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Chatbot

Software application designed to simulate conversation with human users, often used in digital narratives to provide interactive storytelling experiences
Lina Ruth Harder 2026-05-27

Explication

Chatbots span from simple rule-based interfaces to large-scale neural models capable of generating human-like dialogue. The term sits at the intersection of artificial intelligence, human-computer interaction, and computational linguistics. In contemporary digital culture, chatbots function not only as tools but also as narrative interfaces. They treat users as participants in a conversation and generate text that unfolds sequentially.  

Chatbots are now embedded in smartphones, websites, and platforms in customer service, education, entertainment, and healthcare. Adamopoulou and Moussiades (5; fig. 3) propose a seven-part classification. By knowledge domain, chatbots may be closed, open, or generic. By service, they support interpersonal, intrapersonal, or inter-agent use. Their goals vary among informative, task-based, and conversational. Response methods include rule-based, retrieval-based, or generative systems. They may be human-mediated or autonomous, open-source or commercial, and operate through text, voice, or image. conversational. Response methods include rule-based, retrieval-based, or generative systems. They may be human-mediated or autonomous, open-source or commercial, and operate through text, voice, or image. 

The compound term chatbot, formed from chat and robot, reflects shifting notions of automation. Robot, (see Moravec), derived from the Czech robota (forced labour), entered public discourse through Karel Čapek”s 1920 play R.U.R. (Rossum’s Universal Robots), in which artificial workers perform industrial labour (Čapek). Chat, originating in the Middle English period as a clipping of chatter, moved from informal speech to real-time digital exchange. Michael Mauldin (16–17) coined the term chatbot in 1994, describing his game-based ChatterBot player as a conversational agent/robot capable of interacting with human players and producing contextually appropriate responses. The term chatbot thus marks a transition from machines associated with manual labour to systems designed for communicative labour. 

Alan Turing’s 1950 paper “Computing Machinery and Intelligence” introduced the imitation game as a test for machine intelligence (Turing 433). The Turing Test established indistinguishability from human speakers as a design goal. Early chatbots were rule-based. ELIZA (1966), developed by Joseph Weizenbaum, used pattern matching to simulate a psychotherapist. PARRY (1972), by Kenneth Colby, introduced internal state variables to mimic a paranoid schizophrenic. Richard Wallace”s A.L.I.C.E. (Artificial Linguistic Internet Computer Entity, 1995) relied on Artificial Intelligence Markup Language (AIML) to structure dialogue.  

In the 1990s, statistical methods expanded design strategies. Markov chains, introduced by Andrey Markov in 1906 to model stochastic processes, that is, systems that evolve according to probability rather than fixed rules, were applied in HeX, the 1997 Loebner Prize winner, to predict word sequences (Al-Amin et al. 5). SmarterChild (2001) by Mollnár and Zoltán, delivered weather updates and sports scores on instant messaging platforms using rule-based responses within a closed domain (Hsu). 

The 2010s saw a surge in the application of machine learning and natural language processing. Voice-based and internet-connected assistants like Siri (2011), Alexa, and Cortana (both 2014) brought chatbots into mainstream consumer use (Adamopoulou and Moussiades 3). Microsoft”s XiaoIce (2014) applied deep learning to simulate emotional intelligence through a teenage persona (Zemčík 14–15). Replika, launched in 2017 for companionship purposes (Depounti et al.), operates in an open domain, supports intrapersonal interaction, and uses generative models across text and voice. Transformer architectures, introduced in 2017 in “Attention Is All You Need” (Vaswani et al.), enable models to follow and connect ideas across longer conversations. OpenAI”s GPT (Generative Pre-trained Transformer) models, beginning with GPT-2 (2019) and culminating in the public release of ChatGPT (November 2022), exemplify this shift. Trained on massive corpora, these systems generate responses through probabilistic modelling rather than memorization. As these systems advance, ethical and philosophical challenges intensify. Generative models produce hallucinations, defined as confident but false statements (Ji et al. 248). Botshit describes when users accept such ungrounded output uncritically (Timothy R. Hannigan et al. 474). Questions of authorship, consent, ownership, and provenance remain unresolved, particularly where training data is scraped without permission (see: AI’s Pirated-Books Problem, Reisner). Scholars identify linguistic influence and formulaic tendencies in model output (Juzek and Ward; Turk). Environmental costs, including water consumption associated with large-scale computation, are documented (Verma and Tan). Critics describe fauxtomation, where hidden human labour supports claims of automation (Taylor). Empirical analyses (Feine et al.) indicate that many chatbots are gendered through female names, avatars, and pronouns, indicating a structural gender bias. Further debates address user harm (Adams) and the role of chatbots in public health misinformation (Meyrowitsch et al.). 

In the generative age, the chatbot functions as both an instrument and a medium. Chatbots reshape conceptions of intelligence, communication, and language in human-machine interaction, and reconfigure dialogue as a site of algorithmic narrative production.

See Also

  • Algorithmic Narrativity - The combination of the human ability to understand experience through narrative with the power of the computer to process and generate data that results in the development, modification, and distribution of narratives
  • Artificial Intelligence (AI) - Simulation of human intelligence processes by computer systems, to create or interpret content in innovative and sometimes literary ways
  • Emulation - Replication of the functionality of older hardware or software on new systems, ensuring the continued accessibility and functionality of digital works
  • Histobot - Generative AI chatbot that reenacts historical figures, using large language models to simulate historically contextual dialogue
  • Parasocial Relationships - One-sided relationships where individuals become attached to media personalities as if they are engaged in reciprocal friendship
  • Remix - The recombination of existing media elements to create new works, highlighting issues of authorship, originality, and copyright in the digital age
  • Story Generation - Thread of work in computing to automatically generate narratives, sometimes for research purposes and sometimes for artistic and creative ones
  • Transhumanism - Philosophical movement that advocates for the enhancement of humans through advanced technology, often explored in digital narratives in themes of augmentation, AI, and future societies

Works Referenced

Adamopoulou, Eleni, and Lefteris Moussiades. “Chatbots: History, Technology, and Applications.” Machine Learning with Applications, vol. 2, 2020, p. 100006. DOI.org (Crossref), https://doi.org/10.1016/j.mlwa.2020.100006. 

Adams, Michael. “Character.AI Lawsuit Filed Over Teen Suicide After Alleged Sexual Exploitation by Chatbot.” AboutLawsuits.Com, 4 June 2025, https://www.aboutlawsuits.com/character-ai-lawsuit-teen-suicide-sexual-exploitation-chatbot/. 

Al-Amin, Md, et al. “History of Generative Artificial Intelligence (AI) Chatbots: Past, Present, and Future Development.” arXiv.Org [Ithaca, United States], Feb. 2024. Computer Science. ProQuest, https://www.proquest.com/docview/2924071152?pq-origsite=primo&sourcetype=Working%20Papers. 

Capek, Karel. R.U.R. (Rossum’s Universal Robots) A Fantastic Melodrama in Three Acts and an Epilogue. Translated by Paul Selver, with Nigel Playfair, Samuel French, Inc. and Project Gutenberg, 2019, https://www.gutenberg.org/files/59112/59112-h/59112-h.htm. E-book no. 59112. 

“Chat, V. (1).” Oxford English Dictionary, Oxford University Press, September 2025, https://doi.org/10.1093/OED/1066585256. 

Colby, Kenneth Mark, et al. “Turing-like Indistinguishability Tests for the Validation of a Computer Simulation of Paranoid Processes.” Artificial Intelligence, vol. 3, Jan. 1972, pp. 199–221. DOI.org (Crossref), https://doi.org/10.1016/0004-3702(72)90049-5. 

Depounti, Iliana, et al. “Ideal Technologies, Ideal Women: AI and Gender Imaginaries in Redditors’ Discussions on the Replika Bot Girlfriend.” Media, Culture & Society, vol. 45, no. 4, May 2023, pp. 720–36. DOI.org (Crossref), https://doi.org/10.1177/01634437221119021. 

Feine, Jasper, et al. “Gender Bias in Chatbot Design.” Chatbot Research and Design, edited by Asbjørn Følstad et al., vol. 11970, Springer International Publishing, 2020, pp. 79–93. Lecture Notes in Computer Science. DOI.org (Crossref), https://doi.org/10.1007/978-3-030-39540-7_6. 

Hsu, Hansen. “SmarterChild: A Chatbot Buddy from 2001.” CHM, 29 May 2025, https://computerhistory.org/blog/smarterchild-a-chatbot-buddy-from-2001/. 

Ji, Ziwei, et al. “Survey of Hallucination in Natural Language Generation.” ACM Computing Surveys, vol. 55, no. 12, Mar. 2023, pp. 1–38. dl.acm.org (Atypon), https://doi.org/10.1145/3571730. 

Juzek, Tom S., and Zina B. Ward. “Why Does ChatGPT “Delve” So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models.” arXiv:2412.11385, arXiv, 16 Dec. 2024. arXiv.org, https://doi.org/10.48550/arXiv.2412.11385. 

Mauldin, Michael L. “ChatterBots, TinyMuds, and the Turing Test: Entering the Loebner Prize Competition.” AAAI-94 Proceedings, 1994. 

Meyrowitsch, Dan W., et al. “AI Chatbots and (Mis)Information in Public Health: Impact on Vulnerable Communities.”Frontiers in Public Health, vol. 11, Oct. 2023, p. 1226776. DOI.org (Crossref), https://doi.org/10.3389/fpubh.2023.1226776. 

Moravec, Hans. “The Age of Robots.” Extro 1, Proceedings of the First Etropy Institute Conference on TansHumanist Thought, edited by Max Moore, June 1993, https://web.archive.org/web/20060615055406/http://www.frc.ri.cmu.edu/\~hpm/project.archive/general.articles/1993/Robot93.html. 

Reisner, Alex. “The Unbelievable Scale of AI’s Pirated-Books Problem.” The Atlantic, 20 Mar. 2025, https://www.theatlantic.com/technology/archive/2025/03/libgen-meta-openai/682093/. Technology. 

Taylor, Astra. “The Automation Charade.” Logic(s) Magazine, nos 5-Failure, 1 Aug. 2018, https://logicmag.io/failure/the-automation-charade/. 

Timothy R. Hannigan, et al. “Beware of Botshit: How to Manage the Epistemic Risks of Generative Chatbots.” Business Horizons, vol. 67, no. 5, Mar. 2024, pp. 471–86. www.sciencedirect.com, https://doi.org/10.1016/j.bushor.2024.03.001. 

Turing, A. M. “I.—COMPUTING MACHINERY AND INTELLIGENCE.” Mind, LIX, no. 236, Oct. 1950, pp. 433–60. DOI.org (Crossref), https://doi.org/10.1093/mind/LIX.236.433. 

Turk, Victoria. “The Great Language Flattening.” The Atlantic, 29 Apr. 2025, https://www.theatlantic.com/technology/archive/2025/04/great-language-flattening/682627/.

Vaswani, Ashish, et al. “Attention Is All You Need.” arXiv:1706.03762, arXiv, 2 Aug. 2023. arXiv.org, https://doi.org/10.48550/arXiv.1706.03762. 

Verma, Pranshu, and Shelly Tan. “A Bottle of Water per Email: The Hidden Environmental Costs of Using AI Chatbots.” Washington Post, 18 Sept. 2024.  www.washingtonpost.com, https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/. 

Wallace, Richard S. “The Anatomy of A.L.I.C.E.” Parsing the Turing Test, edited by Robert Epstein et al., Springer Netherlands, 2009, pp. 181–210. DOI.org (Crossref), https://doi.org/10.1007/978-1-4020-6710-5_13. 

Weizenbaum, Joseph. “From Computer Power and Human Reason.” The New Media Reader, edited by Noah Wardrip-Fruin et al., The MIT Press, 2003. 

Zemčík, Tomáš. “A Brief History of Chatbots.” DEStech Transactions on Computer Science and Engineering, 2019. ResearchGate, https://doi.org/10.12783/dtcse/aicae2019/31439.

Further Reading

Bastiansen, Mathilde H. A., et al. “Female Chatbots Are Helpful, Male Chatbots Are Competent?: The Effects of Gender and Gendered Language on Human-Machine Communication”. Publizistik, vol. 67, no. 4, 2022, pp. 601–23. -DOI.org (Crossref), https://doi.org/10.1007/s11616-022-00762-8. 

Ciesla, Robert. The Book of Chatbots: From ELIZA to ChatGPT. Springer Nature Switzerland, 2024. DOI.org (Crossref), https://doi.org/10.1007/978-3-031-51004-5.

Cite This

Harder, Lina Ruth. "Chatbot." The Living Glossary of Digital Narrative, 2026. https://glossary.cdn.uib.no/terms/chatbot

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