Conversational UX Design: A Practitioner's Guide to the Natural Conversation Framework
Abstract
With recent advances in natural language understanding techniques and farfield microphone arrays, natural language interfaces, such as voice assistants and chatbots, are emerging as a popular new way to interact with computers. They have made their way out of the industry research labs and into the pockets, desktops, cars and living rooms of the general public. But although such interfaces recognize bits of natural language, and even voice input, they generally lack conversational competence, or the ability to engage in natural conversation. Today's platforms provide sophisticated tools for analyzing language and retrieving knowledge, but they fail to provide adequate support for modeling interaction. The user experience (UX) designer or software developer must figure out how a human conversation is organized, usually relying on commonsense rather than on formal knowledge. Fortunately, practitioners can rely on conversation science. This book adapts formal knowledge from the field of Conversation Analysis (CA) to the design of natural language interfaces. It outlines the Natural Conversation Framework (NCF), developed at IBM Research, a systematic framework for designing interfaces that work like natural conversation. The NCF consists of four main components: 1) an interaction model of "expandable sequences," 2) a corresponding content format, 3) a pattern language with 100 generic UX patterns and 4) a navigation method of six basic user actions. The authors introduce UX designers to a new way of thinking about user experience design in the context of conversational interfaces, including a new vocabulary, new principles and new interaction patterns. User experience designers and graduate students in the HCI field as well as developers and conversation analysis students should find this book of interest.
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Anthropomorphic agents come with the promise of new kinds of user interfaces, spurring advances in the emerging field of conversational UX.
“Anthropomorphic agents come with the promise of new kinds of user interfaces, spurring advances in the emerging field of conversational UX (73, 74).”
From The benefits and dangers of anthropomorphic conversational agents (2025) · cites this paper as [74]
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Ross, S., Martinez, F., Houde, S., Muller, M. J., & Weisz, J. D. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584037 · Cited by 272 · see citing works
“We then describe our analysis of the conversation logs and our development of a coding guide based on Conversation Analysis [76], and specifically, Moore and Arar’s Natural Conversation Framework [50].”
BackgroundMethodInfluentialFuture directions for chatbot research: An interdisciplinary research agenda
Følstad, A., Araujo, T., Law, E. L.-C., Brandtzaeg, P. B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., McAllister, P., Ischen, C., et al. (2021) · Computing · doi:10.1007/s00607-021-01016-7 · Cited by 247 · see citing works
Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots
McTear, M. (2021) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02176-3 · Cited by 155 · see citing works
The benefits and dangers of anthropomorphic conversational agents
Peter, S., Riemer, K., & West, J. D. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415898122 · Cited by 110 · see citing works
Følstad, A., & Taylor, C. (2021) · Quality and User Experience · doi:10.1007/s41233-021-00046-5 · Cited by 109 · see citing works
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 · see citing works
“According to Moore and Arar (2019), it comprises the following activities: observe and engage with users, define user personas and goals, shape conversations, define an agent persona, presume user’s and agent’s messages, prototype and test.”
BackgroundLee, L., Braud, T., Hosio, S. J., & Hui, P. (2021) · ACM Computing Surveys · doi:10.1145/3467963 · Cited by 64 · see citing works
“Among these applications, three types of conversational interfaces frequently appear in the literature [103], which demonstrate a tendency towards multi-modal inputs [108], from voice-based commands [131, 153], interactive dialogues in chatroom alike interfaces [108, 116], to embodied virtual agents [101, 107, 154].”
BackgroundUser Interaction Patterns and Breakdowns in Conversing with LLM-Powered Voice Assistants
Mahmood, A., Wang, J., Yao, B., Wang, D., & Huang, C.-M. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103406 · Cited by 51 · see citing works
McMillan, D., Brown, B. A. T., Kawaguchi, I., Jaber, R., Belenguer, J. S., & Kuzuoka, H. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359278 · Cited by 44 · see citing works
“This echoes Moore & Arar [56] in their selection of patterns of interaction from Conversation Analysis which can be readily applied to conversational user interfaces, which they see as something which future research in both CA and HCI can add to.”
BackgroundDesigners Characterize Naturalness in Voice User Interfaces: Their Goals, Practices, and Challenges
Kim, Y., Reza, M., McGrenere, J., & Yoon, D. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445579 · Cited by 44 · see citing works
“It seems that the strategies of our designers largely echo conversation repair and error recovery strategies suggested by the existing VUI design textbooks [29, 70].”
BackgroundKvale, K., Freddi, E., Hodnebrog, S., Sell, O. A., & Følstad, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68288-0_14 · Cited by 44 · see citing works
Stokoe, E. H., Albert, S., Buschmeier, H., & Stommel, W. (2024) · Discourse & Communication · doi:10.1177/17504813241267118 · Cited by 37 · see citing works
“LLMs, for example, can often surprise users by achieving a ‘good enough’ show of ‘understanding’ by displaying an ‘adequate interpretation of social action within interaction’ (Moore and Arar, 2019, p. 23).”
Conversational Interfaces for Information Search
Liao, Q. V., Geyer, W., Muller, M. J., & Khazaen, Y. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-38825-6_13 · Cited by 37 · see citing works
“Three basic principles are commonly recognized for governing these patterns [45]: efficiency [21, 50], common ground [35], and recipient design [9, 34, 50].”
BackgroundMethodInfluentialA Mental Health and Well-Being Chatbot: User Event Log Analysis
Booth, F. G., Potts, C., Bond, R. R., Mulvenna, M., Kostenius, C., Dhanapala, I., Vakaloudis, A., Cahill, B., Kuosmanen, L., & Ennis, E. (2022) · JMIR mHealth and uHealth · doi:10.2196/43052 · Cited by 29 · see citing works
“This also demonstrates the value of incorporating efficient scripts into the app, providing quick exercises for users needing support [30].”
Quamar, A., Özcan, F., Miller, D., Moore, R. J., Niehus, R., & Kreulen, J. T. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3415478.3415557 · Cited by 32 · see citing works
“We reused primarily the NCF’s Open Request for enabling series of complex requests, in addition to some of its conversation management modules.”
BackgroundMethodInfluentialSelf-citationHeuristic Evaluation of COVID-19 Chatbots
Höhn, S., & Bongard-Blanchy, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68288-0_9 · Cited by 31 · see citing works
“Moore and Arar (2019) [14] argue that chatbots today are similar to the Internet in 1997: made by laypeople based on a set of quickly self-acquired skills.”
BackgroundMethodInfluentialThe Role of Chatbots in Foreign Language Learning: The Present Situation and the Future Outlook
Petrović, J., & Jovanovic, M. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-72711-6_17 · Cited by 43 · see citing works
Tran, B. D., Latif, K., Reynolds, T. L., Park, J., Lafata, J. E., Tai‐Seale, M., & Zheng, K. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad001 · Cited by 31 · see citing works
Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach
Houde, S., Brimijoin, K., Muller, M. J., Ross, S., Moran, D., Gonzalez, G. E., Kunde, S., Foreman, M. A., & Weisz, J. D. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712089 · Cited by 30 · see citing works
An Ontology-Based Conversation System for Knowledge Bases
Quamar, A., Lei, C., Miller, D., Özcan, F., Kreulen, J. T., Moore, R. J., & Efthymiou, V. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3386139 · Cited by 29 · see citing works
“This is an example of a “repair” [24] on the meaning of part of the agent’s prior utterance, namely, its use of the word “effective” in this context.”
BackgroundInfluentialSelf-citationThe IBM natural conversation framework: a new paradigm for conversational UX design
Moore, R. J., An, S., & Ren, G.-J. (2023) · Human–Computer Interaction · doi:10.1080/07370024.2022.2081571 · Cited by 26 · see citing works
“However, the spatial interaction metaphors of these areas of visual design largely do not apply to the design of conversational user interfaces (Moore & Arar, 2019; Moore et al., 2020; Murad et al., 2021; Yankelovich et al., 1995).”
BackgroundMethodInfluentialSelf-citationRADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems
Peng, B., Li, C., Zhang, Z., Zhu, C., Li, J., & Gao, J. (2021) · doi:10.18653/v1/2021.acl-long.341 · Cited by 33 · see citing works
“, 1978), while template-based crowd-sourcing fails in covering the linguistic variations (Schegloff et al., 1977; Moore and Arar, 2019).”
BackgroundKarkera, Y., Tandukar, B., Chandra, S., & Martin-Hammond, A. M. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581561 · Cited by 25 · see citing works
“The vast majority of VA applications can already provide simple informational and transactional interactions [45], such as returning predetermined information or setting up event reminders.”
BackgroundPaakki, H., Vepsäläinen, H., & Salovaara, A. (2021) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-021-09397-1 · Cited by 20 · see citing works
AI in situated action: a scoping review of ethnomethodological and conversation analytic studies
Mlynář, J., de Rijk, L., Liesenfeld, A., Stommel, W., & Albert, S. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-01919-x · Cited by 18 · see citing works
Diabetes and conversational agents: the AIDA project case study
Alloatti, F., Bosca, A., Di, L., & Pieraccini, F. (2021) · Discover Artificial Intelligence · doi:10.1007/s44163-021-00005-1 · Cited by 17 · see citing works
Measuring and Clustering Heterogeneous Chatbot Designs
Cañizares, P. C., López-Morales, J. M., Pérez-Soler, S., Guerra, E., & de Lara, J. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3637228 · Cited by 19 · see citing works
“For example, IBM’s Natural Conversation Framework [38] relies on conversation patterns [39] and conversation design principles [37, 59], such as recipient design (i.e., provide multiple conversation paths for different user types), minimization (i.e., design concise chatbot answers), and repair…”
BackgroundDesign Systems for Conversational UX
Moore, R. J., Liu, E. Y., Mishra, S., & Ren, G. (2020) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3405755.3406150 · Cited by 18 · see citing works
“We call this "conversation-first design" [3].”
BackgroundMethodInfluentialSelf-citationLet’s Talk About CUIs: Putting Conversational User Interface Design Into Practice
Murad, C., Munteanu, C., Cowan, B. R., Clark, L., Porcheron, M., Candello, H., Schlögl, S., Aylett, M., Sin, J., Moore, R. J., et al. (2021) · CHI Extended Abstracts · doi:10.1145/3411763.3441336 · Cited by 15 · see citing works
“The result is the Natural Conversation Framework [17, 19] and the Alma Design System [18], which provide designers with basic concepts, principles, interaction patterns and software components.”
BackgroundSelf-citationDesigning AI Personalities: Enhancing Human-Agent Interaction Through Thoughtful Persona Design
Zargham, N., Dubiel, M., Desai, S., Mildner, T., & Belz, H.-J. (2024) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3701571.3701608 · Cited by 15 · see citing works
“The interfaces that allow users to interact with an agent using natural language through text or voice are referred to as Conversational User Interfaces (CUIs) [29].”
Park, D., Choo, M., Cho, M., Kim, J., & Shin, Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642593 · Cited by 11 · see citing works
“A conversational agent, which has widely used functions such as ordering goods and weather guidance, is structured to appropriately answer its user’s question [60, 64].”
BackgroundJiang, J., & Verhulsdonck, G. (2025) · Technical Communication Quarterly · doi:10.1080/10572252.2025.2473503 · Cited by 11 · see citing works
“…models (LVLMs) – which allow AI to understand user text commands to analyze and create sophisticated images – will most likely change how designers develop user interfaces since this can now be done through conversational means (cf. Deibel & Evanhoe, 2021; Hall & Maeda, 2018; Moore & Arar, 2019).”
The Gamification of E-learning Environments for Learning Programming
Villegas, C. G., & Aguero, N. A. L. (2023) · JOIV International Journal on Informatics Visualization · doi:10.30630/joiv.7.2.1602 · Cited by 10 · see citing works
Vindigni, G. (2024) · European Journal of Contemporary Education and E-Learning · doi:10.59324/ejceel.2024.2(4).10 · Cited by 15 · see citing works
“Empirical evidence across diverse applications underscores the robustness and reliability of these approaches (Moore & Arar, 2019: 55–71; Lew & Schumacher, 2020: 42–47; Kore, 2022: 61–64; Vindigni, 2023b: 183; Vindigni, 2023c: 39–42).”
BackgroundEthnomethodology's Legacies and Prospects
Maynard, D. W., & Heritage, J. C. (2023) · Annual Review of Sociology · doi:10.1146/annurev-soc-020321-033738 · Cited by 9 · see citing works
Lee, L., Braud, T., Hosio, S. J., & Hui, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09207 · Cited by 9 · see citing works
“Among these applications, three types of conversational interfaces frequently appear in the literature [103], which demonstrate a tendency towards multi-modal inputs [108], from voice-based commands [131, 153], interactive dialogues in chatroom alike interfaces [108, 116], to embodied virtual agents [101, 107, 154].”
BackgroundConversational Agents for Learning Foreign Languages a Survey
Petrović, J., & Jovanovic, M. (2020) · International Scientific Conference Sinteza · doi:10.15308/sinteza-2020-14-22 · Cited by 14 · see citing works
“These services employ a common technological metaphor - a personal, user-friendly assistant that provides a service by using a natural language [3].”
BackgroundOk Google: Using virtual assistants for data collection in psychological and behavioral research
Sprengholz, P., & Betsch, C. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01629-y · Cited by 10 · see citing works
“Consequently, before using standard scales, their wording should be checked, and adaptation to a question – answer format should be considered in order to support a more natural style of conversation between the virtual assistant and the user (Kamm, 1995; Moore & Arar, 2019).”
BackgroundMethodRevisiting Habitability in Conversational Systems
Kocaballi, A. B., Coiera, E. W., & Berkovsky, S. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3383014 · Cited by 8 · see citing works
“, conversational user experience patterns [22]) for the five different domains of habitability; ii) more comprehensive and processoriented methods to evaluate habitability within its different domains [13]; iii) an expectation management module responsible for monitoring and adjusting the expectations of users [19]; a…”
MethodTowards Augmented Reality-driven Human-City Interaction: Current Research and Future Challenges.Preprint
Lee, L., Braud, T., Hosio, S. J., & Hui, P. (2020) · arXiv (Cornell University) · Cited by 8 · see citing works
Shin, J., Ko, H., Bae, S. W., & Kim, J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2441530 · Cited by 7 · see citing works
“The default dialogue flows were developed based on conversational UX patterns from the Natural Conversation Framework demonstrated through IBM’s Watson dialog service (Moore & Arar, 2019).”
Vindigni, G. (2025) · European journal of management, economics and business. · doi:10.59324/ejmeb.2025.2(1).03 · Cited by 8 · see citing works
Effects of Naturalistic Variation in Goal-Oriented Dialog
Ganhotra, J., Moore, R. J., Joshi, S., & Wadhawan, K. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.358 · Cited by 7 · see citing works
“A Appendix: Natural Conversation Framework (NCF) patterns At the core of the NCF is a pattern language of 100 interaction patterns that are adapted from conversation science for modeling rule-based dialog.”
BackgroundMethodInfluentialSelf-citationThe Rise of Interactional Multimodality in Human-Computer Interaction
Klowait, N. O., & Ерофеева, M. (2021) · Monitoring obŝestvennogo mneniâ: èkonomičeskie i socialʹnye peremeny · doi:10.14515/monitoring.2021.1.1793 · Cited by 6 · see citing works
RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog SystemsPreprint
Peng, B., Li, C., Zhang, Z., Zhu, C., Li, J., & Gao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14666 · Cited by 6 · see citing works
“, 1978), while template-based crowd-sourcing fails in covering the linguistic variations (Schegloff et al., 1977; Moore and Arar, 2019).”
BackgroundRecommendations for Mental Health Chatbot Conversations: An Integrative Review
Nieminen, H., Vartiainen, A., Bond, R., Laukkanen, E., Mulvenna, M. D., & Kuosmanen, L. (2025) · Journal of Advanced Nursing · doi:10.1111/jan.16762 · Cited by 7 · see citing works
“In conversational interfaces, the user experience is primarily formed from the design and sequencing of utterances (Moore and Arar 2019) and it needs to be considered carefully throughout the design”
Exploring Anthropomorphism in Conversational Agents for Environmental Sustainability
Giudici, M., Scherini, S., Chaussumier, P., Ginocchio, S., & Garzotto, F. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735700 · Cited by 9 · see citing works
Ethnomethodology’s Legacies and Prospects
Heritage, J. C., & Maynard, D. W. (2022) · doi:10.1093/oso/9780190854409.003.0001 · Cited by 7 · see citing works
Responsible Opinion Formation on Debated Topics in Web Search
Rieger, A., Draws, T., Mattis, N. M., Maxwell, D., Elsweiler, D., Gadiraju, U., McKay, D., Bozzon, A., & Pera, M. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56066-8_32 · Cited by 5 · see citing works
Unraveling the thread: understanding and addressing sequential failures in human-robot interaction
Tisserand, L., Stephenson, B., Baldauf-Quilliatre, H., Lefort, M., & Armetta, F. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1359782 · Cited by 7 · see citing works
“In the Natural Conversational Framework (NCF) (Moore and Arar, 2019), interactions are designed as expandable sequences which can accommodate expansions such as clarifications and repairs.”
BackgroundHeo, J., & Lee, U. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610189 · Cited by 5 · see citing works
“There are widely used CUX design guidelines [7, 22, 46, 74]; however, established standards for CUX design are still lacking in this area [19], and supporting tools for CUX designers are inadequate [47, 48].”
MethodAnastasia, N., Harlili, & Yulianti, L. P. (2021) · International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) · doi:10.1109/icaicta53211.2021.9640258 · Cited by 4 · see citing works
Conversational Design Patterns for a UX Evaluation Instrument Implemented by Voice
Díaz-Oreiro, I., López, G., Quesada, L., & Guerrero, L. A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96293-7_44 · Cited by 4 · see citing works
Khemani, K. H., & Reeves, S. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517623 · Cited by 5 · see citing works
Tang, L., Weng, C., Hou, S., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93502-2_5 · Cited by 3 · see citing works
Nonhuman situational enmeshments—How participants build temporal infrastructures for ChatGPT
Klowait, N. O., & Erofeeva, M. (2025) · Journal of Linguistic Anthropology · doi:10.1111/jola.70037 · Cited by 5 · see citing works
Robot Sound in Interaction : Analyzing and Designing Sound for Human-Robot Coordination
Pelikan, H. R. M. (2023) · Studies in language and culture · doi:10.3384/9789180751179 · Cited by 3 · see citing works
Interaction Design Patterns of Web Chatbots
Traubinger, V., & Gaedke, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-62362-2_22 · Cited by 4 · see citing works
Conversational-linguistic politeness parameters for chatbot design: a card-based approach
Monteiro, M. D. S., Pereira, V. C., & Salgado, L. C. D. C. (2024) · Brazilian Symposium on Human Factors in Computing Systems (IHC) · doi:10.1145/3702038.3702067 · Cited by 3 · see citing works
Как попрощаться с роботом? Исследование достижения разговорного закрытия
Егорова, А. И., & Кловайт, Н. (2021) · Monitoring obŝestvennogo mneniâ: èkonomičeskie i socialʹnye peremeny · doi:10.14515/monitoring.2021.1.1810 · Cited by 3 · see citing works
An effective context‐focused hierarchical mechanism for task‐oriented dialogue response generation
Zhao, M., Jiang, Z., Wang, L., Li, R., Lu, X., Hu, Z., & Chen, D. (2022) · Computational Intelligence · doi:10.1111/coin.12544 · Cited by 3 · see citing works
“The updated bAbI and SMD datasets introduce naturalistic variation dialogues through the Natural Conversation Framework (NCF) 54 to alleviate this problem.”
BackgroundAn Analysis of Federal and Municipal Chatbots in Germany
Traubinger, V., Heil, S., Grigera, J., Garrido, A., Abhyankar, S., & Gaedke, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88045-2_13 · Cited by 2 · see citing works
A model-based solution for automated (Re-)engineering of task-oriented chatbots
Pérez-Soler, S., Guerra, E., & Lara, J. D. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112600 · Cited by 2 · see citing works
Conversation Design: The Evolving Paradigm in Technical and Professional Communication
Shalamova, N., & Verhulsdonck, G. (2025) · Communication Design Quarterly · doi:10.1145/3787586.3787589 · Cited by 2 · see citing works
“It has focused on crafting natural, intuitive, and efficient dialogues between humans and machines, including chatbots, voice assistants, and other conversational interfaces (Deibel & Evanhoe, 2021; Hall, 2018; Moore & Arar, 2019; Silva & Canedo, 2024).”
Haunschild, J., Henkel, M., & Reuter, C. (2025) · i-com · doi:10.1515/icom-2024-0067 · Cited by 2 · see citing works
Wu, R., Sunbury, S., Sadler, P. M., & Sonnert, G. (2025) · Computer Science Education · doi:10.1080/08993408.2025.2470572 · Cited by 2 · see citing works
Reimann, M. M., Kunneman, F., Oertel, C., & Hindriks, K. V. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736629 · Cited by 2 · see citing works
Lee, Y., Sargsyan, L., Choi, S., & Kim, S.-H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2530058 · Cited by 2 · see citing works
“They are particularly useful when users ’ hands are occupied or when asking a question is faster than typing and reading (Moore et al., 2020; 2023; Moore & Arar, 2019).”
InfluentialBassetti, C., Blanzieri, E., Borgo, S., & Marangon, S. (2022) · Interaction Studies · doi:10.1075/is.22021.bas · Cited by 2 · see citing works
“…per se, has been extensively pursued in several disciplines, and prominently by Ethnomethodology and Conversation Analysis (EM/CA - cf., e.g., Garfinkel, 1967; Sacks, 1972), whose contribution could be further leveraged (as recognized in Human-Computer Interaction, e.g., Moore and Arar, 2019).”
BackgroundUser Evaluation of Conversational Agents for Aerospace Domain
Liu, Y., Arnold, A., Dupont, G., Kobus, C., Lancelot, F., Granger, G., Rouillard, Y., Duchevet, A., Imbert, J.-P., & Matton, N. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2239544 · Cited by 2 · see citing works
“Researchers proposed a natural conversation framework for a conversational agent that can engage in natural conversations, which is based on the conversation analysis (CA) from sociology (Moore & Arar, 2019).”
Motta, I., Lopes, J., & Quaresma, M. (2020) · Strategic Design Research Journal · doi:10.4013/sdrj.2020.133.20 · Cited by 2 · see citing works
“The system should allow users to cancel menu selections to return to previous options (Killam & Autry, 2000) or ask for a repetition of information (Moore & Arar, 2019).”
BackgroundInfluentialCommercial chatbot monitoring: Approaches focused on automated conversation analysis
Kuligowska, K., & Stanusch, M. (2024) · Humanities & Social Sciences Reviews · doi:10.18510/hssr.2024.1227 · Cited by 1 · see citing works
“Many approaches are proposed to enhance the effectiveness of conversations lead by chatbots (Moore, Arar, 2019) (Adamopoulou, Moussiades, 2020) (Silva, Canedo, 2024).”
BackgroundHanafi, M. F., Fadnis, K., Danilevsky, M., Rosenthal, S., & Katsis, Y. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719962 · Cited by 1 · see citing works
Expert-Generated Privacy Q&A Dataset for Conversational AI and User Study Insights
Leschanowsky, A., Salamatjoo, F., Kolagar, Z., et al. (2025) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720014 · Cited by 1 · see citing works
“Thereby, NLP approaches can produce overly technical and lengthy responses that conflict with conversation design principles such as minimization and user-friendly language [31].”
Moore, R. J., An, S., Gala, J. P., & Jadav, D. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714401 · Cited by 1 · see citing works
“It contains actions for managing real-time interaction through the words alone [33].”
InfluentialSelf-citationHanafi, M. F., Reiss, F. R., Katsis, Y., Moore, R. J., Falakmasir, M. H., Wang, P. C., Wood, D., & Liu, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713312 · Cited by 1 · see citing works
“Sub-stantial work has also been done on designing dialog mechanisms for conversational AI systems, which includes handling misun-derstandings, providing explanations, listing options, and asking clarifying questions [7, 31, 48–50].”
Self-citationPande, C., Fill, H.-G., & Hinkelmann, K. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-95397-2_27 · Cited by 1 · see citing works
Someone Behind This Technology: Conversation Designers in the Artificial Intelligence Loop
Rodwell, E. A. (2025) · Engineering Studies · doi:10.1080/19378629.2025.2575366 · Cited by 1 · see citing works
A Sequence-Based Dialog Management Framework for Co-Regulated Dialog
Kunneman, F., & Hindriks, K. V. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220195 · Cited by 1 · see citing works
Zhou, C., & Zhao, H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2508306 · Cited by 1 · see citing works
Oktarini, K. R. D. (2020) · Soshum Jurnal Sosial dan Humaniora · doi:10.31940/soshum.v10i3.1972 · Cited by 1 · see citing works
“So the intents of the target turn can be written in Intent-Entity-Context-Response (IECR) paradigm (pseudocode) (Moore & Arar, 2019, p. Appendix B) as: Verbal: # Assessment # Compliment # Expression of Existing Intimacy # Invitation to Subsequent Intimacy Talk # Causing embarrassment # Causing…”
BackgroundMethodInfluentialWhat Would Be the Principles for Successful Trollbot Design
Vepsäläinen, H., Salovaara, A., & Paakki, H. (2021) · Työväentutkimus Vuosikirja · Cited by 1 · see citing works
Cao, R., Pan, M., Liu, P., Ariyawansa, V., Prpa, M., & Kitson, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790947
“Prior work has applied conversation analysis to inform the design and evaluation of interactive systems in chat-based [42, 50, 56] and audio-based systems [26, 31, 83].”
Miller, D., Niehus, R., & Moore, R. J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51057-2_14
Self-citationOn the Complexity in Task-oriented Spoken Dialogue Systems
Ultes, S., & Maier, W. (2020) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3405755.3406138
“Scientific systems have been able to realise language-based interaction with a certain degree of dialogue complexity: the complexity of the language ( CoLang ) [25], of the semantic structures internally processible 1 ( CoSem ), and of the interaction ( CoInt ) [30].”
BackgroundDesigning Conversational Agents to Encourage User Narrative and Self-Reflection in Mental Wellbeing
Park, S. H. (2020) · Seoul National University Open Repository (Seoul National University)
Refactoring the UX of a popular voice application
Caione, A., Fiore, A., Mainetti, L., et al. (2020) · International Conference on Information Systems and Technologies (ICIST) · doi:10.1145/3447568.3448538
“The four components of NCF are: (i) an interaction model, (2) common activity modules, (3) a navigation method and (4) a set of sequence metrics.”
BackgroundLiquid Interactions: A Conceptual Interaction Framework for the Future Now
Chaudhary, S. M., Balaji, A., & Ganesh, S. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-0041-8_33
Asymob: a platform for measuring and clustering chatbots
López-Morales, J. M., Cañizares, P. C., Pérez-Soler, S., et al. (2022) · IEEE/ACM International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-companion55297.2022.9793784
Murad, C., Munteanu, C., Cowan, B. R., Clark, L., Porcheron, M., Fischer, J., Candello, H., & Langevin, R. (2023) · International Conference on Conversational User Interfaces · doi:10.1145/3571884.3597440
“Though some have been developed [20, 21, 28, 36, 37], there is a perceived lack of industry focused tools and heuristics to aid in CUI design.”
BackgroundDesign conversacional de chatbots: cultura, linguagem e participação
Monteiro, M. D. S., Pereira, V. C., & Salgado, L. C. D. C. (2023) · doi:10.5753/sbsc_estendido.2023.25653
“Pesquisas recentes tˆem utilizado teorias de an´alise da conversac¸˜ao e pragm´atica para apoiar o design e implementac¸˜ao de chatbots [Dall’Acqua and Tamburini 2021, Moore and Arar 2019].”
BackgroundBridging the gap between conversation technology and conversation analysis
Moore, R. J. (2024) · Discourse & Communication · doi:10.1177/17504813241267092
“Some attempts have been made to translate knowledge from Conversation Analysis into terms that designers and engineers will understand, like ‘pattern language’ or ‘persona’ (Moore and Arar, 2019), but there is still much work to do.”
Self-citationSupporting UX Evaluation with Open Text Word Clustering
Díaz-Oreiro, I., & López, G. (2024) · IEEE Congreso Internacional en Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC) · doi:10.1109/amitic62658.2024.10747605
Design Guidelines for Human-Generative AI Interaction
Lin, L., Wang, Y., Ping, Y., Gao, J., Wang, Z., & Wang, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93718-7_15
Monteiro, M. D. S., Pereira, V. C., & Salgado, L. C. D. C. (2026) · Journal on Interactive Systems · doi:10.5753/jis.2026.5908
“The Natural Conversation Framework (NCF) is a versatile pattern language for conversational design grounded in CA [Moore and Arar, 2019].”
Traubinger, V., Heil, S., Mithari, A. A., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-26717-7_4
Rewriting the Script: Conversation Design’s Evolution with LLM Co-design
Guyre, M., Gonzalez, L., Davis, M., & Divekar, R. R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-26717-7_13
Verhulsdonck, G., & Jiang, J. (2026) · Journal of Business and Technical Communication · doi:10.1177/10506519261485276
- Authors
- Research-Almaden, I., Moore, R. J., Arar, R., & Research-Almaden, I.
- Venue
- ACM eBooks
- Publisher
- Association for Computing Machinery
- Published
- 2019-05-01
- OpenAlex
- W2942671448
- Cited by
- 118
- References
- 0
- Citations by year
- 2026: 25 · 2025: 24 · 2024: 13 · 2023: 12 · 2022: 9 · 2021: 17 · 2020: 17 · 2019: 1
- FWCI
- 4.92
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- ACM eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- conversation analysis, natural language interfaces, voice assistants, chatbots, conversation skills, interaction modeling, user navigation, human-computer interaction
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