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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.
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Claims the paper makes
25 No basis given · 14 Own interpretation · 3 Not checked yet
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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.”
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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.”
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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.”
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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.”
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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.”
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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.”
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- It may state two separate claims as one. 9
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In the paper
“The largest dictionary so far attempted contains about 50 keywords.”
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In the paper
“ELIZA scripts exist (at, this writing) in Welsh attd Gernmn as well as in English.”
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In the paper
“Of course, lhe set of transformation rules can be guaranteed to terminate with a decomposition rule which nmst match.”
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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.”
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In the paper
“Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text.”
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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.”
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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.”
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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.”
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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.”
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In the paper
“This mechanism makes it possible to, in effect, test on key phrases as opposed to single key words.”
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In the paper
“No list named in any of the words of the KEY vector contains more than two key list structures.”
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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.”
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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•”
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In the paper
“The script that has formed the basis for most of this discussion happens to be one with an overwhehningly psychological orientation.”
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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.”
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In the paper
“An important property of ELIZA is that a script is data; i.e., it is not part of the program itself.”
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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.”
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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.”
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“There is, however, another mechanism which causes the s y s t e m to respond more spectacularly in the absence of a key.”
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“Responses are generated by reassembly rules associated with selected decomposition rules.”
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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,”
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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.”
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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).”
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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.”
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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.”
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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.”
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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.”
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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.
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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.”
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- 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.
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In the paper
“The whole script constitutes, in a loose way, a model of certain aspects of the world.”
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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.”
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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.
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In the paper
“It is useful to remember that the ELIZA program itself is merely a translating processor in the technical programming sense.”
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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”
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- 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.”
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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.”
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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 301–400 of 4,609
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Lokman, A. S., & Ameedeen, M. A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-02683-7_75 · Cited by 122
Neumann, A. T., Arndt, T., Köbis, L., Meissner, R., Martin, A., de Lange, P., Pengel, N., Klamma, R., & Wollersheim, H.-W. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.668220 · Cited by 121
Debets, T., Banihashem, S. K., Brinke, D. J.-T., Vos, T. E. J., Wenniger, G. M. D. B., & Camp, G. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105323 · Cited by 124
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Brunette, E. S., Flemmer, R. L. C., & Flemmer, C. L. (2009) · International Conference on Autonomous Robots and Agents (ICARA) · doi:10.1109/icara.2000.4804025 · Cited by 120
Computer-aided treatments of mental health problems.
Marks, I. M., Shaw, S. M., & Parkin, R. (1998) · Clinical Psychology Science and Practice · doi:10.1111/j.1468-2850.1998.tb00141.x · Cited by 113
Relational Agents in Clinical Psychiatry
Bickmore, T., & Gruber, A. (2010) · Harvard Review of Psychiatry · doi:10.3109/10673221003707538 · Cited by 112
Yusuf, H., Money, A., & Daylamani-Zad, D. (2025) · Educational technology research and development · doi:10.1007/s11423-025-10447-4 · Cited by 112
Response Generation by Context-Aware Prototype Editing
Wu, Y., Wei, F., Huang, S., Wang, Y., Li, Z., & Zhou, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017281 · Cited by 112
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Zhang, Z., & Wang, J. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1444382 · Cited by 112
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Kobak, K. A., Greist, J. H., Jefferson, J. W., & Katzelnick, D. J. (1996) · Psychopharmacology · doi:10.1007/s002130050089 · Cited by 112
Wang, H., Gupta, S., Singhal, A., Muttreja, P., Singh, S., Sharma, P., & Piterova, A. (2022) · Journal of Medical Internet Research · doi:10.2196/29969 · Cited by 111
Examining students’ intention to use ChatGPT: Does trust matter?
Rahman, M. S., Sabbir, M. M., Zhang, D. J., Moral, I. H., & Hossain, G. M. S. (2023) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8956 · Cited by 111
Considering the role of human empathy in AI-driven therapy
Rubin, M., Arnon, H., Huppert, J. D., & Perry, A. (2024) · JMIR Mental Health · doi:10.2196/56529 · Cited by 111
The Combination of Artificial Intelligence and Extended Reality: A Systematic Review
Reiners, D., Davahli, M. R., Karwowski, W., & Cruz‐Neira, C. (2021) · Frontiers in Virtual Reality · doi:10.3389/frvir.2021.721933 · Cited by 111
Hari, H., Iyer, R., & Sampat, B. H. (2021) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2021.1988487 · Cited by 111
Towards User-Centric Guidelines for Chatbot Conversational Design
Silva, G. R. S., & Canedo, E. D. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2118244 · Cited by 111
Cohen, G. E., & KERR, B. A. (1999) · Journal of Technology in Human Services · doi:10.1300/j407v15n04_02 · Cited by 111
Fast, E., Chen, B., Mendelsohn, J., Bassen, J., & Bernstein, M. S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174047 · Cited by 110
Building effective question answering characters
Leuski, A., Patel, R., Traum, D. R., & Kennedy, B. (2006) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.3115/1654595.1654600 · Cited by 110
Bird, T., Mansell, W., Wright, J., Gaffney, H., & Tai, S. (2018) · Behavioural and Cognitive Psychotherapy · doi:10.1017/s1352465817000820 · Cited by 109
The Interrogator as Critic: The Turing Test and the Evaluation of Generative Music Systems
Ariza, C. (2009) · Computer Music Journal · doi:10.1162/comj.2009.33.2.48 · Cited by 109
Złotowski, J. A., Sumioka, H., Eyssel, F., Nishio, S., Bartneck, C., & Ishiguro, H. (2018) · International Journal of Social Robotics · doi:10.1007/s12369-018-0476-5 · Cited by 109
Herbener, A. B., & Damholdt, M. F. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103409 · Cited by 107
How Do You Want Your Chatbot? An Exploratory Wizard-of-Oz Study with Young, Urban Indians
Thies, I. M., Menon, N., Magapu, S., Subramony, M., & O’Neill, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67744-6_28 · Cited by 107
The milk of human intentionality
Dennett, D. C. (1980) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0000580x · Cited by 106
Chatbot Utilization for Medical Consultant System
Rosruen, N., & Samanchuen, T. (2018) · Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) · doi:10.1109/times-icon.2018.8621678 · Cited by 106
Impact of the Fourth Industrial Revolution on the Health Sector: A Qualitative Study
Castro, J., & Araújo, N. M. F. (2020) · Healthcare Informatics Research · doi:10.4258/hir.2020.26.4.328 · Cited by 106
Levesque, H. J. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2014.03.007 · Cited by 105
Ghose, S., & Barua, J. J. (2013) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/iciev.2013.6572650 · Cited by 105
Plevris, V., Papazafeiropoulos, G., & Ríos, A. J. (2023) · AI · doi:10.3390/ai4040048 · Cited by 105
Research and Reviews in Question Answering System
Dwivedi, S. K., & Singh, V. (2013) · Procedia Technology · doi:10.1016/j.protcy.2013.12.378 · Cited by 105
Steps toward graceful interaction in spoken and written man-machine communication
Hayes, P. J., & Reddy, D. R. (1983) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(83)80049-2 · Cited by 105
Knote, R., Janson, A., Söllner, M., & Leimeister, J. M. (2020) · Journal of the Association for Information Systems · doi:10.17705/1jais.00667 · Cited by 103
Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study
Espartinez, A. S. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100264 · Cited by 116
Reclaiming AI as a Theoretical Tool for Cognitive Science
van Rooij, I., Guest, O., Adolfi, F., de Haan, R., Kolokolova, A., & Rich, P. (2024) · Computational Brain & Behavior · doi:10.1007/s42113-024-00217-5 · Cited by 131
Demmer, T. R., Kühnapfel, C., Fingerhut, J., & Pelowski, M. J. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107875 · Cited by 112
Ruggiano, N., Brown, E. L., Roberts, L., Framil Suarez, C. V., Luo, Y., Hao, Z., & Hristidis, V. (2021) · Journal of Medical Internet Research · doi:10.2196/25006 · Cited by 100
“One of the first established chatbots was programmed in the 1960s, called ELIZA, which was used for text-based psychiatric interviews [11].”
Demystifying Social Bots: On the Intelligence of Automated Social Media Actors
Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H., & Grimme, C. (2020) · Social Media + Society · doi:10.1177/2056305120939264 · Cited by 100
Feine, J., Gnewuch, U., Morana, S., & Maedche, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-39540-7_6 · Cited by 99
A bibliometric analysis of artificial intelligence chatbots in educational contexts
Lin, Y., & Yu, Z. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-12-2022-0165 · Cited by 99
The rise of chatbots – new personal assistants in foreign language learning
Dokukina, I. V., & Gumanova, J. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.02.212 · Cited by 99
Researching online counselling and psychotherapy: The past, the present and the future
Hanley, T. (2020) · Counselling and Psychotherapy Research · doi:10.1002/capr.12385 · Cited by 99
A Systems Framework for the Field Personality
Mayer, J. D. (1998) · Psychological Inquiry · doi:10.1207/s15327965pli0902_10 · Cited by 99
Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response
Monett, D., Lewis, C. W. P., Þórisson, K. R., Bach, J., Baldassarre, G., Granato, G., Berkeley, I. S. N., Chollet, F., Crosby, M., Shevlin, H., et al. (2020) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2020-0003 · Cited by 110
Hornung, O., & Smolnik, S. (2021) · Electronic Markets · doi:10.1007/s12525-021-00493-0 · Cited by 98
Biomedical Natural Language Processing
Cohen, K. B., & Demner‐Fushman, D. (2014) · Natural language processing · doi:10.1075/nlp.11 · Cited by 98
Scholten, M. R., Kelders, S. M., & Van Gemert-Pijnen, J. E. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7351 · Cited by 97
“Computers as social actors Systematic search: [22] Hand search: [52-54] (concept=multimedia) Embodied conversational agents (ECAs) have the ability to have an open verbal dialogue with users.”
BackgroundMethodBeyond Dyadic Interactions: Considering Chatbots as Community Members
Seering, J., Luria, M., Kaufman, G., & Hammer, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300680 · Cited by 97
Mind, Body, World: Foundations of Cognitive Science
Dawson, M. R. W. (2013) · Athabasca University Press eBooks · doi:10.15215/aupress/9781927356173.01 · Cited by 98
Is neuro-symbolic AI meeting its promises in natural language processing? A structured review
Hamilton, K., Nayak, A., Božić, B., & Longo, L. (2022) · Semantic Web · doi:10.3233/sw-223228 · Cited by 119
Ozuem, W., Ranfagni, S., Willis, M., Salvietti, G., & Howell, K. E. (2024) · Psychology and Marketing · doi:10.1002/mar.22051 · Cited by 97
Chaves, A. P., & Gerosa, M. A. (2020) · Figshare · doi:10.6084/m9.figshare.13207221 · Cited by 97
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Martín-Antón, L. J., González Díez, I., & Almeida, L. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e12843 · Cited by 96
Artificial Intelligence and Virtual Worlds – Toward Human-Level AI Agents
Petrović, V. M. (2018) · IEEE Access · doi:10.1109/access.2018.2855970 · Cited by 102
Survey on Intelligent Chatbots: State-of-the-Art and Future Research Directions
Almansor, E. H., & Hussain, F. K. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-22354-0_47 · Cited by 103
When brain-inspired AI meets AGI
Zhao, L., Zhang, L., Wu, Z., Chen, Y., Dai, H., Yu, X., Liu, Z., Zhang, T., Hu, X., Jiang, X., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100005 · Cited by 116
Kjell, O., Kjell, K., & Schwartz, H. A. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115667 · Cited by 115
Modeling Multimodal Expression of User’s Affective Subjective Experience
Bianchi‐Berthouze, N., & Lisetti, C. (2002) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1013365332180 · Cited by 97
IntelliBot: A Dialogue-based chatbot for the insurance industry
Nuruzzaman, M., & Hussain, O. K. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105810 · Cited by 95
The Evolution of Chatbots in Tourism: A Systematic Literature Review
Calvaresi, D., Ibrahim, A. H., Calbimonte, J.-P., Schegg, R., Fragnière, E., & Schumacher, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-65785-7_1 · Cited by 95
Seymour, W., & Van Kleek, M. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479515 · Cited by 94
Tracery: An Author-Focused Generative Text Tool
Compton, K., Kybartas, B., & Mateas, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27036-4_14 · Cited by 97
Research into Interactive Digital Narrative: A Kaleidoscopic View
Murray, J. H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04028-4_1 · Cited by 94
Malik, R., Shrama, A., Trivedi, S., & Mishra, R. (2021) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v16i18.24315 · Cited by 93
Using Learning Analytics to Understand the Design of an Intelligent Language Tutor – Chatbot Lucy
Fei, Y., & Petrina, S. (2013) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2013.041117 · Cited by 93
Liao, Q. V., Davis, M. J., Geyer, W., Muller, M., & Shami, N. S. (2016) · Designing Interactive Systems Conference · doi:10.1145/2901790.2901842 · Cited by 93
Engaging Museum Visitors with AI: The Case of Chatbots
Gaia, G., Boiano, S., & Borda, A. (2019) · Springer series on cultural computing · doi:10.1007/978-3-319-97457-6_15 · Cited by 93
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Collins, K. M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi‐Xuan, T., Ho, M. K., Mansinghka, V. K., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01991-9 · Cited by 125
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Hua, Y., Liu, F., Yang, K., Li, Z., Na, H., Sheu, Y.-H., Zhou, P., Moran, L. V., Ananiadou, S., Clifton, D. A., et al. (2025) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-025-00363-y · Cited by 92
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Motlagh, F. N., Hajizadeh, M., Majd, M., Najafi, P., Cheng, F., & Meinel, C. (2024) · arXiv · doi:10.48550/arxiv.2402.00891 · Cited by 92
“From Eliza as a pattern recognition chatbot in the 1960s [89], over the years several advancements pushed Natural Language Processing (NLP) forward, such as long short-term memories to handle a wide range of data [35], Stanford CoreNLP suite [56] providing a collection of algorithms to perform…”
Tian, D., Li, M., Ren, Q., Zhang, Z., Han, S., & Shen, Y. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104670 · Cited by 92
The Influence of Information Technology on Patient-Physician Relationships
Weiner, M., & Biondich, P. (2006) · Journal of General Internal Medicine · doi:10.1111/j.1525-1497.2006.00307.x · Cited by 91
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Heersmink, R., de Rooij, B., Clavel Vázquez, M. J., & Colombo, M. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09777-3 · Cited by 91
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Madianou, M. (2021) · Information, Communication & Society · doi:10.1080/1369118x.2021.1909100 · Cited by 91
Feedpal: Understanding Opportunities for Chatbots in Breastfeeding Education of Women in India
Yadav, D., Malik, P., Dabas, K., & Singh, P. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359272 · Cited by 90
The Digital Therapeutic Alliance and Human-Computer Interaction
D'Alfonso, S., Lederman, R., Bucci, S., & Berry, K. (2020) · JMIR Mental Health · doi:10.2196/21895 · Cited by 90
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Lappalainen, Y., & Narayanan, N. (2023) · Journal of Web Librarianship · doi:10.1080/19322909.2023.2221477 · Cited by 89
Toward a design theory for virtual companionship
Strohmann, T., Siemon, D., Khosrawi-Rad, B., & Robra-Bissantz, S. (2022) · Human-Computer Interaction · doi:10.1080/07370024.2022.2084620 · Cited by 95
A Roadmap to Computer-Based Psychotherapy in the United States
Cartreine, J. A., Ahern, D. K., & Locke, S. E. (2010) · Harvard Review of Psychiatry · doi:10.3109/10673221003707702 · Cited by 99
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Yan, X., Yue, T., Winkler, D. A., Yin, Y., Zhu, H., Jiang, G., & Yan, B. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00070 · Cited by 89
"Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI
Yan, R. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/778 · Cited by 88
Iris: An AI-Driven Virtual Tutor for Computer Science Education
Bassner, P., Frankford, E., & Krusche, S. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653543 · Cited by 101
Hayles, N. K. (2006) · Critical Inquiry · doi:10.1086/509749 · Cited by 87
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Zhai, C., & Massung, S. (2016) · doi:10.1145/2915031.2915050 · Cited by 88
von Wolff, R. M., Hobert, S., & Schümann, M. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.013 · Cited by 86
Döllner, J. (2020) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-020-00102-3 · Cited by 95
Implementation of Chatbot in Online Commerce, and Open Innovation
Illescas-Manzano, M. D., López, N. V., González, N. A., & Rodríguez, C. C. (2021) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc7020125 · Cited by 86
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The Handbook on Socially Interactive Agents
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“In healthcare, the first example of a computer program used as a conversational agent was Joseph Weizenbaum’s ELIZA, a program that mimicked a Rogerian psychotherapist that was able to rephrase the patient’s sentences as questions and provide prere-corded answers [2].”
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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
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