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Artificial intelligence-generated marathon training programs: reliable tools in exercise prescription for athletic

Gianluca Montaruli¹, Pantelis Theodoros Nikolaidis², Ghazi Racil^3,4

¹Le Club Gym, 29121 Piacenza, Italy.

British Medical Bulletin

|February 21, 2026

Summary

This summary is machine-generated.

Artificial intelligence shows promise for creating marathon training plans, but personalization and accuracy need improvement before widespread use. Further research is essential to validate AI-driven endurance coaching.

Keywords:

artificial intelligence chatbots digital training innovation marathon running smart training

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Area of Science:

Sports Science
Artificial Intelligence in Fitness

Background:

Marathon running is increasingly popular globally.
Training requires structured plans for pacing, volume, and periodization.
The role of AI in prescribing evidence-based marathon training is not yet clear.

Purpose of the Study:

To evaluate the ability of leading AI models to generate evidence-based marathon training plans.
To compare AI-generated plans against established endurance training principles.

Main Methods:

A descriptive study analyzed outputs from eight leading AI models.
AI models were prompted to create 6-month marathon training plans for beginner, intermediate, and advanced runners.
Outputs were qualitatively assessed against peer-reviewed marathon training literature.

Main Results:

Most AI models identified key training elements like mileage progression, tapering, and low-intensity focus.
Significant variations in accuracy and completeness were observed across AI models.
Some AI plans lacked detail, failed to differentiate athlete levels, or provided inconsistent pacing data, particularly for advanced runners.

Conclusions:

AI has strong potential for generating accessible, structured training content aligned with current principles.
Limitations in personalization and the need for professional oversight require further validation.
Future research should focus on real-world outcomes and integrating personal physiological data into AI training programs.