Paper Number
1027
Paper Type
Complete Research Paper
Abstract
The use of fitness trackers and sport applications has increased among recreational athletes (RAs), aiming to enhance performance and achieve diverse fitness goals. However, the challenge arises when RAs struggle to interpret and adapt their training effectively based on the data collected. Emerging artificial intelligence based trainers (AITs) offer a promising solution by creating personalized training plans. Despite their potential, the adoption of these AITs is inhibited by a lack of trust-building mechanisms in their design. This research emphasizes the critical need for a trust-centered design approach for AITs tailored to RAs' needs. By establishing a design theory derived from athlete insights, the study aims to enhance trust in these systems. Through a focus on data control mechanisms, explainability, and human-like interaction, the research provides a foundation for developing AITs that RAs can trust, potentially leading to increased adoption of these systems in the context of sports training.
Recommended Citation
Strunk, Jobin Alexander; Nissen, Anika; and Smolnik, Stefan, "Getting Trust in Shape: Towards an Athlete-Centered Design Approach for AI-Based Trainers" (2024). ECIS 2024 Proceedings. 3.
https://aisel.aisnet.org/ecis2024/track14_esports/track14_esports/3
Getting Trust in Shape: Towards an Athlete-Centered Design Approach for AI-Based Trainers
The use of fitness trackers and sport applications has increased among recreational athletes (RAs), aiming to enhance performance and achieve diverse fitness goals. However, the challenge arises when RAs struggle to interpret and adapt their training effectively based on the data collected. Emerging artificial intelligence based trainers (AITs) offer a promising solution by creating personalized training plans. Despite their potential, the adoption of these AITs is inhibited by a lack of trust-building mechanisms in their design. This research emphasizes the critical need for a trust-centered design approach for AITs tailored to RAs' needs. By establishing a design theory derived from athlete insights, the study aims to enhance trust in these systems. Through a focus on data control mechanisms, explainability, and human-like interaction, the research provides a foundation for developing AITs that RAs can trust, potentially leading to increased adoption of these systems in the context of sports training.
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