Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health
Verhees, F. G., Wiest, I. C., Kather, J. N., Kambeitz, J., & Mikoláš, P. (2026) · Pharmacopsychiatry · doi:10.1055/a-2816-2869 · Cited by 3
History of chatbots and their emerging applications in textiles, polymers, and sustainability
Al-Amin, M., Hossain, M. E., Ali, M. S., Salam, A., Ali, A., Ullah, A., Alam, N., Akhter, M. N., & Mahmood, S. A. (2026) · Sustainable materials and technologies · doi:10.1016/j.susmat.2026.e01967 · Cited by 3
Rocha, A., Ferreira, J., de Oliveira, P. C., Alves, M., & Sousa, A. J. (2026) · Computer Applications in Engineering Education · doi:10.1002/cae.70176 · Cited by 2
Parhi, R., Jena, G. K., Garg, A., Dhobi, M., & Mondal, S. (2026) · Journal of Holistic Integrative Pharmacy · doi:10.1016/j.jhip.2026.03.002 · Cited by 2
Leveraging chatbots for enhanced decision-making: a comprehensive literature review
Simelane, P. M., & Kittur, J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1748544 · Cited by 2
Using artificial intelligence in marketing
Kamiński, J. (2026) · Rozprawy Społeczne · doi:10.29316/rs/219966 · Cited by 2
AI as Character: Real Patterns in Fictive Interactions
Bernini, M. (2025) · Style · doi:10.5325/style.59.3.0466 · Cited by 2
Who Is Talking to the AI Assistants? A Consumer Segmentation Analysis
Fares, O. H., Yang, X., & Lee, S. H. (2026) · International Journal of Consumer Studies · doi:10.1111/ijcs.70219 · Cited by 2
Zucchetti, A., Bulgari, V., Davini, C., Ghio, M., Dosoli, M., Gregorelli, M., Nibbio, G., Barlati, S., & Vita, A. (2026) · ALPHA PSYCHIATRY · doi:10.31083/ap45104 · Cited by 2
Charismatic machines: On the epistemic power of generative AI within platform convergence
Barisione, M. (2026) · New Media & Society · doi:10.1177/14614448261441417 · Cited by 2
Almugren, I., Varghese, I. K., Agarwal, S., Kurucz, A., Fontana, D., & Nobis, M. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-09-2025-1318 · Cited by 2
Yang, H., Chang, F., Muroi, F., Liu, Z., Zhang, W., & Cai, J. (2026) · Psychotherapy and Psychosomatics · doi:10.1159/000552072 · Cited by 2
Generative AI for Mental Well-Being and Insights From High and Low Resource Languages
Sitoula, S., Bhatt, L. P., Pokhrel, A., & Neupane, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7340-9.ch012 · Cited by 2
Educating What AI Cannot: Character and the Future of Higher Education
Grove, M. J., & Harrison, T. (2026) · British Journal of Educational Studies · doi:10.1080/00071005.2026.2678972 · Cited by 3
Zhang, Y., Ma, Y., Fu, D., Portales, S. Z., Fjeld, M., & Kragić, D. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2675404 · Cited by 2
ChatPlanner: A large language model framework for personalized public transit routing
Yang, T., Xue, C., & Chen, J. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.106017 · Cited by 2
Robert, L., & Sauvage, J. (2026) · AI-Linguistica Linguistic Studies on AI-Generated Texts and Discourses · doi:10.62408/ai-ling.v4i1.51 · Cited by 2
Seeking Late Night Life Lines: Experiences of Conversational AI Use in Mental Health Crisis
Ajmani, L., Ghosh, A., Kaveladze, B. T., Kim, E., Namuduri, K., Nguyen, T., Okoli, E., Schleider, J. L., Ford, D., & Suh, J. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812256 · Cited by 6
Yapay Zekâ Temel İlkeleri ve Diş Hekimliğinde Kullanım Alanları
Dede, Y., & Dede, İ. H. (2026) · Doğal Yaşam Tıbbı Dergisi · doi:10.71051/jnlm.1970094 · Cited by 2
Artificial intelligence-driven environments as emerging contexts in suicide risk
Dangaich, R. (2026) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2026.105103 · Cited by 2
Evaluating Role-Consistency in LLMs for Counselor Training
Rudolph, E., Engert, N., & Albrecht, J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-21997-8_26 · Cited by 2
Yoo, Y., Kim, M., Kim, S., Lee, G., & Kim, J. (2026) · Digital Health · doi:10.1177/20552076261464801 · Cited by 2
Lastretti, M., Manchia, M., Fraschini, M., Faa, A., Coghe, F., Paribello, P., Pinna, M., Tiwari, E., Suri, J. S., Saba, L., et al. (2026) · Psychiatry International · doi:10.3390/psychiatryint7050194 · Cited by 2
Introducing Consciousnet : Internet Content as an Environment for Human-Machine Interaction
Catania, V., Patti, D., & Sciacca, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07230-2_37 · Cited by 2
An interactive stuffed-toy device for communicative description on Twitter
Mase, H., Yoshida, Y., & Yonezawa, T. (2014) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scis-isis.2014.7044832 · Cited by 2
UNDERSTANDING ARITHMETIC PROBLEMS IN TURKISH
Say, A. C. C. (2001) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001401000897 · Cited by 2
Machine Cognition and the Downloading of Scientific Intellect
Aborn, M. (1988) · The Annals of the American Academy of Political and Social Science · doi:10.1177/0002716288495001013 · Cited by 2
The Use of Chatterbots in the Language Classroom
Tanghe, S. (2009) · STEM Journal · doi:10.16875/stem.2009.10.2.147 · Cited by 2
Programming Without Program or How to Program in Natural Language Utterances
Wheatman, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71078-5_5 · Cited by 2
ChaD: Chat-Oriented Dialog Systems
Rudnicky, A. I. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-92108-2_7 · Cited by 2
Modrzejewski, M., & Rokita, P. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00692-1_5 · Cited by 2
An Approach for Building Effective Real Estate Chatbots in Vietnamese
Cao, T., & Nguyen, Q. H. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-49536-7_19 · Cited by 2
Summarizing Behavioral Change Goals from SMS Exchanges to Support Health Coaches
Gupta, I., Eugenio, B. D., Ziebart, B. D., Liu, B., Gerber, B. S., & Sharp, L. K. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.31 · Cited by 3
Metadata
- Authors
- Weizenbaum, J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Weizenbaum, Joseph
- Venue
- Communications of the ACM
- Published
- 1966-01-01
- Volume
- 9
- Issue
- 1
- Pages
- 36-45
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W4236521339
- Cited by
- 4,452
- References
- 2
- Citations by year
- 2026: 450 · 2025: 576 · 2024: 524 · 2023: 465 · 2022: 298 · 2021: 310 · 2020: 245 · 2019: 231 · 2018: 197 · 2017: 103 · 2016: 109 · 2015: 63 · 2014: 100 · 2013: 75 · 2012: 44
- FWCI
- 10.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Funders
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- Eliza, natural language conversation, human-computer interaction, decomposition rules, response generation, script editing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
