research-article

Public Access

If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills

Authors:

Michelle X. Zhou,

Changyan ChiAuthors Info & Claims

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Pages 1 - 14

https://doi.org/10.1145/3313831.3376131

Published: 23 April 2020 Publication History

All formats PDF

Abstract

Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills-the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.

Supplementary Material

ZIP File (paper004aux.zip)

The ZIP file contains a short demo video of an interview chatbot created by our prototype.

Download
26.11 MB

MP4 File (paper004pv.mp4)

Preview video

Download
3.54 MB

MP4 File (a4-xiao-presentation.mp4)

Download
38.08 MB

References

[1]

Jun Araki, Dheeraj Rajagopal, Sreecharan Sankaranarayanan, Susan Holm, Yukari Yamakawa, and Teruko Mitamura. 2016. Generating questions and multiple-choice answers using semantic analysis of texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 1125--1136.

[2]

Christine Bauer, Kathrin Figl, and Renate MotschnigPitrik. 2010. Introducing'Active Listening'to Instant Messaging and E-mail: Benefits and Limitations. IADIS International Journal on WWW/Internet 7, 2 (2010), 1--17.

[3]

Christine Bauer and Kathrin Figl. 2008. Active listening" in written online communication-a case study in a course on soft skills for computer scientists. In 2008 38th Annual Frontiers in Education Conference. IEEE, F2C--1.

[4]

Timothy Bickmore. 2010. Relational agents for chronic disease self management. Health Informatics: A Patient-Centered Approach to Diabetes (2010), 181--204.

[5]

David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993--1022.

Digital Library

[6]

Antoine Bordes, Y-Lan Boureau, and Jason Weston, 2017. Learning end-to-end goal-oriented dialog. ICLR' 2017.

[7]

Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, and others. 2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018).

[8]

Chatfuel. 2019. Retrieved from https://chatfuel.com/

[9]

Bert Decker., 1989. How to communicate effectively, Page, London, UK.

[10]

David DeVault, Ron Artstein, Grace Benn, Teresa Dey, Ed Fast, Alesia Gainer, Kallirroi Georgila, Jon Gratch, Arno Hartholt, Margaux Lhommet, and others. 2014. SimSensei Kiosk: A virtual human interviewer for healthcare decision support. In Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems. International Foundation for Autonomous Agents and Multiagent Systems, 1061--1068.

Digital Library

[11]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171-- 4186.

[12]

Xinya Du, Junru Shao, and Claire Cardie. 2017. Learning to Ask: Neural Question Generation for Reading Comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1342--1352.

[13]

Dialogflow. 2019. Retrieved from https://dialogflow.com/

[14]

Günes Erkan and Dragomir R Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research 22 (2004), 457--479.

[15]

Jerome Friedman, Trevor Hastie, and Robert Tibshirani, 2001. The Elements of Statistical Learning (Vol. 1, No. 10). New York, NY, USA:: Springer series in statistics.

[16]

Ujwal Gadiraju, Ricardo Kawase, Stefan Dietze, and Gianluca Demartini. 2015. Understanding malicious behavior in crowdsourcing platforms: The case of online surveys. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 1631--1640.

Digital Library

[17]

Ryan J Gallagher, Kyle Reing, David Kale, and Greg Ver Steeg. 2017. Anchored correlation explanation: Topic modeling with minimal domain knowledge. Transactions of the Association for Computational Linguistics 5 (2017), 529--542.

[18]

Patrick Gebhard, Tobias Baur, Ionut Damian, Gregor Mehlmann, Johannes Wagner, and Elisabeth André. 2014. Exploring interaction strategies for virtual characters to induce stress in simulated job interviews. In Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems. International Foundation for Autonomous Agents and Multiagent Systems, 661--668.

Digital Library

[19]

Thomas Gordon. 1977. Leader Effectiveness Training. New York: Wyden books. p. 57.

[20]

Sambhav Gupta, Krithika Jagannath, Nitin Aggarwal, Ramamurti Sridar, Shawn Wilde, and Yu Chen. 2019. Artificially Intelligent (AI) tutors in the classroom: A need assessment study of designing chatbots to sup-port student learning. In Proceedings of the 2019 PACIS Pacific Asia Conference on Information Systems. AIS.

[21]

Matt Grech. 2017. The Current State of Chatbots in 2017. (Apr 2017). https://getvoip.com/blog/2017/04/ 21/the- current- state- of- chatbots- in- 2017/

[22]

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. ACM, 209.

Digital Library

[23]

Tianran Hu, Anbang Xu, Zhe Liu, Quanzeng You, Yufan Guo, Vibha Sinha, Jiebo Luo, and Rama Akkiraju. 2018. Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 415.

Digital Library

[24]

Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, and Eric Xing. 2016. Harnessing deep neural networks with logic rules. arXiv preprint arXiv:1603.06318 (2016).

[25]

IBM Watson Assistant. 2019. Retrieved from https://www.ibm.com/cloud/watson-assistant/

[26]

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), 170.

Digital Library

[27]

Douglas Jones, 1979, Elementary Information Theory. Clarendon Press.

[28]

Juji document for chatbot designers. 2019 Retrieved from https://docs.juji.io/

[29]

Soomin Kim, Joonhwan Lee, and Gahgene Gweon. 2019. Comparing Data from Chatbot and Web Surveys: Effects of Platform and Conversational Style on Survey Response Quality. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 86.

Digital Library

[30]

Minha Lee, Sander Ackermans, Nena van As, Hanwen Chang, Enzo LUniversity of Chinese Academy of Sciences, and Wijnand IJsselsteijn. 2019. Caring for Vincent: A Chatbot for Self-Compassion. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 702.

Digital Library

[31]

Terri Lee, Krithika Jagannath, Nitin Aggarwal, Ramamurti Sridar, Shawn Wilde, Timothy Hill, and Yu Chen. 2019b. Intelligent Career Advisers in Your Pocket? A Need Assessment Study of Chatbots for Student Career Advising. (2019).

[32]

Jingyi Li, Michelle X. Zhou, Huahai Yang, and Gloria Mark. 2017. Confiding in and listening to virtual agents. In Proceedings of the 22nd International Conference on Intelligent User Interfaces-IUI, Vol. 17.

Digital Library

[33]

Shixia Liu, Michelle X Zhou, Shimei Pan, Yangqiu Song, Weihong Qian, Weijia Cai, and Xiaoxiao Lian. 2012. Tiara: Interactive, topic-based visual text summarization and analysis. ACM Transactions on Intelligent Systems and Technology (TIST) 3, 2 (2012), 25.

[34]

Gale M LUniversity of Chinese Academy of Sciences, Jonathan Gratch, Aisha King, and Louis-Philippe Morency. 2014. It's only a computer: Virtual humans increase willingness to disclose. Computers in Human Behavior 37 (2014), 94--100.

Digital Library

[35]

Stephen Louw, R Watson Todd, and P Jimarkon. 2011. Active listening in qualitative research interviews. In Proceedings of the International Conference: Research in Applied Linguistics, April.

[36]

Ryan Thomas Lowe, Nissan Pow, Iulian Vlad Serban, Laurent Charlin, Chia-Wei Liu, and Joelle Pineau. 2017. Training end-to-end dialogue systems with the ubuntu dialogue corpus. Dialogue & Discourse 8, 1 (2017), 31--65.

[37]

Manychat. 2019 Retrieved from https://manychat.com/

[38]

Michael C McCord, J William Murdock, and Branimir K Boguraev. 2012. Deep parsing in Watson. IBM Journal of research and development 56, 3.4 (2012), 3--1.

[39]

Clifford Nass, Jonathan Steuer, and Ellen R Tauber. 1994. Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 72--78.

Digital Library

[40]

Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2015. A review of relational machine learning for knowledge graphs. Proc. IEEE 104, 1 (2015), 11--33.

[41]

Jay Nunamaker, Derrick Douglas, Elkins Aaron, Burgoon Judee, and Patton Mark.2011 Embodied conversational agent-based kiosk for automated interviewing. Journal of Management Information Systems 28.1: 17--48.

Digital Library

[42]

Ioannis Papaioannou, Christian Dondrup, Jekaterina Novikova, and Oliver Lemon. 2017. Hybrid chat and task dialogue for more engaging hri using reinforcement learning. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 593--598.

Digital Library

[43]

Geoffrey Keppel. 1991 Design and analysis: A researcher's handbook. Prentice-Hall, Inc.

[44]

Radim Rehrek and Petr Sojka. 2011. Gensim-statistical semantics in python. statistical semantics; gensim; Python; LDA; SVD (2011).

[45]

Carl R Rogers and Richard E Farson. 1984. Active listening. Organizational Psychology, 4th Ed. Englewood Cliffs, NJ (1984), 255--266.

[46]

Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2016. Building end-to-end dialogue systems using generative hierarchical neural network models. In Thirtieth AAAI Conference on Artificial Intelligence.

Digital Library

[47]

Louis Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, and Ray Kurzweil. 2016. Generating long and diverse responses with neural conversation models.

[48]

Yangqiu Song, Shimei Pan, Shixia Liu, Michelle X Zhou, and Weihong Qian. 2009. Topic and keyword re-ranking for LDA-based topic modeling. In Proceedings of the 18th ACM conference on Information and knowledge management. ACM, 1757--1760.

Digital Library

[49]

S. Shyam Sundar and Jingyoung Kim 2019. Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 538.

Digital Library

[50]

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 3319-- 3328.

Digital Library

[51]

Ella Tallyn, Hector Fried, Rory Gianni, Amy Isard, and Chris Speed. 2018. The Ethnobot: Gathering Ethnographies in the Age of IoT. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 604.

Digital Library

[52]

David Traum. 2017 Computational Approaches to Dialogue. The Routledge Handbook of Language and Dialogue, Chapter 9, Weigand, E. (Ed.), New York.

[53]

Debbe Thompson, Karen W Cullen, Maria J Redondo, and Barbara Anderson. 2016. Use of relational agents to improve family communication in type 1 diabetes: methods. JMIR research protocols 5, 3 (2016), e151.

[54]

James Vincent. 2016. Twitter taught Microsoft's friendly AI chatbot to be a racist asshole in less than a day. Retrieved from https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbotracist

[55]

Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering 29, 12 (2017), 2724--2743.

[56]

Yansen Wang, Chenyi Liu, Minlie Huang, and Liqiang Nie. 2018. Learning to Ask Questions in Opendomain Conversational Systems with Typed Decoders. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2193--2203.

[57]

Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, and Lawrence Carin. 2018a. Joint embedding of words and labels for text classification. arXiv preprint arXiv:1805.04174 (2018).

[58]

Alice Watson, Timothy Bickmore, Abby Cange, Ambar Kulshreshtha, and Joseph Kvedar. 2012. An internet-based virtual coach to promote physical activity adherence in overweight adults: randomized controlled trial. Journal of medical Internet research 14, 1 (2012), e1.

[59]

Harry Weger Jr, Gina R Castle, and Melissa C Emmett. 2010. Active listening in peer interviews: The influence of message paraphrasing on perceptions of listening skill. The Intl. Journal of Listening 24, 1 (2010), 34--49.

[60]

Joseph Weizenbaum and others. 1966. ELIZA-a computer program for the study of natural language communication between man and machine. Commun. ACM 9, 1 (1966), 36--45.

Digital Library

[61]

Alex C Williams, Harmanpreet Kaur, Gloria Mark, Anne Loomis Thompson, Shamsi T Iqbal, and Jaime Teevan. 2018. Supporting workplace detachment and reattachment with conversational intelligence. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 88.

Digital Library

[62]

Jason D Williams, Kavosh Asadi, and Geoffrey Zweig. 2017. Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017

[63]

Ziang Xiao, Michelle X Zhou, and Wai-Tat Fu. 2019a. Who should be my teammates: Using a conversational agent to understand individuals and help teaming. In Proceedings of the 24th International Conference on Intelligent User Interfaces. ACM, 437--447.

Digital Library

[64]

Ziang Xiao, Michelle X Zhou, Q Vera Liao, Gloria Mark, Changyan Chi, Wenxi Chen, and Huahai Yang. 2019b. Tell Me About Yourself: Using an AIPowered Chatbot to Conduct Conversational Surveys. arXiv preprint arXiv:1905.10700 (2019).

[65]

Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma. 2017. Topic aware neural response generation. In Thirty-First AAAI Conference on Artificial Intelligence.

Digital Library

[66]

Zhou Yu, Alan W Black, and Alexander I Rudnicky. 2017. Learning conversational systems that interleave task and non-task content. Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI Press, 2017.

[67]

Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017. Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 654--664.

[68]

Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2018. Emotional chatting machine: Emotional conversation generation with internal and external memory. In Thirty-Second AAAI Conference on Artificial Intelligence.

[69]

Michelle X Zhou, Gloria Mark, Jingyi Li, and Huahai Yang. 2019. Trusting Virtual Agents: The Effect of Personality. ACM Transactions on Interactive Intelligent Systems (TiiS) 9, 2--3 (2019), 10.

Digital Library

Cited By

Paek EFan TFinch JChoi J(2024)Enhancing Task-Oriented Dialogue Systems through Synchronous Multi-Party Interaction and Multi-Group Virtual SimulationInformation10.3390/info1509058015:9(580)Online publication date: 19-Sep-2024
https://doi.org/10.3390/info15090580
Akram SBuono PLanzilotti R(2024)Exploring the Impact of Conversational Style in Enhancing Recruitment Chatbot InteractionsProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685387(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685387
Rashik MJasim MKucher KSarvghad AMahyar N(2024)Beyond Text and Speech in Conversational Agents: Mapping the Design Space of AvatarsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661563(1875-1894)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661563
Show More Cited By

Index Terms

If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
2. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Evaluating and Informing the Design of Chatbots
DIS '18: Proceedings of the 2018 Designing Interactive Systems Conference

Text messaging-based conversational agents (CAs), popularly called chatbots, received significant attention in the last two years. However, chatbots are still in their nascent stage: They have a low penetration rate as 84% of the Internet users have not ...
Designing Effective Interview Chatbots: Automatic Chatbot Profiling and Design Suggestion Generation for Chatbot Debugging
CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Recent studies show the effectiveness of interview chatbots for information elicitation. However, designing an effective interview chatbot is non-trivial. Few tools exist to help designers design, evaluate, and improve an interview chatbot iteratively. ...
User Engagement with Chatbots: A Discursive Psychology Approach
CUI '20: Proceedings of the 2nd Conference on Conversational User Interfaces

Conversational agents have transcended into multiple industries with increased ability for user engagement in intelligent conversation. Conversations with chatbots are different from interpersonal communication in terms of turn-taking, intentions, and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

April 2020

10688 pages

ISBN:9781450367080

DOI:10.1145/3313831

General Chairs:
Regina Bernhaupt
Eindhoven University of Technology, Netherlands
,
Florian 'Floyd' Mueller
Monash University, Australia
,
David Verweij
Newcastle University, UK
,
Josh Andres
RMIT, Australia
,
Program Chairs:
Joanna McGrenere
University of British Columbia, Canada
,
Andy Cockburn
University of Canterbury, New Zealand
,
Ignacio Avellino
University of Maryland Baltimore County, USA
,
Alix Goguey
Grenoble Alpes University, France
,
Pernille Bjørn
University of Copenhagen, Denmark
,
Shengdong (Shen) Zhao
National University of Singapore, Singapore
,
Briane Paul Samson
Future University Hakodate, Japan & De La Salle University, Philippines
,
Rafal Kocielnik
University of Washington, USA

Copyright © 2020 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: 23 April 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Air Force Office of Scientific Research

Conference

CHI '20

Sponsor:

SIGCHI

CHI '20: CHI Conference on Human Factors in Computing Systems

April 25 - 30, 2020

HI, Honolulu, USA

Acceptance Rates

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

Upcoming Conference

CHI '25

Sponsor:
sigchi

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

54
Total Citations
View Citations
7,671
Total Downloads

Downloads (Last 12 months)1,933
Downloads (Last 6 weeks)339

Reflects downloads up to 06 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Paek EFan TFinch JChoi J(2024)Enhancing Task-Oriented Dialogue Systems through Synchronous Multi-Party Interaction and Multi-Group Virtual SimulationInformation10.3390/info1509058015:9(580)Online publication date: 19-Sep-2024
https://doi.org/10.3390/info15090580
Akram SBuono PLanzilotti R(2024)Exploring the Impact of Conversational Style in Enhancing Recruitment Chatbot InteractionsProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685387(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685387
Rashik MJasim MKucher KSarvghad AMahyar N(2024)Beyond Text and Speech in Conversational Agents: Mapping the Design Space of AvatarsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661563(1875-1894)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661563
Wei JKim SJung HKim Y(2024)Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported DataProceedings of the ACM on Human-Computer Interaction10.1145/36373648:CSCW1(1-35)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637364
Hu JGuo JTang NMa XYao YYang CXu Y(2024)Designing the Conversational Agent: Asking Follow-up Questions for Information ElicitationProceedings of the ACM on Human-Computer Interaction10.1145/36373208:CSCW1(1-30)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637320
Ma ZMei YLong YSu ZGajos K(2024)Evaluating the Experience of LGBTQ+ People Using Large Language Model Based Chatbots for Mental Health SupportProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642482(1-15)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642482
Hoque MMahfuz AKindi MHassan N(2024)Towards Designing a Question-Answering Chatbot for Online News: Understanding Questions and PerspectivesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642007(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642007
Namratha MLokesh RBhat PSrikanth NGagan M(2024)InterviewPal - Elevating Interview Automation with Deep Learning and Natural Language Processing Perspectives2024 International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS)10.1109/ICETCS61022.2024.10543368(1-6)Online publication date: 22-Apr-2024
https://doi.org/10.1109/ICETCS61022.2024.10543368
Sakai KHsieh TYoshikawa YIshiguro H(2024)Multiparty conversation via multirobot system: incorporation of nonverbal user responses for continued conversationAdvanced Robotics10.1080/01691864.2024.232696938:7(482-491)Online publication date: 9-Mar-2024
https://doi.org/10.1080/01691864.2024.2326969
Jin YChen LZhao XCai W(2024)The way you assess matters: User interaction design of survey chatbots for mental healthInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103290189(103290)Online publication date: Sep-2024
https://doi.org/10.1016/j.ijhcs.2024.103290
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.

Get Access

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

Media

Figures

Other

Tables

View Table of Contents