Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3544548.3581369acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

DAPIE: Interactive Step-by-Step Explanatory Dialogues to Answer Children’s Why and How Questions

Published: 19 April 2023 Publication History

Abstract

Children acquire an understanding of the world by asking “why” and “how” questions. Conversational agents (CAs) like smart speakers or voice assistants can be promising respondents to children’s questions as they are more readily available than parents or teachers. However, CAs’ answers to “why” and “how” questions are not designed for children, as they can be difficult to understand and provide little interactivity to engage the child. In this work, we propose design guidelines for creating interactive dialogues that promote children’s engagement and help them understand explanations. Applying these guidelines, we propose DAPIE, a system that answers children’s questions through interactive dialogue by employing an AI-based pipeline that automatically transforms existing long-form answers from online sources into such dialogues. A user study (N=16) showed that, with DAPIE, children performed better in an immediate understanding assessment while also reporting higher enjoyment than when explanations were presented sentence-by-sentence.

Supplementary Material

Supplemental Materials (3544548.3581369-supplemental-materials.zip)
MP4 File (3544548.3581369-talk-video.mp4)
Pre-recorded Video Presentation
MP4 File (3544548.3581369-video-preview.mp4)
Video Preview
MP4 File (3544548.3581369-video-figure.mp4)
Video Figure

References

[1]
Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, and Srinivas Chappidi. 2021. Open-Domain Question Answering Goes Conversational via Question Rewriting. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 520–534.
[2]
David Paul Ausubel. 2012. The acquisition and retention of knowledge: A cognitive view. Springer Science & Business Media.
[3]
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258(2021).
[4]
Elizabeth Baraff Bonawitz and Tania Lombrozo. 2012. Occam’s rattle: children’s use of simplicity and probability to constrain inference.Developmental psychology 48, 4 (2012), 1156.
[5]
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. https://doi.org/10.48550/ARXIV.2005.14165
[6]
Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Daniel Duckworth, Semih Yavuz, Ben Goodrich, Amit Dubey, Andy Cedilnik, and Kyu-Young Kim. 2019. Taskmaster-1: Toward a realistic and diverse dialog dataset. arXiv preprint arXiv:1909.05358(2019).
[7]
Sonia Q. Cabell, Laura M. Justice, Anita S. McGinty, Jamie DeCoster, and Lindsay D. Forston. 2015. Teacher–child conversations in preschool classrooms: Contributions to children’s vocabulary development. Early Childhood Research Quarterly 30 (2015), 80–92. https://doi.org/10.1016/j.ecresq.2014.09.004
[8]
William Cai, Hao Sheng, and Sharad Goel. 2020. MathBot: A Personalized Conversational Agent for Learning Math.
[9]
Yi Cheng, Kate Yen, Yeqi Chen, Sijin Chen, and Alexis Hiniker. 2018. Why Doesn’t It Work? Voice-Driven Interfaces and Young Children’s Communication Repair Strategies. In Proceedings of the 17th ACM Conference on Interaction Design and Children (Trondheim, Norway) (IDC ’18). Association for Computing Machinery, New York, NY, USA, 337–348. https://doi.org/10.1145/3202185.3202749
[10]
Michelene T.H. 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. https://doi.org/10.1016/0364-0213(89)90002-5
[11]
Michelene T. H. Chi and Ruth Wylie. 2014. The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes. Educational Psychologist 49, 4 (2014), 219–243. https://doi.org/10.1080/00461520.2014.965823 arXiv:https://doi.org/10.1080/00461520.2014.965823
[12]
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018. QuAC: Question Answering in Context. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 2174–2184. https://doi.org/10.18653/v1/D18-1241
[13]
Michelle M Chouinard, Paul L Harris, and Michael P Maratsos. 2007. Children’s questions: A mechanism for cognitive development. Monographs of the society for research in child development (2007), i–129.
[14]
Kathleen H. Corriveau and Katelyn E. Kurkul. 2014. "Why Does Rain Fall?": Children Prefer to Learn From an Informant Who Uses Noncircular Explanations. Child Development 85, 5 (2014), 1827–1835. http://www.jstor.org/stable/24033022
[15]
Catherine Crain-Thoreson, Michael P Dahlin, and Terris A Powell. 2001. Parent-child interaction in three conversational contexts: Variations in style and strategy. New directions for child and adolescent development 2001, 92(2001), 23–38. https://doi.org/10.1002/cd.13
[16]
Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Mike Green, Qazi Rashid, and Kelvin Guu. 2022. Dialog Inpainting: Turning Documents to Dialogs. In International Conference on Machine Learning (ICML). PMLR.
[17]
Judith H. Danovitch, Candice M. Mills, Kaitlin R. Sands, and Allison J. Williams. 2021. Mind the gap: How incomplete explanations influence children’s interest and learning behaviors. Cognitive Psychology 130(2021), 101421. https://doi.org/10.1016/j.cogpsych.2021.101421
[18]
Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A. Smith, and Matt Gardner. 2021. A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers. In NAACL.
[19]
Jan De Belder and Marie-Francine Moens. 2010. Text simplification for children. (01 2010).
[20]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).
[21]
Griffin Dietz, Zachary Pease, Brenna McNally, and Elizabeth Foss. 2020. Giggle Gauge: A Self-Report Instrument for Evaluating Children’s Engagement with Technology. In Proceedings of the Interaction Design and Children Conference (London, United Kingdom) (IDC ’20). Association for Computing Machinery, New York, NY, USA, 614–623. https://doi.org/10.1145/3392063.3394393
[22]
Stefania Druga, Randi Williams, Cynthia Breazeal, and Mitchel Resnick. 2017. "Hey Google is It OK If I Eat You?": Initial Explorations in Child-Agent Interaction. In Proceedings of the 2017 Conference on Interaction Design and Children (Stanford, California, USA) (IDC ’17). Association for Computing Machinery, New York, NY, USA, 595–600. https://doi.org/10.1145/3078072.3084330
[23]
Gerald G. Duffy, Laura R. Roehler, Michael S. Meloth, and Linda G. Vavrus. 1986. Conceptualizing instructional explanation. Teaching and Teacher Education 2, 3 (1986), 197–214. https://doi.org/10.1016/S0742-051X(86)80002-6
[24]
Mary Evans, Shelley Moretti, Deborah Shaw, and Maureen Fox. 2003. Parent Scaffolding in Children’s Oral Reading. Early Education and Development - EARLY EDUC DEV 14 (07 2003), 363–388. https://doi.org/10.1207/s15566935eed1403_5
[25]
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019. ELI5: Long Form Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 3558–3567. https://doi.org/10.18653/v1/P19-1346
[26]
Brandy N. Frazier, Susan A. Gelman, and Henry M. Wellman. 2009. Preschoolers’ Search for Explanatory Information within Adult: Child Conversation. Child Development 80, 6 (2009), 1592–1611. http://www.jstor.org/stable/25592097
[27]
Brandy N Frazier, Susan A Gelman, and Henry M Wellman. 2016. Young children prefer and remember satisfying explanations. Journal of Cognition and Development 17, 5 (2016), 718–736.
[28]
Radhika Garg, Hua Cui, Spencer Seligson, Bo Zhang, Martin Porcheron, Leigh Clark, Benjamin R. Cowan, and Erin Beneteau. 2022. The Last Decade of HCI Research on Children and Voice-Based Conversational Agents. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 149, 19 pages. https://doi.org/10.1145/3491102.3502016
[29]
Susan A Gelman. 1988. The development of induction within natural kind and artifact categories. Cognitive Psychology 20, 1 (1988), 65–95. https://doi.org/10.1016/0010-0285(88)90025-4
[30]
Usha Goswami. 2001. Analogical Reasoning in Children. 437 – 470.
[31]
Graeme S. Halford. 2009. Children’s understanding: The development of mental models. Erlbaum.
[32]
Paul L Harris. 2012. Trusting what you’re told: How children learn from others. Harvard University Press.
[33]
Jiangbo Hu, Camilla Gordon, Ning Yang, and Yonggang Ren. 2020. “Once Upon A Styoar”: A Science Education Program Based on Personification Storytelling in Promoting Preschool Children’s Understanding of Astronomy Concepts. Early Education and Development 32 (05 2020), 1–19. https://doi.org/10.1080/10409289.2020.1759011
[34]
Kayoko Inagaki and Giyoo Hatano. 1987. Young Children’s Spontaneous Personification as Analogy. Child Development 58, 4 (1987), 1013–1020. http://www.jstor.org/stable/1130542
[35]
Joan N Kaderavek, Ying Guo, and Laura M Justice. 2014. Validity of the children’s orientation to book reading rating scale. Journal of Research in Reading 37, 2 (2014), 159–178.
[36]
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 6769–6781.
[37]
Deborah Kelemen. 2019. The Magic of Mechanism: Explanation-Based Instruction on Counterintuitive Concepts in Early Childhood. Perspectives on Psychological Science 14 (04 2019), 174569161982701. https://doi.org/10.1177/1745691619827011
[38]
James Kennedy, Séverin Lemaignan, Caroline Montassier, Pauline Lavalade, Bahar Irfan, Fotios Papadopoulos, Emmanuel Senft, and Tony Belpaeme. 2017. Child Speech Recognition in Human-Robot Interaction: Evaluations and Recommendations. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (Vienna, Austria) (HRI ’17). Association for Computing Machinery, New York, NY, USA, 82–90. https://doi.org/10.1145/2909824.3020229
[39]
Yea-Seul Kim, Jessica Hullman, Matthew Burgess, and Eytan Adar. 2016. SimpleScience: Lexical Simplification of Scientific Terminology. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Austin, Texas, 1066–1071. https://doi.org/10.18653/v1/D16-1114
[40]
Alison King. 1994. Guiding knowledge construction in the classroom: Effects of teaching children how to question and how to explain. American educational research journal 31, 2 (1994), 338–368.
[41]
Christina Krist, Christina V. Schwarz, and Brian J. Reiser. 2019. Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning. Journal of the Learning Sciences 28, 2 (2019), 160–205. https://doi.org/10.1080/10508406.2018.1510404 arXiv:https://doi.org/10.1080/10508406.2018.1510404
[42]
Christoph Kulgemeyer. 2018. Towards a framework for effective instructional explanations in science teaching. Studies in Science Education 54, 2 (2018), 109–139. https://doi.org/10.1080/03057267.2018.1598054 arXiv:https://doi.org/10.1080/03057267.2018.1598054
[43]
Katelyn E Kurkul, Eleanor Castine, Kathryn Leech, and Kathleen H Corriveau. 2021. How does a switch work? The relation between adult mechanistic language and children’s learning. Journal of Applied Developmental Psychology 72 (2021), 101221. https://doi.org/10.1016/j.appdev.2020.101221
[44]
Katelyn E Kurkul and Kathleen H Corriveau. 2018. Question, explanation, follow-up: A mechanism for learning from others?Child Development 89, 1 (2018), 280–294.
[45]
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural Questions: A Benchmark for Question Answering Research. Transactions of the Association for Computational Linguistics 7 (2019), 452–466. https://doi.org/10.1162/tacl_a_00276
[46]
Yoonjoo Lee, John Joon Young Chung, Tae Soo Kim, Jean Y Song, and Juho Kim. 2022. Promptiverse: Scalable Generation of Scaffolding Prompts Through Human-AI Hybrid Knowledge Graph Annotation. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 96, 18 pages. https://doi.org/10.1145/3491102.3502087
[47]
Cristine H Legare. 2014. The contributions of explanation and exploration to children’s scientific reasoning. Child Development Perspectives 8, 2 (2014), 101–106. https://doi.org/10.1111/cdep.12070
[48]
Cristine H Legare and Tania Lombrozo. 2014. Selective effects of explanation on learning during early childhood. Journal of experimental child psychology 126 (2014), 198–212.
[49]
Gaea Leinhardt, Kevin Crowley, and Karen Knutson. 2015. Building islands of expertise in everyday family activity. Routledge.
[50]
Gaea Leinhardt and Michael Steele. 2005. Seeing the Complexity of Standing to the Side: Instructional Dialogues. Cognition and Instruction - COGNITION INSTRUCT 23 (03 2005), 87–163. https://doi.org/10.1207/s1532690xci2301_4
[51]
Dani Levine, Amy Pace, Rufan Luo, Kathy Hirsh-Pasek, Roberta Michnick Golinkoff, Jill de Villiers, Aquiles Iglesias, and Mary Sweig Wilson. 2020. Evaluating socioeconomic gaps in preschoolers’ vocabulary, syntax and language process skills with the Quick Interactive Language Screener (QUILS). Early Childhood Research Quarterly 50 (2020), 114–128.
[52]
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 7871–7880.
[53]
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–9474.
[54]
Margaret Li, Jason Weston, and Stephen Roller. 2019. ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons. https://doi.org/10.48550/ARXIV.1909.03087
[55]
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. Dailydialog: A manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957(2017).
[56]
Paul Light and George Butterworth. 2017. Chapter 7. Desituating cognition through the construction of conceptual knowledge. Routledge.
[57]
Mike E.U. Ligthart, Mark A. Neerincx, and Koen V. Hindriks. 2020. Design Patterns for an Interactive Storytelling Robot to Support Children’s Engagement and Agency. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge, United Kingdom) (HRI ’20). Association for Computing Machinery, New York, NY, USA, 409–418. https://doi.org/10.1145/3319502.3374826
[58]
Silvia B. Lovato, Anne Marie Piper, and Ellen A. Wartella. 2019. Hey Google, Do Unicorns Exist? Conversational Agents as a Path to Answers to Children’s Questions. In Proceedings of the 18th ACM International Conference on Interaction Design and Children (Boise, ID, USA) (IDC ’19). Association for Computing Machinery, New York, NY, USA, 301–313. https://doi.org/10.1145/3311927.3323150
[59]
Louis Martin, Angela Fan, Éric de la Clergerie, Antoine Bordes, and Benoît Sagot. 2021. MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases. arXiv preprint arXiv:2005.00352(2021).
[60]
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:2005.00661(2020).
[61]
Candice M. Mills, Judith. H. Danovitch, Victoria N. Mugambi, Kaitlin R. Sands, and Candice Pattisapu Fox. 2022. “Why do dogs pant?”: Characteristics of parental explanations about science predict children’s knowledge. Child Development 93, 2 (2022), 326–340. https://doi.org/10.1111/cdev.13681 arXiv:https://srcd.onlinelibrary.wiley.com/doi/pdf/10.1111/cdev.13681
[62]
Candice M Mills, Kaitlin R Sands, Sydney P Rowles, and Ian L Campbell. 2019. “I want to know more!”: Children are sensitive to explanation quality when exploring new information. Cognitive Science 43, 1 (2019), e12706.
[63]
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. 2021. WebGPT: Browser-assisted question-answering with human feedback. CoRR abs/2112.09332(2021). arXiv:2112.09332https://arxiv.org/abs/2112.09332
[64]
Nielsen. 2018. (Smart) speaking my language: Despite their vast capabilities, smart speakers are all about the music.
[65]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research 21, 140 (2020), 1–67. http://jmlr.org/papers/v21/20-074.html
[66]
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2383–2392.
[67]
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019. CoQA: A Conversational Question Answering Challenge. Transactions of the Association for Computational Linguistics 7 (2019), 249–266. https://doi.org/10.1162/tacl_a_00266
[68]
Darrel A Regier, William E Narrow, Diana E Clarke, Helena C Kraemer, S Janet Kuramoto, Emily A Kuhl, and David J Kupfer. 2013. DSM-5 field trials in the United States and Canada, Part II: test-retest reliability of selected categorical diagnoses. American journal of psychiatry 170, 1 (2013), 59–70.
[69]
Melissa N. Richards and Sandra L. Calvert. 2017. Measuring young U.S. children’s parasocial relationships: toward the creation of a child self-report survey. Journal of Children and Media 11, 2 (2017), 229–240. https://doi.org/10.1080/17482798.2017.1304969 arXiv:https://doi.org/10.1080/17482798.2017.1304969
[70]
Laura R Roehler and Danise J Cantlon. 1997. Scaffolding: A powerful tool in social constructivist classrooms.(1997).
[71]
Rod D. Roscoe and Michelene T. H. Chi. 2007. Understanding Tutor Learning: Knowledge-Building and Knowledge-Telling in Peer Tutors’ Explanations and Questions. Review of Educational Research 77, 4 (2007), 534–574. https://doi.org/10.3102/0034654307309920 arXiv:https://doi.org/10.3102/0034654307309920
[72]
Sherry Ruan, Liwei Jiang, Justin Xu, Bryce Joe-Kun Tham, Zhengneng Qiu, Yeshuang Zhu, Elizabeth L. Murnane, Emma Brunskill, and James A. Landay. 2019. QuizBot: A Dialogue-Based Adaptive Learning System for Factual Knowledge. Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3290605.3300587
[73]
Rosemary Russ, Rachel Scherr, David Hammer, and Jamie Mikeska. 2008. Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science. Science Education 92 (05 2008), 499 – 525. https://doi.org/10.1002/sce.20264
[74]
Alex Sciuto, Arnita Saini, Jodi Forlizzi, and Jason I. Hong. 2018. "Hey Alexa, What’s Up?": A Mixed-Methods Studies of In-Home Conversational Agent Usage. In Proceedings of the 2018 Designing Interactive Systems Conference (Hong Kong, China) (DIS ’18). Association for Computing Machinery, New York, NY, USA, 857–868. https://doi.org/10.1145/3196709.3196772
[75]
Catherine Snow. 1983. Literacy and language: Relationships during the preschool years. Harvard educational review 53, 2 (1983), 165–189.
[76]
Dan Su, Xiaoguang Li, Jindi Zhang, Lifeng Shang, Xin Jiang, Qun Liu, and Pascale Fung. 2022. Read before Generate! Faithful Long Form Question Answering with Machine Reading. https://doi.org/10.48550/ARXIV.2203.00343
[77]
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019. Mitigating Gender Bias in Natural Language Processing: Literature Review. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 1630–1640. https://doi.org/10.18653/v1/P19-1159
[78]
Anuj Tewari and John Canny. 2014. What Did Spot Hide? A Question-Answering Game for Preschool Children. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI ’14). Association for Computing Machinery, New York, NY, USA, 1807–1816. https://doi.org/10.1145/2556288.2557205
[79]
Barbara Tizard and Martin Hughes. 2008. Young children learning. John Wiley & Sons.
[80]
Araceli Valle and Maureen Callanan. 2006. Similarity Comparisons and Relational Analogies in Parent-Child Conversations About Science Topics. Merrill-Palmer Quarterly 52 (01 2006), 96–124. https://doi.org/10.1353/mpq.2006.0009
[81]
Stella Vosniadou and Marlene Schommer. 1988. Explanatory Analogies Can Help Children Acquire Information from Expository Text. Technical Report No. 460.
[82]
Lev Semenovich Vygotsky and Michael Cole. 1978. Mind in society: Development of higher psychological processes. Harvard university press.
[83]
Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, and Mohit Iyyer. 2022. Modeling Exemplification in Long-form Question Answering via Retrieval. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Seattle, United States, 2079–2092. https://doi.org/10.18653/v1/2022.naacl-main.151
[84]
Rainer Winkler, Sebastian Hobert, Antti Salovaara, Matthias Söllner, and Jan Marco Leimeister. 2020. Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agent. Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376781
[85]
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. 2020. Approximate nearest neighbor negative contrastive learning for dense text retrieval. arXiv preprint arXiv:2007.00808(2020).
[86]
Fangyuan Xu, Junyi Jessy Li, and Eunsol Choi. 2022. How Do We Answer Complex Questions: Discourse Structure of Long-form Answers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 3556–3572. https://doi.org/10.18653/v1/2022.acl-long.249
[87]
Ying Xu, Valery Vigil, Andres S. Bustamante, and Mark Warschauer. 2022. “Elinor’s Talking to Me!”:Integrating Conversational AI into Children’s Narrative Science Programming. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 166, 16 pages. https://doi.org/10.1145/3491102.3502050
[88]
Ying Xu and Mark Warschauer. 2020. A Content Analysis of Voice-Based Apps on the Market for Early Literacy Development. In Proceedings of the Interaction Design and Children Conference (London, United Kingdom) (IDC ’20). Association for Computing Machinery, New York, NY, USA, 361–371. https://doi.org/10.1145/3392063.3394418
[89]
Ying Xu and Mark Warschauer. 2020. Exploring Young Children’s Engagement in Joint Reading with a Conversational Agent. In Proceedings of the Interaction Design and Children Conference (London, United Kingdom) (IDC ’20). Association for Computing Machinery, New York, NY, USA, 216–228. https://doi.org/10.1145/3392063.3394417

Cited By

View all
  • (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: Jan-2025
  • (2024)Towards Modeling and Evaluating Instructional Explanations in Teacher-Student DialoguesProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678665(225-230)Online publication date: 4-Sep-2024
  • (2024)Ensuring Child Rights in the Age of AI: A Multidimensional Analysis of Existing FrameworksProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678643(76-83)Online publication date: 4-Sep-2024
  • Show More Cited By

Index Terms

  1. DAPIE: Interactive Step-by-Step Explanatory Dialogues to Answer Children’s Why and How Questions

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
        April 2023
        14911 pages
        ISBN:9781450394215
        DOI:10.1145/3544548
        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

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 19 April 2023

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. Children
        2. Conversational Agents
        3. Dialogue
        4. Natural Language
        5. Question Answering

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Funding Sources

        • KAIST-NAVER Hypercreative AI Center

        Conference

        CHI '23
        Sponsor:

        Acceptance Rates

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

        Upcoming Conference

        CHI 2025
        ACM CHI Conference on Human Factors in Computing Systems
        April 26 - May 1, 2025
        Yokohama , Japan

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)999
        • Downloads (Last 6 weeks)91
        Reflects downloads up to 25 Dec 2024

        Other Metrics

        Citations

        Cited By

        View all
        • (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: Jan-2025
        • (2024)Towards Modeling and Evaluating Instructional Explanations in Teacher-Student DialoguesProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678665(225-230)Online publication date: 4-Sep-2024
        • (2024)Ensuring Child Rights in the Age of AI: A Multidimensional Analysis of Existing FrameworksProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678643(76-83)Online publication date: 4-Sep-2024
        • (2024)Co-Creating Question-and-Answer Style Articles with Large Language Models for Research PromotionProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3660705(975-994)Online publication date: 1-Jul-2024
        • (2024)Interpretability Gone Bad: The Role of Bounded Rationality in How Practitioners Understand Machine LearningProceedings of the ACM on Human-Computer Interaction10.1145/36373548:CSCW1(1-34)Online publication date: 26-Apr-2024
        • (2024)One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI GenerationsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3662681(2518-2531)Online publication date: 3-Jun-2024
        • (2024)"When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts TherapyProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642852(1-21)Online publication date: 11-May-2024
        • (2024)GenQuery: Supporting Expressive Visual Search with Generative ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642847(1-19)Online publication date: 11-May-2024
        • (2024)AQuA: Automated Question-Answering in Software Tutorial Videos with Visual AnchorsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642752(1-19)Online publication date: 11-May-2024
        • (2024)Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative StorytellingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642647(1-23)Online publication date: 11-May-2024
        • Show More Cited By

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        Full Text

        HTML Format

        View this article in HTML Format.

        HTML Format

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media