Reimagining the role of technology in sport officiating: how artificial intelligence (AI) supports the design and delivery of ecologically dynamic development processes

Abstract

ABSTRACT Purpose/Rationale Sport officials operate within settings that dynamically change and shift. While they gather, synthesise, and store experiences related to task, performer, and environmental constraints, their internal mental models of judgement and decision-making individually evolve as they perform in different contexts. However, while a large body of work in psychology and behavioural economics has attempted to capture the way humans make decisions, there is a growing realisation among researchers, evaluators, and educational designers that quality improvement interventions cannot be understood outside of the context in which they occur [Ramaswamy, R., Reed, J., Livesley, N., Boguslavsky, V., Garcia-Elorrio, E., Sax, S., Houleymata D., Kimble L., Parry, G. (2018). Unpacking the black box of improvement. International Journal for Quality in Health Care, 30(suppl_1), 15–19. https://doi.org/10.1093/intqhc/mzy009]. Approach In this futuristic proposal, we put forward our vision of how artificial intelligence technologies can unpack and support the internal collections of cognitive knowledge, context, task goals, and on-field experiences that influence sport officiating development. Findings In what follows, we define what we mean by artificial intelligence and machine learning technologies, briefly highlighting their histories in sport analytics contexts. We outline how education is a promising field for the adoption of artificial intelligence/machine learning technologies and conclude by providing a theoretical case study scenario that describes a potential platform through which perspectives of environment, task and performer expertise might be developed for amateur and elite sport officials. Practical implications Using advanced AI technologies as the basis through which to examine on-field data provides tremendous potential to theoretically tackle the idiosyncrasies of officiating development in a range of sports as it can close the gap between a descriptive analysis (i.e. understanding the interactions undertaken by officials in the presence of others), and a more prescriptive one (i.e. suggesting the actions such officials should have executed). Research contribution We put forward that artificial intelligence technologies can offer sport organisations sophisticated, constructively based help with opening the “black box” of learning related to sport officiating development. By using ecological dynamics as the fundamental framework through which to filter the data collected, statistical information and qualitative ecological outcomes can be linked and managed into understandable and stable development content [Liu, A., Mahapatra, R. P., & Mayuri, A. V. R. (2021). Hybrid design for sports data visualization using AI and big data analytics. Complex & Intelligent Systems, https://doi.org/10.1007/s40747-021-00557-w], that strongly benefits the online development of amateur sport officials.