Update paper

sotopia-lab · May 25, 2024 · 26496cb · 26496cb
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diff --git a/components/papers.tsx b/components/papers.tsx
@@ -4603,6 +4603,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://par.nsf.gov/biblio/10437737-traveling-bazaar-portable-support-face-face-collaboration",
     bibtex: "@INPROCEEDINGS{Vitiello2023-xx,\n  title           = \"Traveling Bazaar: Portable Support for {Face-to-Face}\n                     Collaboration\",\n  booktitle       = \"Proceedings of 3rd Annual Meeting of the International\n                     Society of the Learning Sciences ({ISLS})\",\n  author          = \"Vitiello, R and Tiwari, S D and Murray, R C and Ros{\\'e},\n                     C\",\n  pages           = \"59--60\",\n  month           = may,\n  year            =  2023,\n  conference      = \"ISLS\",\n  location        = \"Montreal\",\n  url             = \"https://par.nsf.gov/biblio/10437737-traveling-bazaar-portable-support-face-face-collaboration\",\n  environments = {mixed_objectives},\n  agents = {two_agents, more_than_three_agents},\n  evaluation = {human, rule_based},\n  other = {education, more_omniscient}\n}",
+    authors: "Vitiello et al.",
     subsection: "applications/education",
 },
 
@@ -4615,6 +4616,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://doi.org/10.1007/s10648-024-09868-z",
     bibtex: "@ARTICLE{Huber2024-by,\n  title    = \"Leveraging the Potential of Large Language Models in Education\n              Through Playful and {Game-Based} Learning\",\n  author   = \"Huber, Stefan E and Kiili, Kristian and Nebel, Steve and Ryan,\n              Richard M and Sailer, Michael and Ninaus, Manuel\",\n  abstract = \"This perspective piece explores the transformative potential and\n              associated challenges of large language models (LLMs) in\n              education and how those challenges might be addressed utilizing\n              playful and game-based learning. While providing many\n              opportunities, the stochastic elements incorporated in how\n              present LLMs process text, requires domain expertise for a\n              critical evaluation and responsible use of the generated output.\n              Yet, due to their low opportunity cost, LLMs in education may\n              pose some risk of over-reliance, potentially and unintendedly\n              limiting the development of such expertise. Education is thus\n              faced with the challenge of preserving reliable expertise\n              development while not losing out on emergent opportunities. To\n              address this challenge, we first propose a playful approach\n              focusing on skill practice and human judgment. Drawing from\n              game-based learning research, we then go beyond this playful\n              account by reflecting on the potential of well-designed games to\n              foster a willingness to practice, and thus nurturing\n              domain-specific expertise. We finally give some perspective on\n              how a new pedagogy of learning with AI might utilize LLMs for\n              learning by generating games and gamifying learning materials,\n              leveraging the full potential of human-AI interaction in\n              education.\",\n  journal  = \"Educ. Psychol. Rev.\",\n  volume   =  36,\n  number   =  1,\n  pages    = \"25\",\n  month    =  feb,\n  year     =  2024,\n  url      = \"https://doi.org/10.1007/s10648-024-09868-z\",\n  environments = {mixed_objectives},\n  agents = {two_agents, more_than_three_agents},\n  evaluation = {human, rule_based},\n  other = {education, more_omniscient}\n}",
+    authors: "Huber et al.",
     subsection: "applications/education",
 },
 
@@ -4627,6 +4629,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://doi.org/10.1145/633292.633443",
     bibtex: "@inproceedings{Cassell2000-ok,\n  author = {Cassell, J. and Ananny, M. and Basu, A. and Bickmore, T. and Chong, P. and Mellis, D. and Ryokai, K. and Smith, J. and Vilhj\\'{a}lmsson, H. and Yan, H.},\n  title = {Shared reality: physical collaboration with a virtual peer},\n  month = {April},\n  year = {2000},\n  isbn = {1581132484},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/633292.633443},\n  doi = {10.1145/633292.633443},\n  abstract = {We describe a novel interface, in which a human and embodied conversational agent share a seamlessly integrated virtual and physical environment. This type of interface, in which objects are passed from the real to the virtual world, has potential applications in unsupervised learning, collaborative work, and entertainment. We introduce Sam, our first implementation of such an interface, which allows children to engage in natural storytelling play with real objects, in collaboration with a virtual playmate who shares access to those real objects.},\n  booktitle = {CHI '00 Extended Abstracts on Human Factors in Computing Systems},\n  pages = {259\u2013260},\n  numpages = {2},\n  keywords = {tangible interface, storytelling, shared reality, peer, embodied conversational agent, collaboration, children},\n  location = {The Hague, The Netherlands},\n  series = {CHI EA '00},\n  environments = {collaboration},\n  agents = {two_agents},\n  evaluation = {human, rule_based},\n  other = {education, more_omniscient}\n}",
+    authors: "Cassell et al.",
     subsection: "applications/education",
 },
 
@@ -4639,6 +4642,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://doi.org/10.1007/978-3-642-21869-9_22",
     bibtex: "@inproceedings{Lane2011-lv,\n  author = {Lane, H. Chad and Noren, Dan and Auerbach, Daniel and Birch, Mike and Swartout, William},\n  title = {Intelligent tutoring goes to the museum in the big city: a pedagogical agent for informal science education},\n  month = {June},\n  year = {2011},\n  isbn = {9783642218682},\n  publisher = {Springer-Verlag},\n  address = {Berlin, Heidelberg},\n  abstract = {In this paper, we describe Coach Mike, a virtual staff member at the Boston Museum of Science that seeks to help visitors at Robot Park, an interactive exhibit for computer programming. By tracking visitor interactions and through the use of animation, gestures, and synthesized speech, Coach Mike provides several forms of support that seek to improve the experiences of museum visitors. These include orientation tactics, exploration support, and problem solving guidance. Additional tactics use encouragement and humor to entice visitors to stay more deeply engaged. Preliminary analysis of interaction logs suggest that visitors can follow Coach Mike's guidance and may be less prone to immediate disengagement, but further study is needed.},\n  booktitle = {Proceedings of the 15th International Conference on Artificial Intelligence in Education},\n  pages = {155\u2013162},\n  numpages = {8},\n  keywords = {coaching, computer science education, entertainment, informal science education, intelligent tutoring systems, pedagogical agents},\n  location = {Auckland, New Zealand},\n  series = {AIED'11},\n  url = {https://doi.org/10.1007/978-3-642-21869-9_22},\n  environments = {collaboration},\n  agents = {two_agents},\n  evaluation = {human, rule_based},\n  other = {education, more_omniscient}\n}",
+    authors: "Lane et al.",
     subsection: "applications/education",
 },
 
@@ -4651,6 +4655,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://doi.org/10.1145/3613905.3651008",
     bibtex: "@inproceedings{Liu2024-od,\n  author = {Liu, Jiawen and Yao, Yuanyuan and An, Pengcheng and Wang, Qi},\n  title = {PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning},\n  month = {May},\n  year = {2024},\n  isbn = {9798400703317},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3613905.3651008},\n  doi = {10.1145/3613905.3651008},\n  abstract = {In children\u2019s collaborative learning, effective peer conversations can significantly enhance the quality of children\u2019s collaborative interactions. The integration of Large Language Model (LLM) agents into this setting explores their novel role as peers, assessing impacts as team moderators and participants. We invited two groups of participants to engage in a collaborative learning workshop, where they discussed and proposed conceptual solutions to a design problem. The peer conversation transcripts were analyzed using thematic analysis. We discovered that peer agents, while managing discussions effectively as team moderators, sometimes have their instructions disregarded. As participants, they foster children\u2019s creative thinking but may not consistently provide timely feedback. These findings highlight potential design improvements and considerations for peer agents in both roles.},\n  booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems},\n  articleno = {263},\n  numpages = {6},\n  keywords = {Collaborat learning, Conversational agent, Large Language Model, Peer conversation},\n  location  = \"<conf-loc> <city>Honolulu</city> <state>HI</state>\n               <country>USA</country> </conf-loc>\",\n  series = {CHI EA '24},\n  environments = {mixed_objectives},\n  agents = {more_than_three_agents},\n  evaluation = {human, rule_based},\n  other = {education, more_omniscient}\n}",
+    authors: "Liu et al.",
     subsection: "applications/education",
 },
 
@@ -4663,6 +4668,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://doi.org/10.1145/3613905.3650774",
     bibtex: "@inproceedings{Isaza-Giraldo2024-ek,\n  author = {Isaza-Giraldo, Andr\\'{e}s and Bala, Paulo and Campos, Pedro F. and Pereira, Lucas},\n  title = {Prompt-Gaming: A Pilot Study on LLM-Evaluating Agent in a Meaningful Energy Game},\n  month = {May},\n  year = {2024},\n  isbn = {9798400703317},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3613905.3650774},\n  doi = {10.1145/3613905.3650774},\n  abstract = {Building on previous work on incorporating large language models (LLM) in gaming, we investigate the possibility of implementing LLM as evaluating agents of open-ended challenges in serious games and its potential to facilitate a meaningful experience for the player. We contribute with a sustainability game prototype in a single natural language prompt about energy communities and we tested it with 13 participants inside ChatGPT-3.5. Two participants were already aware of energy communities before the game, and eight of the remaining 11 gained valuable knowledge about the specific topic. Comparing ChatGPT-3.5 evaluations of players\u2019 interaction with an expert\u2019s assessment, ChatGPT-3.5 correctly evaluated 81\\% of player\u2019s answers. Our results are encouraging and show the potential of using LLMs as mediating agents in educational games, while also allowing easy prototyping of games through natural language prompts.},\n  booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems},\n  articleno = {272},\n  numpages = {12},\n  keywords = {Energy Communities, Game-based Learning, Large Language Models (LLMs), Natural Language Processing (NLP), Serious Games, Sustainability},\n  location  = \"<conf-loc> <city>Honolulu</city> <state>HI</state>\n                <country>USA</country> </conf-loc>\",\n  series = {CHI EA '24},\n  environments = {collaboration},\n  agents = {two_agents},\n  evaluation = {human, rule_based},\n  other = {education, more_omniscient}\n}",
+    authors: "Isaza-Giraldo et al.",
     subsection: "applications/education",
 },
 
@@ -4675,6 +4681,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://doi.org/10.1145/3613905.3650868",
     bibtex: "@inproceedings{Cai2024-nb,\n  author = {Cai, Zhenyao and Park, Seehee and Nixon, Nia and Doroudi, Shayan},\n  title = {Advancing Knowledge Together: Integrating Large Language Model-based Conversational AI in Small Group Collaborative Learning},\n  month = {May}, \n  year = {2024},\n  isbn = {9798400703317},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3613905.3650868},\n  doi = {10.1145/3613905.3650868},\n  abstract = {In today\u2019s educational landscape, students learn collaboratively, where students benefit from both peer interactions and facilitator guidance. Prior research in Human-Computer Interaction (HCI) and Computer-Supported Collaborative Learning (CSCL) has explored chatbots and AI techniques to aid such collaboration. However, these methods often depend on predefined dialogues (which limits adaptability), are not based on collaborative learning theories, and do not fully recognize the learning context. In this paper, we introduce an Large Language Model (LLM)-powered conversational AI, designed to enhance small group learning through its advanced language understanding and generation capabilities. We detail the iterative design process, final design, and implementation. Our preliminary evaluation indicates that the bot performs as designed but points to considerations in the timing of interventions and bot\u2019s role in discussions. The evaluation also reveals that learners perceive the bot\u2019s tone and behavior as important for engagement. We discuss design implications for chatbot integration in collaborative learning and future research directions.},\n  booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems},\n  articleno = {37},\n  numpages = {9},\n  keywords = {AI facilitator, Collaborative Learning, Human-AI Collaboration},\n  location  = \"<conf-loc> <city>Honolulu</city> <state>HI</state>\n                <country>USA</country> </conf-loc>\",\n  series = {CHI EA '24},\n  environments = {mixed_objectives},\n  agents = {more_than_three_agents},\n  evaluation = {human, rule_based},\n  other = {education, more_omniscient}\n}",
+    authors: "Cai et al.",
     subsection: "applications/education",
 },
 
@@ -4687,6 +4694,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://doi.org/10.1145/3613905.3650770",
     bibtex: "@inproceedings{10.1145/3613905.3650770,\n  author = {Chin, Jenna H and Lee, Seungwook and Ashraf, Mohsena and Zago, Matt and Xie, Yun and Wolfgram, Elizabeth A and Yeh, Tom and Kim, Pilyoung},\n  title = {Young Children's Creative Storytelling with ChatGPT vs. Parent: Comparing Interactive Styles},\n  month = {May},\n  year = {2024},\n  isbn = {9798400703317},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3613905.3650770},\n  doi = {10.1145/3613905.3650770},\n  abstract = {Creative storytelling with parents plays an important role in child development including language skills, social competence, and emotional understanding. Recognizing the challenges parents face in finding time for storytelling due to work and home responsibilities, we explore the feasibility of ChatGPT for engaging children in creative storytelling. This study investigates the use of ChatGPT, a conversational agent powered by GPT-4, in creative storytelling with children aged 5-6, comparing its interaction styles with those of parents. The current study included eight child-parent dyads. We found that children were engaged in shorter and more frequent interactions with parents compared to ChatGPT. ChatGPT and parents asked different types of questions, and ChatGPT more frequently provided positive feedback compared to parents. More children selected the interactions with ChatGPT as their favorite interactions. The study provides preliminary evidence on ChatGPT's interaction styles and insights into its potential role in supporting families in creative storytelling activities.},\n  booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems},\n  articleno = {379},\n  numpages = {7},\n  keywords = {ChatGPT, Children, Parents, Storytelling},\n  location  = \"<conf-loc> <city>Honolulu</city> <state>HI</state>\n                  <country>USA</country> </conf-loc>\",\n  series = {CHI EA '24},\n  environments = {collaboration},\n  agents = {two_agents},\n  evaluation = {human, rule_based},\n  other = {education, more_omniscient}\n}",
+    authors: "Chin et al.",
     subsection: "applications/education",
 },
 
@@ -4699,6 +4707,7 @@ export const data: Paper[] = [
     other: "education, more_omniscient",
     url: "https://aclanthology.org/2020.sigdial-1.31",
     bibtex: "@inproceedings{wang-etal-2020-agent,\n    title = \"Agent-Based Dynamic Collaboration Support in a Smart Office Space\",\n    author = \"Wang, Yansen  and\n      Murray, R. Charles  and\n      Bao, Haogang  and\n      Rose, Carolyn\",\n    editor = \"Pietquin, Olivier  and\n      Muresan, Smaranda  and\n      Chen, Vivian  and\n      Kennington, Casey  and\n      Vandyke, David  and\n      Dethlefs, Nina  and\n      Inoue, Koji  and\n      Ekstedt, Erik  and\n      Ultes, Stefan\",\n    booktitle = \"Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue\",\n    month = jul,\n    year = \"2020\",\n    address = \"1st virtual meeting\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2020.sigdial-1.31\",\n    doi = \"10.18653/v1/2020.sigdial-1.31\",\n    pages = \"257--260\",\n    environments = {mixed_objectives},\n    agents = {two_agents, more_than_three_agents},\n    evaluation = {human, rule_based},\n    other = {education, more_omniscient}\n}",
+    authors: "Wang et al.",
     subsection: "applications/education",
 },