Work

More details, less variability? A crossover design study on the impact of information granularity on ChatGPT’s training program stability

Year: 2025

Type: article

Abstract: This study aimed to evaluate how varying levels of information granularity affect the output variability and multidimensional quality -including Personality, Effectiveness, Safety, and Feasibilityof ChatGPT-generated training programs.A crossover design was used to compare simple and detailed input prompts, with each prompt input into GPT-4 (accessed via ChatGPT) four times to generate eight training programs.The training programs were anonymized by the research team and subsequently evaluated in a blinded manner by 11 experts (mean age = 35.4years, average of 18.1 years of practical experience in the field of sport and exercise science).Output variability was assessed using the coefficient of variation (CV%), and quality ratings were based on a custom 15-item scale covering Personality, Effectiveness, Safety, and Feasibility.Differences in expert ratings across training programs were examined using repeated-measures ANOVA, with Friedman tests applied as sensitivity analyses to test the robustness of the results.Training programs generated from detailed input prompts consistently received higher expert ratings across all dimensions.CV% was generally lower under the detailed input prompts, indicating more stable outputs.Significant main effects of information granularity were found in Personality, Safety, Feasibility, and overall scores (all p < 0.05), though not in Effectiveness.Notably, repeated inputs of the same information granularity still yielded structurally and qualitatively different outputs, highlighting residual variability even under controlled conditions.Information granularity plays a crucial role in shaping the quality and stability of AI-generated training programs.Providing detailed, structured input enhances personalization, reduces output fluctuation, and improves alignment with exercise science principles.

Source: Biology of Sport

DOI: 10.5114/biolsport.2026.154148

Authors: Zhangyu Yang, Xing Zhang, Hansen Li, Jianfei Ye

Institutions: Fuyang Normal University, Universidad de Granada, Sichuan Agricultural University, Huangshan University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $480