Sports

会議の名前
CHI 2025
GPTCoach: Towards LLM-Based Physical Activity Coaching
要旨

Mobile health applications show promise for scalable physical activity promotion but are often insufficiently personalized. In contrast, health coaching offers highly personalized support but can be prohibitively expensive and inaccessible. This study draws inspiration from health coaching to explore how large language models (LLMs) might address personalization challenges in mobile health. We conduct formative interviews with 12 health professionals and 10 potential coaching recipients to develop design principles for an LLM-based health coach. We then built GPTCoach, a chatbot that implements the onboarding conversation from an evidence-based coaching program, uses conversational strategies from motivational interviewing, and incorporates wearable data to create personalized physical activity plans. In a lab study with 16 participants using three months of historical data, we find promising evidence that GPTCoach gathers rich qualitative information to offer personalized support, with users feeling comfortable sharing concerns. We conclude with implications for future research on LLM-based physical activity support.

著者
Matthew Jörke
Stanford University, Stanford, California, United States
Shardul Sapkota
Stanford University, Stanford, California, United States
Lyndsea Warkenthien
Stanford University, Stanford, California, United States
Niklas Vainio
Stanford University, Stanford, California, United States
Paul Schmiedmayer
Stanford University, Stanford , California, United States
Emma Brunskill
Stanford University, Stanford, California, United States
James A.. Landay
Stanford University, Stanford, California, United States
DOI

10.1145/3706598.3713819

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713819

動画
Efficient Management of LLM-Based Coaching Agents' Reasoning While Maintaining Interaction Quality and Speed
要旨

LLM-based agents improve upon standalone LLMs, which are optimized for immediate intent-satisfaction, by allowing the pursuit of more extended objectives, such as helping users over the long term. To do so, LLM-based agents need to reason before responding. For complex tasks like personalized coaching, this reasoning can be informed by adding relevant information at key moments, shifting it in the desired direction. However, the pursuit of objectives beyond interaction quality may compromise this very quality. Moreover, as the depth and informativeness of reasoning increase, so do the number of tokens required, leading to higher latency and cost. This study investigates how an LLM-based coaching agent can adjust its reasoning depth using a discrepancy mechanism that signals how much reasoning effort to allocate based on how well the objective is being met. Our discrepancy-based mechanism constrains reasoning to better align with alternative objectives, reducing cost roughly tenfold while minimally impacting interaction quality.

著者
Andreas Göldi
University of St.Gallen, St.Gallen, Switzerland
Roman Rietsche
Institute for Digital Technology Management, Bern, Bern, Switzerland
Lyle Ungar
University of Pennsylvania, Philadelphia, Pennsylvania, United States
DOI

10.1145/3706598.3713606

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713606

動画
Crafting Champions: An Observation Study of Esports Coaching Processes
要旨

As esports grows into a multi-million dollar industry of professional players and competitions, so too grows the interest in and need for professional coaching. Accordingly, there are increased demands and attempts to support and improve coaching for esports. A more comprehensive, granular understanding of the esports coaching process would provide a valuable foundation to inform opportunities to advance the domain via HCI theories and practices. However, in-depth studies of coaching practice, from the lens of HCI, are far less common in existing literature. In this paper, we take the first steps to provide such a foundation through an observation study conducted at an elite, award-winning League of Legends training academy. By analyzing 112 hours of dialogue and footage from coaching sessions, we identify pertinent activities and events that occur within the coaching process, which enable us to consider how esports coaching can be improved via theory, practice, and technology from HCI.

著者
Hanbyeol Lee
Yonsei University, Seoul, Korea, Republic of
Erica Kleinman
Northeastern University, Boston, Massachusetts, United States
Namsub Kim
Seoul National University of Science and Technology, Seoul, Korea, Republic of
Sangbeom Pak
T1, Seoul, Korea, Republic of
Casper Harteveld
Northeastern University, Boston, Massachusetts, United States
Byungjoo Lee
Yonsei University, Seoul, Korea, Republic of
DOI

10.1145/3706598.3713141

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713141

動画
BleacherBot: AI Agent as a Sports Co-Viewing Partner
要旨

Co-viewing, traditionally defined as watching content together in the same physical space, enhances emotional connections through shared experiences. With the rise of remote viewing during the COVID-19 pandemic, existing solutions, such as second-screen platforms and rule-based AI companions, struggle to facilitate meaningful social interactions. This study explores the potential of Large Language Models, which offer human-like interactions and personalization. Our formative study with ten participants revealed the importance of managing arousal levels, highlighting the need to balance between high- and low-arousal levels across different viewing contexts. Based on these insights, we developed `BleacherBot', a sports co-viewing agent with distinct interaction styles that vary in arousal levels. Our main study with 27 participants demonstrated that matching users' preferred arousal levels with the agent's interaction style significantly enhanced their engagement and overall enjoyment. We propose design guidelines for AI co-viewing agents that consider their role as complements to human social interactions.

著者
Hyungwoo Song
Seoul National University, Seoul, Korea, Republic of
Kyusik Kim
Seoul National University, Seoul, Korea, Republic of
Jeongwoo Ryu
Seoul National University, Seoul, Korea, Republic of
Changhoon Oh
Yonsei University, Seoul, Korea, Republic of
Bongwon Suh
Seoul National University, Seoul, Korea, Republic of
DOI

10.1145/3706598.3714178

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714178

動画
Bridging Coaching Knowledge and AI Feedback to Enhance Motor Learning in Basketball Shooting Mechanics Through a Knowledge-Based SOP Framework
要旨

We present a methodology for designing an AI feedback system aimed at assisting basketball beginners in refining their shooting techniques during independent practice sessions. Mastering shooting mechanics requires consistent, precise repetition, which traditionally depends on coaching feedback and the breakdown of movements into steps during the early stages. However, due to limited coaching resources, this guidance is often unavailable, leading to ineffective and even detrimental motor learning. To bridge this gap, we propose a Standard Operating Procedure (SOP) framework grounded in expert human knowledge, or knowledge-based SOP, which allows our AI-driven system to verify and guide players' movements in real-time. Through a formative study involving interviews with 13 coaches and players, we identified key challenges faced by beginners, such as uncertainty in movement correctness and lack of guidance during unsupervised practice. Our AI system addresses these issues by providing immediate, actionable feedback using SOP tailored to individual players. In a study with 28 participants, we confirmed that our system improves shooting form, increases confidence in adjustments, and enhances self-awareness during practice. This work highlights the potential of integrating coaching expertise with AI to empower athletes with more effective tools for self-directed practice.

受賞
Honorable Mention
著者
Jian-Jia Weng
National Tsing Hua University, Hsinchu, Taiwan
Calvin Ku
National Tsing Hua University, Hsinchu, Taiwan
Jo Chien Wang
National Tsing Hua University, Hsinchu, Taiwan
Chih-Jen Cheng
National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Tica Lin
Taiwan Institute of Sports Science, Kaohsiung, Taiwan
Yu-An Su
Acadmia Sinica, Taipei, Taiwan
Tsung-Hsun Tsai
National Tsing Hua University, Hsinchu, Taiwan
You-Yi Lin
Taipei First Girls High School, Taipei, Taiwan
Lun-Wei Ku
Academia Sinica, Taipei, Taiwan
Hung-Kuo Chu
National Tsing Hua University, Hsinchu, Taiwan
Min-Chun Hu
National Tsing Hua University, Hsinchu, Taiwan
DOI

10.1145/3706598.3713324

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713324

動画
Coach, Data Analyst, and Protector: Exploring Data Practices of Collegiate Coaching Staff
要旨

A rapidly emerging research community at the intersection of sport and human-computer interaction (SportsHCI) explores how technology can support physically active humans, such as athletes. At highly competitive levels, coaching staff play a central role in the athlete experience by using data to enhance performance, reduce injuries, and foster team success. However, little is known about the practices and needs of these coaching staff. We conducted five focus groups with 17 collegiate coaching staff across three women’s teams and two men’s teams at an elite U.S. university. Our findings show that coaching staff selectively use data with the goal of balancing performance goals, athlete emotional well-being, and privacy. This paper contributes design recommendations to support coaching staff in operating across the data life cycle through gathering, sharing, deciding, acting, and assessing data as they aim to support team success and foster the well-being of student-athletes.

著者
Mollie Brewer
University of Florida, Gainesville, Florida, United States
Kevin Childs
University of Florida, Gainesville, Florida, United States
Spencer Thomas
University Athletic Association, Gainesville, Florida, United States
Celeste Wilkins
University of Florida, Gainesville, Florida, United States
Kristy Elizabeth Boyer
University of Florida, Gainesville, Florida, United States
Jennifer A.. Nichols
University of Florida, Gainesville, Florida, United States
Kevin Butler
University of Florida, Gainesville, Florida, United States
Garrett F. Beatty
University of Florida, Gainesville, Florida, United States
Daniel P. Ferris
University of Florida, Gainesville, Florida, United States
DOI

10.1145/3706598.3714026

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714026

動画