research-article

Open access

How to Guide Task-oriented Chatbot Users, and When: A Mixed-methods Study of Combinations of Chatbot Guidance Types and Timings

Authors:

You-Hsuan Chiang,

Yung-Ju ChangAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 488, Pages 1 - 16

https://doi.org/10.1145/3491102.3501941

Published: 29 April 2022 Publication History

All formats PDF

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

How to Guide Task-oriented Chatbot Users, and When: A Mixed-methods Study of Combinations of Chatbot Guidance Types and Timings

Pages 1 - 16

Abstract
Supplemental Material
References

Abstract

The popularity of task-oriented chatbots is constantly growing, but smooth conversational progress with them remains profoundly challenging. In recent years, researchers have argued that chatbot systems should include guidance for users on how to converse with them. Nevertheless, empirical evidence about what to place in such guidance, and when to deliver it, has been lacking. Using a mixed-methods approach that integrates results from a between-subjects experiment and a reflection session, this paper compares the effectiveness of eight combinations of two guidance types (example-based and rule-based) at four guidance timings (service-onboarding, task-intro, after-failure, and upon-request), as measured by users’ task performance, improvement on subsequent tasks, and subjective experience. It establishes that each guidance type and timing has particular strengths and weaknesses, thus that each type/timing combination has a unique impact on performance metrics, learning outcomes, and user experience. On that basis, it presents guidance-design recommendations for future task-oriented chatbots.

Supplemental Material

MP4 File

Talk Video

Transcript for: Talk Video

ZIP File

Supplemental Materials

Download
539.93 KB

References

[1]

Bill Albert and Tom Tullis. 2013. Measuring the user experience: collecting, analyzing, and presenting usability metrics. Newnes, Burlington, MA.

Digital Library

[2]

James F Allen, Donna K Byron, Myroslava Dzikovska, George Ferguson, Lucian Galescu, and Amanda Stent. 2001. Toward conversational human-computer interaction. AI magazine 22, 4 (2001), 27–27.

Digital Library

[3]

Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, 2019. Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems. Association for Computing Machinery, New York, NY, 1–13.

Digital Library

[4]

Zahra Ashktorab, Mohit Jain, Q Vera Liao, and Justin D Weisz. 2019. Resilient chatbots: Repair strategy preferences for conversational breakdowns. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, 1–12.

Digital Library

[5]

Robert K Atkinson, Alexander Renkl, and Mary Margaret Merrill. 2003. Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps.Journal of educational psychology 95, 4 (2003), 774.

[6]

Marion Boiteux. 2018. Messenger at F8 2018. Meta. Retrieved January 16, 2021 from https://blog.messengerdevelopers.com/messenger-at-f8-2018-44010dc9d2ea

[7]

Petter Bae Brandtzaeg and Asbjørn Følstad. 2017. Why people use chatbots. In International conference on internet science. Springer, pringer, Cham, New York, NY, 377–392.

Digital Library

[8]

Petter Bae Brandtzaeg and Asbjørn Følstad. 2018. Chatbots: changing user needs and motivations. Interactions 25, 5 (2018), 38–43.

Digital Library

[9]

Raluca Budiu. 2018. The user experience of chatbots. Nielsen Norman Group. Retrieved August 16, 2021 from https://www.nngroup.com/articles/chatbots/

[10]

Carrie J Cai, Jonas Jongejan, and Jess Holbrook. 2019. The effects of example-based explanations in a machine learning interface. In Proceedings of the 24th International Conference on Intelligent User Interfaces. Association for Computing Machinery, New York, NY, 258–262.

Digital Library

[11]

Donald J Campbell. 1988. Task complexity: A review and analysis. Academy of management review 13, 1 (1988), 40–52.

[12]

John M Carroll and Caroline Carrithers. 1984. Training wheels in a user interface. Commun. ACM 27, 8 (1984), 800–806.

Digital Library

[13]

Michael E Caspersen and Jens Bennedsen. 2007. Instructional design of a programming course: a learning theoretic approach. In Proceedings of the third international workshop on Computing education research. Association for Computing Machinery, New York, NY, 111–122.

Digital Library

[14]

Richard Catrambone and John M Carroll. 1986. Learning a word processing system with training wheels and guided exploration. ACM SIGCHI Bulletin 18, 4 (1986), 169–174.

Digital Library

[15]

Ana Paula Chaves and Marco Aurelio Gerosa. 2021. How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction 37, 8(2021), 729–758.

[16]

Michelene TH Chi, Miriam Bassok, Matthew W Lewis, Peter Reimann, and Robert Glaser. 1989. Self-explanations: How students study and use examples in learning to solve problems. Cognitive science 13, 2 (1989), 145–182.

[17]

Rikke Friis Dam and Teo Yu Siang. 2020. Affinity Diagrams – Learn How to Cluster and Bundle Ideas and Facts. Interaction Design Foundation. Retrieved September 2, 2021 from https://www.interaction-design.org/literature/article/affinity-diagrams-learn-how-to-cluster-and-bundle-ideas-and-facts

[18]

Alan Dix. 2020. Statistics for HCI: Making Sense of Quantitative Data. Synthesis Lectures on Human-Centered Informatics 13, 2(2020), 1–181.

[19]

Jens Edlund, Joakim Gustafson, Mattias Heldner, and Anna Hjalmarsson. 2008. Towards human-like spoken dialogue systems. Speech communication 50, 8-9 (2008), 630–645.

[20]

Nick Ellis. 1993. Rules and instances in foreign language learning: Interactions of explicit and implicit knowledge. European Journal of Cognitive Psychology 5, 3 (1993), 289–318.

[21]

Dario Fiore, Matthias Baldauf, and Christian Thiel. 2019. ” Forgot your password again?” acceptance and user experience of a chatbot for in-company IT support. In Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia. Association for Computing Machinery, New York, NY, 1–11.

Digital Library

[22]

Asbjørn Følstad and Ragnhild Halvorsrud. 2020. Communicating Service Offers in a Conversational User Interface: An Exploratory Study of User Preferences in Chatbot Interaction. In 32nd Australian Conference on Human-Computer Interaction. Association for Computing Machinery, New York, NY, 671–676.

Digital Library

[23]

Asbjørn Følstad and Marita Skjuve. 2019. Chatbots for customer service: user experience and motivation. In Proceedings of the 1st international conference on conversational user interfaces. Association for Computing Machinery, New York, NY, 1–9.

Digital Library

[24]

Asbjørn Følstad and Cameron Taylor. 2019. Conversational repair in chatbots for customer service: the effect of expressing uncertainty and suggesting alternatives. In International Workshop on Chatbot Research and Design. Springer, Springer, Cham, New York, NY, 201–214.

[25]

Milton Friedman. 1940. A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 11, 1 (1940), 86–92.

[26]

Google. 2021. Conversation design. Google. Retrieved August 16, 2021 from https://developers.google.com/assistant/conversation-design/welcome

[27]

Frederick J Gravetter and Lori-Ann B Forzano. 2018. Research methods for the behavioral sciences. Cengage Learning, Boston,MA.

[28]

Jonathan Grudin and Richard Jacques. 2019. Chatbots, humbots, and the quest for artificial general intelligence. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, 1–11.

Digital Library

[29]

Lakisha Hall. 2018. 6 steps to successful conversational design. IBM. Retrieved August 16, 2021 from https://www.ibm.com/blogs/watson/2018/09/6-steps-to-successful-conversational-design/

[30]

Kai Halttunen. 2003. Scaffolding performance in IR instruction: Exploring learning experiences and performance in two learning environments. Journal of Information Science 29, 5 (2003), 375–390.

[31]

Kai Halttunen. 2011. Pedagogical design and evaluation of interactive information retrieval learning environment. In Teaching and learning in information retrieval. Springer, New York, NY, 61–73.

[32]

Andrew F Hayes and Klaus Krippendorff. 2007. Answering the call for a standard reliability measure for coding data. Communication methods and measures 1, 1 (2007), 77–89.

[33]

Robert R Hoffman, Shane T Mueller, Gary Klein, and Jordan Litman. 2018. Metrics for explainable AI: Challenges and prospects.

[34]

Reid Holmes and Gail C Murphy. 2005. Using structural context to recommend source code examples. In Proceedings of the 27th international conference on Software engineering. Association for Computing Machinery, New York, NY, 117–125.

[35]

Kristina Höök. 2000. Steps to take before intelligent user interfaces become real. Interacting with computers 12, 4 (2000), 409–426.

[36]

IBM. 2021. IBM Cloud Docs/Watson Assistant. IBM. Retrieved November 16, 2021 from https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-slots

[37]

Mohit Jain, Pratyush Kumar, Ishita Bhansali, Q Vera Liao, Khai Truong, and Shwetak Patel. 2018. FarmChat: a conversational agent to answer farmer queries. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 4 (2018), 1–22.

Digital Library

[38]

Mohit Jain, Pratyush Kumar, Ramachandra Kota, and Shwetak N Patel. 2018. Evaluating and informing the design of chatbots. In Proceedings of the 2018 Designing Interactive Systems Conference. Association for Computing Machinery, New York, NY, 895–906.

Digital Library

[39]

Jiepu Jiang, Wei Jeng, and Daqing He. 2013. How do users respond to voice input errors? Lexical and phonetic query reformulation in voice search. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. Association for Computing Machinery, New York, NY, 143–152.

[40]

Philipp Kirschthaler, Martin Porcheron, and Joel E Fischer. 2020. What Can I Say? Effects of Discoverability in VUIs on Task Performance and User Experience. In Proceedings of the 2nd Conference on Conversational User Interfaces. Association for Computing Machinery, New York, NY, 1–9.

Digital Library

[41]

Terry K Koo and Mae Y Li. 2016. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine 15, 2 (2016), 155–163.

[42]

Klaus Krippendorff. 2004. Content analysis: An introduction to its methodology. Sage publications, Washington, D.C.

[43]

Klaus Krippendorff. 2011. Computing Krippendorff’s alpha-reliability.

[44]

Knut Kvale, Olav Alexander Sell, Stig Hodnebrog, and Asbjørn Følstad. 2019. Improving Conversations: Lessons Learnt from Manual Analysis of Chatbot Dialogues. In International Workshop on Chatbot Research and Design. Springer, Springer, Cham, New York, NY, 187–200.

[45]

Suna Kyun, Slava Kalyuga, and John Sweller. 2013. The effect of worked examples when learning to write essays in English literature. The Journal of Experimental Education 81, 3 (2013), 385–408.

[46]

Raina Langevin, Ross J Lordon, Thi Avrahami, Benjamin R Cowan, Tad Hirsch, and Gary Hsieh. 2021. Heuristic Evaluation of Conversational Agents. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, 1–15.

Digital Library

[47]

SeoYoung Lee and Junho Choi. 2017. Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies 103 (2017), 95–105.

Digital Library

[48]

Chi-Hsun Li, Su-Fang Yeh, Tang-Jie Chang, Meng-Hsuan Tsai, Ken Chen, and Yung-Ju Chang. 2020. A Conversation Analysis of Non-Progress and Coping Strategies with a Banking Task-Oriented Chatbot. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, 1–12.

Digital Library

[49]

Jing Li, Aixin Sun, and Zhenchang Xing. 2018. Learning to answer programming questions with software documentation through social context embedding. Information Sciences 448(2018), 36–52.

[50]

Xiujun Li, Yun-Nung Chen, Lihong Li, Jianfeng Gao, and Asli Celikyilmaz. 2017. End-to-end task-completion neural dialogue systems.

[51]

Matthew Lombard, Jennifer Snyder-Duch, and Cheryl Campanella Bracken. 2010. Practical resources for assessing and reporting intercoder reliability in content analysis research projects.

[52]

Ewa Luger and Abigail Sellen. 2016. ” Like Having a Really Bad PA” The Gulf between User Expectation and Experience of Conversational Agents. In Proceedings of the 2016 CHI conference on human factors in computing systems. Association for Computing Machinery, New York, NY, 5286–5297.

Digital Library

[53]

Michael Meng, Stephanie Steinhardt, and Andreas Schubert. 2018. Application programming interface documentation: what do software developers want?Journal of Technical Writing and Communication 48, 3(2018), 295–330.

[54]

Microsoft. 2021. Best practices for building a language understanding (LUIS) app. Microsoft. Retrieved August 16, 2021 from https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-best-practices

[55]

Mohammed Slim Ben Mimoun, Ingrid Poncin, and Marion Garnier. 2012. Case study—Embodied virtual agents: An analysis on reasons for failure. Journal of Retailing and Consumer services 19, 6 (2012), 605–612.

[56]

Christine Murad, Cosmin Munteanu, Leigh Clark, and Benjamin R Cowan. 2018. Design guidelines for hands-free speech interaction. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. Association for Computing Machinery, New York, NY, 269–276.

Digital Library

[57]

Brad A Myers. 1986. Visual programming, programming by example, and program visualization: a taxonomy. ACM sigchi bulletin 17, 4 (1986), 59–66.

Digital Library

[58]

Chelsea Myers, Anushay Furqan, Jessica Nebolsky, Karina Caro, and Jichen Zhu. 2018. Patterns for how users overcome obstacles in voice user interfaces. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, 1–7.

Digital Library

[59]

Jakob Nielsen. 1994. How to Conduct a Heuristic Evaluation.Nielsen Norman Group. Retrieved June 10, 2021 from https://www.nngroup.com/articles/how-to-conduct-a-heuristic-evaluation/

[60]

Jakob Nielsen. 1994. Usability engineering. Morgan Kaufmann, San Francisco, CA.

Digital Library

[61]

Jakob Nielsen. 2010. Mental Models. Nielsen Norman Group. Retrieved August 16, 2021 from https://www.nngroup.com/articles/mental-models/

[62]

Jakob Nielsen. 2011. Workflow Expectations: Presenting Steps at the Right Time. Nielsen Norman Group. Retrieved September 2, 2021 from https://www.nngroup.com/articles/workflow-expectations/

[63]

Alexander Renkl. 1997. Learning from worked-out examples: A study on individual differences. Cognitive science 21, 1 (1997), 1–29.

[64]

Alexander Renkl. 2002. Worked-out examples: Instructional explanations support learning by self-explanations. Learning and instruction 12, 5 (2002), 529–556.

[65]

Alexander Renkl. 2014. Toward an instructionally oriented theory of example-based learning. Cognitive science 38, 1 (2014), 1–37.

[66]

Alexander Renkl, Robert K Atkinson, and Cornelia S Große. 2004. How fading worked solution steps works–a cognitive load perspective. Instructional science 32, 1 (2004), 59–82.

[67]

Alexander Renkl, Tatjana Hilbert, and Silke Schworm. 2009. Example-based learning in heuristic domains: A cognitive load theory account. Educational Psychology Review 21, 1 (2009), 67–78.

[68]

Alexander Renkl, Robin Stark, Hans Gruber, and Heinz Mandl. 1998. Learning from worked-out examples: The effects of example variability and elicited self-explanations. Contemporary educational psychology 23, 1 (1998), 90–108.

[69]

Julian Roelle, Sara Hiller, Kirsten Berthold, and Stefan Rumann. 2017. Example-based learning: The benefits of prompting organization before providing examples. Learning and Instruction 49 (2017), 1–12.

[70]

Ruhi Sarikaya. 2017. The technology behind personal digital assistants: An overview of the system architecture and key components. IEEE Signal Processing Magazine 34, 1 (2017), 67–81.

[71]

Agnieszka Sienkiewicz. 2021. 11 Chatbot Statistics and Trends You Need to Know in 2021. Tidio. Retrieved January 16, 2021 from https://www.tidio.com/blog/chatbot-statistics/

[72]

Arjun Srinivasan, Mira Dontcheva, Eytan Adar, and Seth Walker. 2019. Discovering natural language commands in multimodal interfaces. In Proceedings of the 24th International Conference on Intelligent User Interfaces. Association for Computing Machinery, New York, NY, 661–672.

Digital Library

[73]

Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret Burnett, Thomas Dietterich, Erin Sullivan, and Jonathan Herlocker. 2009. Interacting meaningfully with machine learning systems: Three experiments. International journal of human-computer studies 67, 8 (2009), 639–662.

Digital Library

[74]

Jasper van der Waa, Elisabeth Nieuwburg, Anita Cremers, and Mark Neerincx. 2021. Evaluating XAI: A comparison of rule-based and example-based explanations. Artificial Intelligence 291 (2021), 103404.

[75]

Frank Wilcoxon. 1992. Individual comparisons by ranking methods. In Breakthroughs in statistics. Springer, New York, NY, 196–202.

[76]

Koos Winnips and Catherine McLoughlin. 2001. Six WWW based learner supports you can build. Association for the Advancement of Computing in Education (AACE), Waynesville, NC.

[77]

Svetlana Yarosh, Stryker Thompson, Kathleen Watson, Alice Chase, Ashwin Senthilkumar, Ye Yuan, and AJ Bernheim Brush. 2018. Children asking questions: speech interface reformulations and personification preferences. In Proceedings of the 17th ACM Conference on Interaction Design and Children. Association for Computing Machinery, New York, NY, 300–312.

Digital Library

Cited By

Haunschild JHenkel MReuter C(2025)Breaking down barriers to warning technology adoption: usability and usefulness of a messenger app warning boti-com10.1515/icom-2024-0067Online publication date: 26-Feb-2025
https://doi.org/10.1515/icom-2024-0067
Yan YHou YXiao YZhang RWang Q(2025)KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph IntegrationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345636431:1(547-557)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TVCG.2024.3456364
de Jong SWester JSchrills TS. Secher KF. Griggio Cvan Berkel N(2024)Assessing Cognitive and Social Awareness among Group Members in AI-assisted CollaborationProceedings of the International Conference on Mobile and Ubiquitous Multimedia10.1145/3701571.3701582(338-350)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1145/3701571.3701582
Show More Cited By

Index Terms

How to Guide Task-oriented Chatbot Users, and When: A Mixed-methods Study of Combinations of Chatbot Guidance Types and Timings
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

A Conversation Analysis of Non-Progress and Coping Strategies with a Banking Task-Oriented Chatbot
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Task-oriented chatbots are becoming popular alternatives for fulfilling users' needs, but few studies have investigated how users cope with conversational 'non-progress' (NP) in their daily lives. Accordingly, we analyzed a three-month conversation log ...
Chatbot with Touch and Graphics: An Interaction of Users for Emotional Expression and Turn-taking
CUI '20: Proceedings of the 2nd Conference on Conversational User Interfaces

Use of chatbots for emotional exchange is recently increasing in various domains. However, as existing chatbots have been considered in terms of natural language processing techniques for interaction with text-based chatting, chatbot interaction with ...
When There is No Progress with a Task-Oriented Chatbot: A Conversation Analysis
MobileHCI '19: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services

Task-oriented chatbots are increasingly prevalent in our daily life. Research effort has been devoted to advancing our understanding of users' interaction with conversational agents, including conversation breakdowns. However, most research attempts ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA
,
Steven Drucker
Microsoft Research, USA
,
Julie Williamson
University of Glasgow, UK
,
Koji Yatani
University of Tokyo, Japan

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 April 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Honorable Mention

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Ministry of Science and Technology, Taiwan

Conference

CHI '22

Sponsor:

SIGCHI

CHI '22: CHI Conference on Human Factors in Computing Systems

April 29 - May 5, 2022

LA, New Orleans, USA

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
3,486
Total Downloads

Downloads (Last 12 months)1,057
Downloads (Last 6 weeks)107

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Haunschild JHenkel MReuter C(2025)Breaking down barriers to warning technology adoption: usability and usefulness of a messenger app warning boti-com10.1515/icom-2024-0067Online publication date: 26-Feb-2025
https://doi.org/10.1515/icom-2024-0067
Yan YHou YXiao YZhang RWang Q(2025)KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph IntegrationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345636431:1(547-557)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TVCG.2024.3456364
de Jong SWester JSchrills TS. Secher KF. Griggio Cvan Berkel N(2024)Assessing Cognitive and Social Awareness among Group Members in AI-assisted CollaborationProceedings of the International Conference on Mobile and Ubiquitous Multimedia10.1145/3701571.3701582(338-350)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1145/3701571.3701582
Chen CLiao MSundar S(2024)When to Explain? Exploring the Effects of Explanation Timing on User Perceptions and Trust in AI systemsProceedings of the Second International Symposium on Trustworthy Autonomous Systems10.1145/3686038.3686066(1-17)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3686038.3686066
Chen CLee SJang ESundar S(2024)Is Your Prompt Detailed Enough? Exploring the Effects of Prompt Coaching on Users' Perceptions, Engagement, and Trust in Text-to-Image Generative AI ToolsProceedings of the Second International Symposium on Trustworthy Autonomous Systems10.1145/3686038.3686060(1-12)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3686038.3686060
Zargham NAvanesi VMildner TJavanmardi KPorzel RMalaka R(2024)HASI: A Model for Human-Agent Speech InteractionProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665885(1-8)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665885
Qian CWexler J(2024)Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI CollaborationProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645198(370-384)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645198
Neshaei SRietsche RSu XWambsganss T(2024)Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design DynamicsProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645169(88-102)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645169
Alipour SElahimanesh SJahanzad SMohammadi IMorassafar PNeshaei STefagh M(2024)Improving Grading Fairness and Transparency with Decentralized Collaborative Peer AssessmentProceedings of the ACM on Human-Computer Interaction10.1145/36373508:CSCW1(1-24)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637350
Mildner TCooney OMeck ABartl MSavino GDoyle PGaraialde DClark LSloan JWenig NMalaka RNiess J(2024)Listening to the Voices: Describing Ethical Caveats of Conversational User Interfaces According to Experts and Frequent UsersProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642542(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642542
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten