diff --git a/components/data/chartData.tsx b/components/data/chartData.tsx index 1146391..1eb48a6 100644 --- a/components/data/chartData.tsx +++ b/components/data/chartData.tsx @@ -2,20 +2,20 @@ export const bar_data = [ { name: 'Text', overall: 163, - collaboration: 22, + collaboration: 24, competition: 9, - mixed_objectives: 54, + mixed_objectives: 56, text: 163, embodied: 7, virtual: 24, robotics: 5, - two_agents: 56, + two_agents: 58, reinforcement_learning: 21, agents_with_personas: 24, - human: 88, + human: 92, not_applicable: 148, - rule_based: 56, - more_than_three_agents: 33, + rule_based: 60, + more_than_three_agents: 35, more_information_asymmetrical: 2, prompting_and_in_context_learning: 47, qualitative: 35, @@ -23,13 +23,13 @@ export const bar_data = [ implicit_objectives: 18, finetuning: 36, agents_with_memory: 16, - more_omniscient: 3, + more_omniscient: 7, pretraining: 18, model_based: 45, simulated_humans: 14, agent_teams: 6, health: 18, - education: 4, + education: 8, policy: 11, }, { @@ -37,14 +37,14 @@ export const bar_data = [ overall: 95, virtual: 95, prompting_and_in_context_learning: 42, - rule_based: 84, + rule_based: 89, not_applicable: 52, - mixed_objectives: 38, - two_agents: 32, + mixed_objectives: 41, + two_agents: 37, qualitative: 22, - human: 39, + human: 44, human_agent: 35, - more_omniscient: 21, + more_omniscient: 26, simulated_humans: 26, finetuning: 21, embodied: 4, @@ -52,14 +52,14 @@ export const bar_data = [ implicit_objectives: 12, agents_with_memory: 9, pretraining: 1, - collaboration: 12, + collaboration: 14, competition: 6, agent_teams: 9, - more_than_three_agents: 13, + more_than_three_agents: 16, fully_omniscient: 1, health: 3, model_based: 4, - education: 20, + education: 25, robotics: 1, agents_with_personas: 1, }, @@ -132,29 +132,29 @@ export const area_data = [ human: 1, rule_based: 1, human_agent: 1, - agents_with_personas: 0, - fully_omniscient: 0, - health: 0, pretraining: 0, - not_applicable: 0, - education: 0, - qualitative: 0, - more_omniscient: 0, - implicit_objectives: 0, - two_agents: 0, - policy: 0, - finetuning: 0, simulated_humans: 0, - model_based: 0, + education: 0, more_information_asymmetrical: 0, - competition: 0, - text: 0, - prompting_and_in_context_learning: 0, + model_based: 0, + two_agents: 0, agent_teams: 0, + prompting_and_in_context_learning: 0, + agents_with_personas: 0, + qualitative: 0, agents_with_memory: 0, - more_than_three_agents: 0, + fully_omniscient: 0, + policy: 0, virtual: 0, + more_omniscient: 0, + text: 0, embodied: 0, + finetuning: 0, + competition: 0, + health: 0, + more_than_three_agents: 0, + implicit_objectives: 0, + not_applicable: 0, }, { name: '2016', @@ -176,18 +176,18 @@ export const area_data = [ more_than_three_agents: 1, model_based: 1, education: 1, - fully_omniscient: 0, - health: 0, pretraining: 0, - not_applicable: 0, - more_omniscient: 0, - implicit_objectives: 0, - policy: 0, simulated_humans: 0, more_information_asymmetrical: 0, - prompting_and_in_context_learning: 0, agent_teams: 0, + prompting_and_in_context_learning: 0, agents_with_memory: 0, + fully_omniscient: 0, + policy: 0, + more_omniscient: 0, + health: 0, + implicit_objectives: 0, + not_applicable: 0, }, { name: '2017', @@ -204,23 +204,23 @@ export const area_data = [ robotics: 1, qualitative: 1, human: 1, + pretraining: 0, + simulated_humans: 0, + education: 0, + more_information_asymmetrical: 0, + model_based: 0, + agent_teams: 0, + prompting_and_in_context_learning: 0, agents_with_personas: 0, collaboration: 0, fully_omniscient: 0, - health: 0, - pretraining: 0, - education: 0, - more_omniscient: 0, - implicit_objectives: 0, policy: 0, + more_omniscient: 0, + embodied: 0, finetuning: 0, - simulated_humans: 0, - model_based: 0, - more_information_asymmetrical: 0, - prompting_and_in_context_learning: 0, - agent_teams: 0, + health: 0, more_than_three_agents: 0, - embodied: 0, + implicit_objectives: 0, }, { name: '2018', @@ -251,9 +251,9 @@ export const area_data = [ education: 1, more_omniscient: 1, pretraining: 0, - policy: 0, more_information_asymmetrical: 0, agent_teams: 0, + policy: 0, }, { name: '2019', @@ -279,29 +279,29 @@ export const area_data = [ agent_teams: 1, model_based: 1, health: 1, - fully_omniscient: 0, pretraining: 0, - education: 0, - more_omniscient: 0, - policy: 0, simulated_humans: 0, + education: 0, more_information_asymmetrical: 0, agents_with_memory: 0, + fully_omniscient: 0, + policy: 0, + more_omniscient: 0, }, { name: '2020', text: 18, - mixed_objectives: 8, - more_than_three_agents: 5, + mixed_objectives: 9, + more_than_three_agents: 6, model_based: 10, human_agent: 11, collaboration: 5, - rule_based: 16, + rule_based: 17, not_applicable: 25, finetuning: 6, agents_with_personas: 1, qualitative: 5, - human: 17, + human: 18, embodied: 4, reinforcement_learning: 6, simulated_humans: 3, @@ -309,14 +309,14 @@ export const area_data = [ prompting_and_in_context_learning: 1, robotics: 5, fully_omniscient: 1, - two_agents: 8, + two_agents: 9, pretraining: 4, competition: 1, implicit_objectives: 1, health: 2, policy: 1, - education: 1, - more_omniscient: 1, + education: 2, + more_omniscient: 2, more_information_asymmetrical: 0, agent_teams: 0, agents_with_memory: 0, @@ -349,10 +349,10 @@ export const area_data = [ more_omniscient: 1, agents_with_personas: 1, prompting_and_in_context_learning: 1, - fully_omniscient: 0, pretraining: 0, more_information_asymmetrical: 0, agents_with_memory: 0, + fully_omniscient: 0, }, { name: '2022', @@ -384,28 +384,28 @@ export const area_data = [ education: 6, policy: 2, more_omniscient: 2, - fully_omniscient: 0, more_information_asymmetrical: 0, + fully_omniscient: 0, }, { name: '2023', collaboration: 25, embodied: 25, prompting_and_in_context_learning: 55, - more_than_three_agents: 20, - rule_based: 68, + more_than_three_agents: 21, + rule_based: 69, not_applicable: 81, text: 53, implicit_objectives: 18, finetuning: 22, - human: 52, + human: 53, human_agent: 42, reinforcement_learning: 18, competition: 6, - two_agents: 39, - mixed_objectives: 36, + two_agents: 40, + mixed_objectives: 37, agents_with_memory: 17, - more_omniscient: 10, + more_omniscient: 11, qualitative: 23, simulated_humans: 25, virtual: 31, @@ -417,31 +417,31 @@ export const area_data = [ fully_omniscient: 1, health: 7, policy: 2, - education: 7, + education: 8, more_information_asymmetrical: 0, }, { name: '2024', - collaboration: 11, + collaboration: 13, competition: 4, - mixed_objectives: 27, + mixed_objectives: 30, text: 34, embodied: 7, virtual: 35, robotics: 10, - two_agents: 22, + two_agents: 25, reinforcement_learning: 7, agents_with_personas: 5, - human: 33, + human: 38, not_applicable: 50, - rule_based: 41, - more_than_three_agents: 11, + rule_based: 46, + more_than_three_agents: 14, more_information_asymmetrical: 2, prompting_and_in_context_learning: 46, qualitative: 17, human_agent: 26, finetuning: 19, - more_omniscient: 7, + more_omniscient: 12, pretraining: 8, simulated_humans: 13, fully_omniscient: 1, @@ -450,7 +450,7 @@ export const area_data = [ agent_teams: 8, implicit_objectives: 8, model_based: 19, - education: 7, + education: 12, policy: 3, }, ]; diff --git a/components/papers.tsx b/components/papers.tsx index 6c319ed..ada8fe2 100644 --- a/components/papers.tsx +++ b/components/papers.tsx @@ -4242,6 +4242,114 @@ export const data: Paper[] = [ }, +{ title: "Traveling Bazaar: Portable Support for {Face-to-Face}\n Collaboration", + date: "05/2023", + environments: "mixed_objectives", + agents: "two_agents, more_than_three_agents", + evaluation: "human, rule_based", + 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}", + subsection: "applications/education", +}, + + +{ title: "Leveraging the Potential of Large Language Models in Education\n Through Playful and {Game-Based} Learning", + date: "02/2024", + environments: "mixed_objectives", + agents: "two_agents, more_than_three_agents", + evaluation: "human, rule_based", + 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}", + subsection: "applications/education", +}, + + +{ title: "Shared reality: physical collaboration with a virtual peer", + date: "04/2000", + environments: "collaboration", + agents: "two_agents", + evaluation: "human, rule_based", + 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}", + subsection: "applications/education", +}, + + +{ title: "Intelligent tutoring goes to the museum in the big city: a pedagogical agent for informal science education", + date: "06/2011", + environments: "collaboration", + agents: "two_agents", + evaluation: "human, rule_based", + 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}", + subsection: "applications/education", +}, + + +{ title: "PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning", + date: "05/2024", + environments: "mixed_objectives", + agents: "more_than_three_agents", + evaluation: "human, rule_based", + 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 = \" Honolulu HI\n USA \",\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}", + subsection: "applications/education", +}, + + +{ title: "Prompt-Gaming: A Pilot Study on LLM-Evaluating Agent in a Meaningful Energy Game", + date: "05/2024", + environments: "collaboration", + agents: "two_agents", + evaluation: "human, rule_based", + 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 = \" Honolulu HI\n USA \",\n series = {CHI EA '24},\n environments = {collaboration},\n agents = {two_agents},\n evaluation = {human, rule_based},\n other = {education, more_omniscient}\n}", + subsection: "applications/education", +}, + + +{ title: "Advancing Knowledge Together: Integrating Large Language Model-based Conversational AI in Small Group Collaborative Learning", + date: "05/2024", + environments: "mixed_objectives", + agents: "more_than_three_agents", + evaluation: "human, rule_based", + 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 = \" Honolulu HI\n USA \",\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}", + subsection: "applications/education", +}, + + +{ title: "Young Children's Creative Storytelling with ChatGPT vs. Parent: Comparing Interactive Styles", + date: "05/2024", + environments: "collaboration", + agents: "two_agents", + evaluation: "human, rule_based", + 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 = \" Honolulu HI\n USA \",\n series = {CHI EA '24},\n environments = {collaboration},\n agents = {two_agents},\n evaluation = {human, rule_based},\n other = {education, more_omniscient}\n}", + subsection: "applications/education", +}, + + +{ title: "Agent-Based Dynamic Collaboration Support in a Smart Office Space", + date: "07/2020", + environments: "mixed_objectives", + agents: "two_agents, more_than_three_agents", + evaluation: "human, rule_based", + 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}", + subsection: "applications/education", +}, + + { title: "Characterizing manipulation from AI systems", date: "10/2023", environments: "n/a", diff --git a/docs/helper.md b/docs/helper.md index dbebf71..3fb5f38 100644 --- a/docs/helper.md +++ b/docs/helper.md @@ -686,6 +686,14 @@ ### applications/education +[5, 2024] [PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning](https://doi.org/10.1145/3613905.3651008), Jiawen Liu et al., Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems + +[5, 2024] [Prompt-Gaming: A Pilot Study on LLM-Evaluating Agent in a Meaningful Energy Game](https://doi.org/10.1145/3613905.3650774), Andr\'{e}s Isaza-Giraldo et al., Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems + +[5, 2024] [Advancing Knowledge Together: Integrating Large Language Model-based Conversational AI in Small Group Collaborative Learning](https://doi.org/10.1145/3613905.3650868), Zhenyao Cai et al., Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems + +[5, 2024] [Young Children's Creative Storytelling with ChatGPT vs. Parent: Comparing Interactive Styles](https://doi.org/10.1145/3613905.3650770), Jenna H Chin et al., Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems + [4, 2024] [How to Teach Programming in the {AI} Era? Using {LLM}s as a Teachable Agent for Debugging](https://arxiv.org/abs/2310.05292), Qianou Ma et al., International Conference on Artificial Intelligence in Education [4, 2024] [Generating Situated Reflection Triggers About Alternative Solution Paths: A Case Study in Generative {AI} for Computer-Supported Collaborative Learning](https://arxiv.org/abs/2404.18262), Atharva Naik et al., International Conference on Artificial Intelligence in Education @@ -694,6 +702,9 @@ [3, 2024] [Doodlebot: An Educational Robot for Creativity and AI Literacy](https://doi.org/10.1145/3610977.3634950), Randi Williams et al., Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction +[2, 2024] [Leveraging the Potential of Large Language Models in Education + Through Playful and {Game-Based} Learning](https://doi.org/10.1007/s10648-024-09868-z), Stefan E Huber et al., Educ. Psychol. Rev. + [01, 2024] [{CodeAid}: Evaluating a Classroom Deployment of an {LLM-based} Programming Assistant that Balances Student and Educator Needs](http://arxiv.org/abs/2401.11314), Majeed Kazemitabaar et al., arXiv [01, 2024] [Learning Agent-based Modeling with {LLM} Companions: Experiences of Novices and Experts Using {ChatGPT} \& {NetLogo} Chat](http://arxiv.org/abs/2401.17163), John Chen et al., arXiv @@ -708,6 +719,10 @@ [05, 2023] [{CLASS} Meet {SPOCK}: An Education Tutoring Chatbot based on Learning Science Principles](http://arxiv.org/abs/2305.13272), Shashank Sonkar et al., arXiv +[5, 2023] [Traveling Bazaar: Portable Support for {Face-to-Face} + Collaboration](https://par.nsf.gov/biblio/10437737-traveling-bazaar-portable-support-face-face-collaboration), R Vitiello et al., Proceedings of 3rd Annual Meeting of the International + Society of the Learning Sciences ({ISLS}) + [3, 2023] [A Social Robot Reading Partner for Explorative Guidance](https://dl.acm.org/doi/10.1145/3568162.3576968), Xiajie Zhang et al., Proceedings of the 2023 {ACM/IEEE} International Conference on {Human-Robot} Interaction @@ -729,6 +744,8 @@ [6, 2021] [Going Online: A Simulated Student Approach for Evaluating Knowledge Tracing in the Context of Mastery Learning](http://files.eric.ed.gov/fulltext/ED615518.pdf), Qiao Zhang et al., International Educational Data Mining Society +[7, 2020] [Agent-Based Dynamic Collaboration Support in a Smart Office Space](https://aclanthology.org/2020.sigdial-1.31), Wang et al., Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue + [6, 2020] [Investigating differential error types between human and simulated learners](https://link.springer.com/chapter/10.1007/978-3-030-52237-7_47), D Weitekamp et al., Artif. Intell. [6, 2018] [Automated Pitch Convergence Improves Learning in a Social, @@ -740,11 +757,15 @@ [7, 2013] [How Effective are Pedagogical Agents for Learning? A {Meta-Analytic} Review](https://doi.org/10.2190/EC.49.1.a), Noah L Schroeder et al., Journal of Educational Computing Research +[6, 2011] [Intelligent tutoring goes to the museum in the big city: a pedagogical agent for informal science education](https://doi.org/10.1007/978-3-642-21869-9_22), H. Chad Lane et al., Proceedings of the 15th International Conference on Artificial Intelligence in Education + [1, 2011] [Architecture for Building Conversational Agents that Support Collaborative Learning](https://ieeexplore.ieee.org/document/5669250), Rohit Kumar et al., IEEE Transactions on Learning Technologies [3, 2010] [Using Intelligent Tutor Technology to Implement Adaptive Support for Student Collaboration](https://link.springer.com/article/10.1007/s10648-009-9116-9), Dejana Diziol et al., Educ. Psychol. Rev. +[4, 2000] [Shared reality: physical collaboration with a virtual peer](https://doi.org/10.1145/633292.633443), J. Cassell et al., CHI '00 Extended Abstracts on Human Factors in Computing Systems + [4, 1985] [Intelligent tutoring systems](http://dx.doi.org/10.1126/science.228.4698.456), J R Anderson et al., Science ### concerns/risks diff --git a/docs/paper_table.md b/docs/paper_table.md index f164fb9..c6bb1cc 100644 --- a/docs/paper_table.md +++ b/docs/paper_table.md @@ -326,10 +326,16 @@ | [The ai economist: Optimal economic policy design via two-level deep reinforcement learning](https://arxiv.org/abs/2108.02755) | 8, 2021 | ['text', 'mixed_objectives'] | ['more_than_three_agents', 'reinforcement_learning'] | ['human', 'rule_based'] | ['policy'] | | [Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist](https://arxiv.org/abs/2108.02904) | 08, 2021 | ['text', 'mixed_objectives'] | ['more_than_three_agents', 'reinforcement_learning'] | ['human', 'rule_based'] | ['policy'] | | [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://arxiv.org/abs/2004.13332) | 4, 2020 | ['text', 'mixed_objectives'] | ['more_than_three_agents'] | ['human', 'rule_based'] | ['policy'] | +| [PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning](https://doi.org/10.1145/3613905.3651008) | 5, 2024 | ['mixed_objectives'] | ['more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | +| [Prompt-Gaming: A Pilot Study on LLM-Evaluating Agent in a Meaningful Energy Game](https://doi.org/10.1145/3613905.3650774) | 5, 2024 | ['collaboration'] | ['two_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | +| [Advancing Knowledge Together: Integrating Large Language Model-based Conversational AI in Small Group Collaborative Learning](https://doi.org/10.1145/3613905.3650868) | 5, 2024 | ['mixed_objectives'] | ['more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | +| [Young Children's Creative Storytelling with ChatGPT vs. Parent: Comparing Interactive Styles](https://doi.org/10.1145/3613905.3650770) | 5, 2024 | ['collaboration'] | ['two_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | [How to Teach Programming in the {AI} Era? Using {LLM}s as a Teachable Agent for Debugging](https://arxiv.org/abs/2310.05292) | 4, 2024 | ['mixed_objectives', 'virtual'] | ['two_agents', 'prompting_and_in_context_learning'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | | [Generating Situated Reflection Triggers About Alternative Solution Paths: A Case Study in Generative {AI} for Computer-Supported Collaborative Learning](https://arxiv.org/abs/2404.18262) | 4, 2024 | ['mixed_objectives', 'virtual'] | ['more_than_three_agents', 'prompting_and_in_context_learning'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | | [Generative {AI} and {K}-12 {Education}: An {MIT} {Perspective}](https://mit-genai.pubpub.org/pub/4k9msp17/release/1) | 3, 2024 | ['mixed_objectives'] | ['two_agents'] | ['human', 'rule_based'] | ['education'] | | [Doodlebot: An Educational Robot for Creativity and AI Literacy](https://doi.org/10.1145/3610977.3634950) | 3, 2024 | ['mixed_objectives', 'virtual', 'robotics'] | ['more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | +| [Leveraging the Potential of Large Language Models in Education | 2, 2024 | ['mixed_objectives'] | ['two_agents', 'more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | +| Through Playful and {Game-Based} Learning](https://doi.org/10.1007/s10648-024-09868-z) | | | | | | | [{CodeAid}: Evaluating a Classroom Deployment of an {LLM-based} Programming Assistant that Balances Student and Educator Needs](http://arxiv.org/abs/2401.11314) | 01, 2024 | ['mixed_objectives', 'virtual'] | ['two_agents', 'prompting_and_in_context_learning'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'more_omniscient'] | | [Learning Agent-based Modeling with {LLM} Companions: Experiences of Novices and Experts Using {ChatGPT} \& {NetLogo} Chat](http://arxiv.org/abs/2401.17163) | 01, 2024 | ['mixed_objectives', 'virtual'] | ['two_agents', 'prompting_and_in_context_learning'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'more_omniscient'] | | [{AI-TA}: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source {LLMs}](http://arxiv.org/abs/2311.02775) | 11, 2023 | ['collaboration', 'text'] | ['two_agents', 'agents_with_memory', 'finetuning', 'reinforcement_learning', 'prompting_and_in_context_learning'] | ['human', 'model_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | @@ -337,6 +343,8 @@ | [Teach {AI} How to Code: Using Large Language Models as Teachable Agents for Programming Education](http://arxiv.org/abs/2309.14534) | 09, 2023 | ['mixed_objectives', 'virtual'] | ['two_agents', 'prompting_and_in_context_learning'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | | [{GPTeach}: Interactive {TA} Training with {GPT-based} Students](https://doi.org/10.1145/3573051.3593393) | 7, 2023 | ['mixed_objectives', 'virtual'] | ['two_agents', 'prompting_and_in_context_learning'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | | [{CLASS} Meet {SPOCK}: An Education Tutoring Chatbot based on Learning Science Principles](http://arxiv.org/abs/2305.13272) | 05, 2023 | ['mixed_objectives', 'virtual'] | ['two_agents'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | +| [Traveling Bazaar: Portable Support for {Face-to-Face} | 5, 2023 | ['mixed_objectives'] | ['two_agents', 'more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | +| Collaboration](https://par.nsf.gov/biblio/10437737-traveling-bazaar-portable-support-face-face-collaboration) | | | | | | | [A Social Robot Reading Partner for Explorative Guidance](https://dl.acm.org/doi/10.1145/3568162.3576968) | 3, 2023 | ['mixed_objectives', 'robotics'] | ['two_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | [AI for Students with Learning Disabilities: A Systematic Review](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4617715) | 1, 2023 | ['text', 'virtual'] | ['two_agents', 'more_than_three_agents'] | ['human'] | ['education'] | | [Pedagogical Agents](https://dl.acm.org/doi/10.1145/3563659.3563669) | 11, 2022 | ['mixed_objectives', 'virtual'] | ['two_agents', 'more_than_three_agents'] | ['human', 'rule_based'] | ['education'] | @@ -344,15 +352,18 @@ | [Escape!Bot: Social Robots as Creative Problem-Solving Partners](https://doi.org/10.1145/3527927.3532793) | 6, 2022 | ['mixed_objectives', 'robotics'] | ['two_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | [Designing {PairBuddy---A} Conversational Agent for Pair Programming](https://doi.org/10.1145/3498326) | 5, 2022 | ['mixed_objectives', 'virtual'] | ['two_agents'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | | [Going Online: A Simulated Student Approach for Evaluating Knowledge Tracing in the Context of Mastery Learning](http://files.eric.ed.gov/fulltext/ED615518.pdf) | 6, 2021 | ['mixed_objectives', 'virtual'] | ['more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'simulated_humans', 'more_omniscient'] | +| [Agent-Based Dynamic Collaboration Support in a Smart Office Space](https://aclanthology.org/2020.sigdial-1.31) | 7, 2020 | ['mixed_objectives'] | ['two_agents', 'more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | [Investigating differential error types between human and simulated learners](https://link.springer.com/chapter/10.1007/978-3-030-52237-7_47) | 6, 2020 | ['mixed_objectives', 'virtual'] | ['more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'simulated_humans', 'more_omniscient'] | | [Automated Pitch Convergence Improves Learning in a Social, | 6, 2018 | ['mixed_objectives', 'robotics'] | ['two_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | Teachable Robot for Middle School Mathematics](https://link.springer.com/chapter/10.1007/978-3-319-93843-1_21) | | | | | | | [Affective personalization of a social robot tutor for children’s second language skills](https://ojs.aaai.org/index.php/AAAI/article/view/9914) | 3, 2016 | ['robotics', 'collaboration'] | ['reinforcement_learning'] | ['human', 'model_based', 'rule_based'] | ['human_agent', 'education'] | | [Cognitive anatomy of tutor learning: Lessons learned with {SimStudent}](http://dx.doi.org/10.1037/a0031955) | 11, 2013 | ['mixed_objectives', 'virtual'] | ['two_agents'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | | [How Effective are Pedagogical Agents for Learning? A {Meta-Analytic} Review](https://doi.org/10.2190/EC.49.1.a) | 7, 2013 | ['mixed_objectives', 'virtual'] | ['two_agents'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | +| [Intelligent tutoring goes to the museum in the big city: a pedagogical agent for informal science education](https://doi.org/10.1007/978-3-642-21869-9_22) | 6, 2011 | ['collaboration'] | ['two_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | [Architecture for Building Conversational Agents that Support Collaborative Learning](https://ieeexplore.ieee.org/document/5669250) | 1, 2011 | ['mixed_objectives', 'virtual'] | ['more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | [Using Intelligent Tutor Technology to Implement Adaptive Support | 3, 2010 | ['mixed_objectives', 'virtual'] | ['more_than_three_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | for Student Collaboration](https://link.springer.com/article/10.1007/s10648-009-9116-9) | | | | | | +| [Shared reality: physical collaboration with a virtual peer](https://doi.org/10.1145/633292.633443) | 4, 2000 | ['collaboration'] | ['two_agents'] | ['human', 'rule_based'] | ['education', 'more_omniscient'] | | [Intelligent tutoring systems](http://dx.doi.org/10.1126/science.228.4698.456) | 4, 1985 | ['mixed_objectives', 'virtual'] | ['two_agents'] | ['qualitative', 'human', 'rule_based'] | ['education', 'human_agent', 'simulated_humans', 'more_omniscient'] | | [The potential of generative AI for personalized persuasion at scale](https://www.nature.com/articles/s41598-024-53755-0) | 2, 2024 | ['text', 'implicit_objectives'] | ['two_agents'] | ['rule_based'] | ['n/a'] | | [Jailbroken: How does llm safety training fail?](https://proceedings.neurips.cc/paper_files/paper/2023/file/fd6613131889a4b656206c50a8bd7790-Paper-Conference.pdf) | 2, 2024 | ['text'] | ['n/a'] | ['model_based'] | ['n/a'] | @@ -397,7 +408,7 @@ | [Ethical challenges in data-driven dialogue systems](https://dl.acm.org/doi/10.1145/3278721.3278723) | 12, 2018 | ['text'] | ['n/a'] | ['n/a'] | ['n/a'] | ### Basic Stats -Total number of papers: 393 +Total number of papers: 402 #### Subsections environments/language: 34 environments/embodied: 7 @@ -416,6 +427,6 @@ interactions/robot: 22 interactions/human: 20 applications/health: 19 applications/policy: 9 -applications/education: 26 +applications/education: 35 concerns/risks: 21 concerns/safety: 20 diff --git a/main.bib b/main.bib index 7ace79c..88187ca 100644 --- a/main.bib +++ b/main.bib @@ -5808,8 +5808,237 @@ @inproceedings{gordon2016affective other = {human_agent, education}, } -### END Education papers ### +@INPROCEEDINGS{Vitiello2023-xx, + title = "Traveling Bazaar: Portable Support for {Face-to-Face} + Collaboration", + booktitle = "Proceedings of 3rd Annual Meeting of the International + Society of the Learning Sciences ({ISLS})", + author = "Vitiello, R and Tiwari, S D and Murray, R C and Ros{\'e}, + C", + pages = "59--60", + month = may, + year = 2023, + conference = "ISLS", + location = "Montreal", + url = "https://par.nsf.gov/biblio/10437737-traveling-bazaar-portable-support-face-face-collaboration", + environments = {mixed_objectives}, + agents = {two_agents, more_than_three_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} + +@ARTICLE{Huber2024-by, + title = "Leveraging the Potential of Large Language Models in Education + Through Playful and {Game-Based} Learning", + author = "Huber, Stefan E and Kiili, Kristian and Nebel, Steve and Ryan, + Richard M and Sailer, Michael and Ninaus, Manuel", + abstract = "This perspective piece explores the transformative potential and + associated challenges of large language models (LLMs) in + education and how those challenges might be addressed utilizing + playful and game-based learning. While providing many + opportunities, the stochastic elements incorporated in how + present LLMs process text, requires domain expertise for a + critical evaluation and responsible use of the generated output. + Yet, due to their low opportunity cost, LLMs in education may + pose some risk of over-reliance, potentially and unintendedly + limiting the development of such expertise. Education is thus + faced with the challenge of preserving reliable expertise + development while not losing out on emergent opportunities. To + address this challenge, we first propose a playful approach + focusing on skill practice and human judgment. Drawing from + game-based learning research, we then go beyond this playful + account by reflecting on the potential of well-designed games to + foster a willingness to practice, and thus nurturing + domain-specific expertise. We finally give some perspective on + how a new pedagogy of learning with AI might utilize LLMs for + learning by generating games and gamifying learning materials, + leveraging the full potential of human-AI interaction in + education.", + journal = "Educ. Psychol. Rev.", + volume = 36, + number = 1, + pages = "25", + month = feb, + year = 2024, + url = "https://doi.org/10.1007/s10648-024-09868-z", + environments = {mixed_objectives}, + agents = {two_agents, more_than_three_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} + +@inproceedings{Cassell2000-ok, + 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.}, + title = {Shared reality: physical collaboration with a virtual peer}, + month = {April}, + year = {2000}, + isbn = {1581132484}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, + url = {https://doi.org/10.1145/633292.633443}, + doi = {10.1145/633292.633443}, + 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.}, + booktitle = {CHI '00 Extended Abstracts on Human Factors in Computing Systems}, + pages = {259–260}, + numpages = {2}, + keywords = {tangible interface, storytelling, shared reality, peer, embodied conversational agent, collaboration, children}, + location = {The Hague, The Netherlands}, + series = {CHI EA '00}, + environments = {collaboration}, + agents = {two_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} +@inproceedings{Lane2011-lv, + author = {Lane, H. Chad and Noren, Dan and Auerbach, Daniel and Birch, Mike and Swartout, William}, + title = {Intelligent tutoring goes to the museum in the big city: a pedagogical agent for informal science education}, + month = {June}, + year = {2011}, + isbn = {9783642218682}, + publisher = {Springer-Verlag}, + address = {Berlin, Heidelberg}, + 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.}, + booktitle = {Proceedings of the 15th International Conference on Artificial Intelligence in Education}, + pages = {155–162}, + numpages = {8}, + keywords = {coaching, computer science education, entertainment, informal science education, intelligent tutoring systems, pedagogical agents}, + location = {Auckland, New Zealand}, + series = {AIED'11}, + url = {https://doi.org/10.1007/978-3-642-21869-9_22}, + environments = {collaboration}, + agents = {two_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} + + +@inproceedings{Liu2024-od, + author = {Liu, Jiawen and Yao, Yuanyuan and An, Pengcheng and Wang, Qi}, + title = {PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning}, + month = {May}, + year = {2024}, + isbn = {9798400703317}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, + url = {https://doi.org/10.1145/3613905.3651008}, + doi = {10.1145/3613905.3651008}, + abstract = {In children’s collaborative learning, effective peer conversations can significantly enhance the quality of children’s 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’s creative thinking but may not consistently provide timely feedback. These findings highlight potential design improvements and considerations for peer agents in both roles.}, + booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems}, + articleno = {263}, + numpages = {6}, + keywords = {Collaborat learning, Conversational agent, Large Language Model, Peer conversation}, + location = " Honolulu HI + USA ", + series = {CHI EA '24}, + environments = {mixed_objectives}, + agents = {more_than_three_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} + +@inproceedings{Isaza-Giraldo2024-ek, + author = {Isaza-Giraldo, Andr\'{e}s and Bala, Paulo and Campos, Pedro F. and Pereira, Lucas}, + title = {Prompt-Gaming: A Pilot Study on LLM-Evaluating Agent in a Meaningful Energy Game}, + month = {May}, + year = {2024}, + isbn = {9798400703317}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, + url = {https://doi.org/10.1145/3613905.3650774}, + doi = {10.1145/3613905.3650774}, + 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’ interaction with an expert’s assessment, ChatGPT-3.5 correctly evaluated 81\% of player’s 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.}, + booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems}, + articleno = {272}, + numpages = {12}, + keywords = {Energy Communities, Game-based Learning, Large Language Models (LLMs), Natural Language Processing (NLP), Serious Games, Sustainability}, + location = " Honolulu HI + USA ", + series = {CHI EA '24}, + environments = {collaboration}, + agents = {two_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} + +@inproceedings{Cai2024-nb, + author = {Cai, Zhenyao and Park, Seehee and Nixon, Nia and Doroudi, Shayan}, + title = {Advancing Knowledge Together: Integrating Large Language Model-based Conversational AI in Small Group Collaborative Learning}, + month = {May}, + year = {2024}, + isbn = {9798400703317}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, + url = {https://doi.org/10.1145/3613905.3650868}, + doi = {10.1145/3613905.3650868}, + abstract = {In today’s 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’s role in discussions. The evaluation also reveals that learners perceive the bot’s tone and behavior as important for engagement. We discuss design implications for chatbot integration in collaborative learning and future research directions.}, + booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems}, + articleno = {37}, + numpages = {9}, + keywords = {AI facilitator, Collaborative Learning, Human-AI Collaboration}, + location = " Honolulu HI + USA ", + series = {CHI EA '24}, + environments = {mixed_objectives}, + agents = {more_than_three_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} + +@inproceedings{10.1145/3613905.3650770, + 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}, + title = {Young Children's Creative Storytelling with ChatGPT vs. Parent: Comparing Interactive Styles}, + month = {May}, + year = {2024}, + isbn = {9798400703317}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, + url = {https://doi.org/10.1145/3613905.3650770}, + doi = {10.1145/3613905.3650770}, + 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.}, + booktitle = {Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems}, + articleno = {379}, + numpages = {7}, + keywords = {ChatGPT, Children, Parents, Storytelling}, + location = " Honolulu HI + USA ", + series = {CHI EA '24}, + environments = {collaboration}, + agents = {two_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} + +@inproceedings{wang-etal-2020-agent, + title = "Agent-Based Dynamic Collaboration Support in a Smart Office Space", + author = "Wang, Yansen and + Murray, R. Charles and + Bao, Haogang and + Rose, Carolyn", + editor = "Pietquin, Olivier and + Muresan, Smaranda and + Chen, Vivian and + Kennington, Casey and + Vandyke, David and + Dethlefs, Nina and + Inoue, Koji and + Ekstedt, Erik and + Ultes, Stefan", + booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue", + month = jul, + year = "2020", + address = "1st virtual meeting", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.sigdial-1.31", + doi = "10.18653/v1/2020.sigdial-1.31", + pages = "257--260", + environments = {mixed_objectives}, + agents = {two_agents, more_than_three_agents}, + evaluation = {human, rule_based}, + other = {education, more_omniscient} +} + +### END Education papers ### ### Concerns