{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:59:11Z","timestamp":1781006351102,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3772318.3790459","type":"proceedings-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T04:12:26Z","timestamp":1776053546000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["TurnStyle: A Framework for Analyzing Human Conversational Behaviors to Predict Success in LLM-Assisted Tasks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1229-6790","authenticated-orcid":false,"given":"Urvi","family":"Awasthi","sequence":"first","affiliation":[{"name":"BCG X, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9133-226X","authenticated-orcid":false,"given":"Lisa","family":"Krayer","sequence":"additional","affiliation":[{"name":"Boston Consulting Group, Philadelphia, Pennsylvania, USA and BCG Henderson Institute, Philadelphia, Pennsylvania, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4013-1554","authenticated-orcid":false,"given":"Daniel","family":"Sack","sequence":"additional","affiliation":[{"name":"BCG X, Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Anthropic. [n. d.]. Models Overview \u2014 Claude: Model Comparison Table. Model comparison table year = 2025 note = Accessed: 2025-09-12."},{"key":"e_1_3_3_2_3_2","unstructured":"Anthropic. 2025. Messages API Documentation. https:\/\/docs.anthropic.com\/en\/api\/messages. Accessed: 2025-09-12."},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613905.3650841"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","unstructured":"Katharina Breckner Thomas Neumayr Matthias Mara Michael Streit and Markus Augstein. 2025. The Changing Nature of Human\u2012AI Relations: A Scoping Review on Terminology and Evolvement in the Scientific Literature. International Journal of Human\u2013Computer Interaction (2025) 1\u201358. 10.1080\/10447318.2025.2482742","DOI":"10.1080\/10447318.2025.2482742"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1037\/10096-006"},{"key":"e_1_3_3_2_7_2","unstructured":"James Fodor. 2025. Line Goes Up? Inherent Limitations of Benchmarks for Evaluating Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2502.14318\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2502.14318"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613905.3650786"},{"key":"e_1_3_3_2_9_2","unstructured":"E.\u00a0G.\u00a0Santana Jr Gabriel Benjamin Melissa Araujo Harrison Santos David Freitas Eduardo Almeida Paulo\u00a0Anselmo da M.\u00a0S.\u00a0Neto Jiawei Li Jina Chun and Iftekhar Ahmed. 2025. Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks. arxiv:https:\/\/arXiv.org\/abs\/2506.05614\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2506.05614"},{"key":"e_1_3_3_2_10_2","unstructured":"Minseok Jung Aurora Zhang May Fung Junho Lee and Paul\u00a0Pu Liang. 2025. Quantitative Insights into Large Language Model Usage and Trust in Academia: An Empirical Study. arxiv:https:\/\/arXiv.org\/abs\/2409.09186\u00a0[cs.CY] https:\/\/arxiv.org\/abs\/2409.09186"},{"key":"e_1_3_3_2_11_2","unstructured":"Tushar Khot Harsh Trivedi Matthew Finlayson Yao Fu Kyle Richardson Peter Clark and Ashish Sabharwal. 2023. Decomposed Prompting: A Modular Approach for Solving Complex Tasks. arxiv:https:\/\/arXiv.org\/abs\/2210.02406\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2210.02406"},{"key":"e_1_3_3_2_12_2","unstructured":"Oliver Kramer and Jill Baumann. 2024. Unlocking Structured Thinking in Language Models with Cognitive Prompting. arxiv:https:\/\/arXiv.org\/abs\/2410.02953\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2410.02953"},{"key":"e_1_3_3_2_13_2","unstructured":"Thomas Kwa Ben West Joel Becker Amy Deng Katharyn Garcia Max Hasin Sami Jawhar Megan Kinniment Nate Rush Sydney\u00a0Von Arx Ryan Bloom Thomas Broadley Haoxing Du Brian Goodrich Nikola Jurkovic Luke\u00a0Harold Miles Seraphina Nix Tao Lin Neev Parikh David Rein Lucas Jun\u00a0Koba Sato Hjalmar Wijk Daniel\u00a0M. Ziegler Elizabeth Barnes and Lawrence Chan. 2025. Measuring AI Ability to Complete Long Tasks. arxiv:https:\/\/arXiv.org\/abs\/2503.14499\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2503.14499"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3502030"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","unstructured":"Rong Li Ming Li and Weiguo Qiao. 2025. Engineering Students\u2019 Use of Large Language Model Tools: An Empirical Study Based on a Survey of Students from 12 Universities. Education Sciences 15 3 (2025) 280. 10.3390\/educsci15030280","DOI":"10.3390\/educsci15030280"},{"key":"e_1_3_3_2_16_2","unstructured":"Do\u00a0Xuan Long Duy Dinh Ngoc-Hai Nguyen Kenji Kawaguchi Nancy\u00a0F. Chen Shafiq Joty and Min-Yen Kan. 2025. What Makes a Good Natural Language Prompt? arxiv:https:\/\/arXiv.org\/abs\/2506.06950\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2506.06950"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","unstructured":"Timothy\u00a0R McIntosh Teo Susnjak Nalin Arachchilage Tong Liu Dan Xu Paul Watters and Malka\u00a0N Halgamuge. 2025. Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence. IEEE Transactions on Artificial Intelligence (2025) 1\u201318. 10.1109\/tai.2025.3569516","DOI":"10.1109\/tai.2025.3569516"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Bruce\u00a0M. Mclaren Oliver Scheuer and Jan Mik\u0161\u00e1tko. 2010. Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques. Int. J. Artif. Intell. Ed. 20 1 (Jan. 2010) 1\u201346.","DOI":"10.3233\/JAI-2010-0001"},{"key":"e_1_3_3_2_19_2","unstructured":"Hunter McNichols Fareya Ikram and Andrew Lan. 2025. The StudyChat Dataset: Student Dialogues With ChatGPT in an Artificial Intelligence Course. arxiv:https:\/\/arXiv.org\/abs\/2503.07928\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2503.07928"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3714401"},{"key":"e_1_3_3_2_21_2","unstructured":"OpenAI. 2025. GPT-5 Nano. https:\/\/platform.openai.com\/docs\/models\/gpt-5-nano. Accessed: 2025-09-12."},{"key":"e_1_3_3_2_22_2","unstructured":"OpenAI. 2025. Text Guide. https:\/\/platform.openai.com\/docs\/guides\/text. Accessed: 2025-09-12."},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Dino Pedreschi Luca Pappalardo Emanuele Ferragina Ricardo Baeza-Yates Albert-Laszlo Barabasi Frank Dignum Virginia Dignum Tina Eliassi-Rad Fosca Giannotti Janos Kertesz Alistair Knott Yannis Ioannidis Paul Lukowicz Andrea Passarella Alex\u00a0Sandy Pentland John Shawe-Taylor and Alessandro Vespignani. 2024. Human-AI Coevolution. arxiv:https:\/\/arXiv.org\/abs\/2306.13723\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2306.13723","DOI":"10.1016\/j.artint.2024.104244"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","unstructured":"Martin\u00a0J. Pickering and Simon Garrod. 2004. Toward a Mechanistic Psychology of Dialogue. Behavioral and Brain Sciences 27 2 (2004) 169\u2013190. 10.1017\/S0140525X04000056","DOI":"10.1017\/S0140525X04000056"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3580995"},{"key":"e_1_3_3_2_26_2","unstructured":"Pranab Sahoo Ayush\u00a0Kumar Singh Sriparna Saha Vinija Jain Samrat Mondal and Aman Chadha. 2025. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. arxiv:https:\/\/arXiv.org\/abs\/2402.07927\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2402.07927"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","unstructured":"Alexander Scarlatos Ryan\u00a0S. Baker and Andrew Lan. 2024. Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs. arxiv:https:\/\/arXiv.org\/abs\/2409.16490\u00a0[cs.CL] 10.1145\/3706468.3706501","DOI":"10.1145\/3706468.3706501"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706468.3706501"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.5555\/38407"},{"key":"e_1_3_3_2_30_2","unstructured":"Emma Wiles Lisa Krayer Mohamed Abbadi Urvi Awasthi Ryan Kennedy Pamela Mishkin Daniel Sack and Francois Candelon. 2024. GenAI as an Exoskeleton: Experimental Evidence on Knowledge Workers Using GenAI on New Skills. https:\/\/ssrn.com\/abstract=4300783 Manuscript. Pre-registered March 13 2024."},{"key":"e_1_3_3_2_31_2","unstructured":"Emma Wiles Lisa Krayer Mohamed Abbadi Urvi Awasthi Ryan Kennedy Pamela Mishkin Daniel Sack and Francois Candelon. 2024. Online Appendix for Generative AI as a Temporary Exoskeleton for Upskilling Knowledge Workers. Online appendix \/ supplementary materials."},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3643991.3648400"},{"key":"e_1_3_3_2_33_2","unstructured":"Tiffany Zhu Kexun Zhang and William\u00a0Yang Wang. 2024. Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students. arxiv:https:\/\/arXiv.org\/abs\/2411.18708\u00a0[cs.HC] https:\/\/arxiv.org\/abs\/2411.18708"},{"key":"e_1_3_3_2_34_2","unstructured":"Glenn Zorpette. 2025. Large Language Models Are Improving Exponentially. IEEE Spectrum (2 July 2025). https:\/\/spectrum.ieee.org\/large-language-model-performance"},{"key":"e_1_3_3_2_35_2","unstructured":"Glenn Zorpette. 2025. LLM Benchmarking Shows Capabilities Doubling Every 7 Months. IEEE Spectrum (2 July 2025). https:\/\/spectrum.ieee.org\/llm-benchmarking-metr"}],"event":{"name":"CHI 2026: CHI Conference on Human Factors in Computing Systems","location":"Barcelona Spain","acronym":"CHI '26","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3772318.3790459","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:13:23Z","timestamp":1781003603000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3772318.3790459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":34,"alternative-id":["10.1145\/3772318.3790459","10.1145\/3772318"],"URL":"https:\/\/doi.org\/10.1145\/3772318.3790459","relation":{},"subject":[],"published":{"date-parts":[[2026,4,13]]},"assertion":[{"value":"2026-04-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}