In endurance sports, athletes and coaches shift increasingly from intuition-based decision-making to data-driven approaches powered by modern technology and analytics. Since 2018, the field has experienced significant advances, influencing endurance sports disciplines. This systematic literature review identified 75 peer-reviewed studies on intelligent data analysis in endurance sports training. Each study was categorized by its intelligent method (e.g., machine learning, deep learning, computational intelligence), the types of sensors and wearables used, and the specific training application and approach. Our synthesis reveals that machine learning and deep learning are among the most used approaches, with running and cycling identified as the most extensively studied sports. Physiological and environmental data, such as heart rate, biomechanical signals, and GPS, are often used to aid in generating personalized training plans, predicting injuries, and increasing athletes’ long-term performance. Despite these advancements, challenges remain, related to data quality and the small participant sample sizes.

This study investigates the effects of LLM-based coding assistance on web application development by students using a frontend framework. Rather than comparing different models, it focuses on how students interact with LLM tools to isolate the impact of query type on coding success. To this end, participants were instructed to rely exclusively on LLMs for writing code, based on a given set of specifications, and their queries were categorized into seven types: Error Fixing (EF), Feature Implementation (FI), Code Optimization (CO), Code Understanding (CU), Best Practices (BP), Documentation (DOC), and Concept Clarification (CC). The results reveal that students who queried LLMs for error fixing (EF) were statistically more likely to have runnable code, regardless of prior knowledge. Additionally, students seeking code understanding (CU) and error fixing performed better, even when normalizing for previous coding ability. These findings suggest that the nature of the queries made to LLMs influences the success of programming tasks and provides insights into how AI tools can assist learning in software development.

Pristop k razvoju aplikacij, ki temelji na uporabi orodij z malo ali nič programiranja, naslavlja že desetletja znane in prisotne izzive, povezane s produktivnostjo in agilnostjo v oddelkih za informatiko. Hkrati omogoča razvoj poslovnih aplikacij tudi strokovnjakom, ki nimajo programerskih znanj, zato predstavlja idealno rešitev za hiter razvoj inovativnih rešitev v procesih digitalne preobrazbe ter uveljavitev poslovno vodenega razvoja. V prispevku predstavljeni rezultati raziskave, v katero so bili vključeni predstavniki slovenskih podjetij ter študenti informatike, zagotavljajo vpogled v stopnjo poznavanja, sprejetosti in uporabnosti orodij za razvoj aplikacij z nič ali malo programiranja v Sloveniji ter nudijo možnost primerjave s svetovnimi trendi. Razkrivajo tudi nekatere razloge, zakaj pristop v našem prostoru še ni širše uporabljen, vsekakor pa velja izpostaviti pozitivna pričakovanja, ki sovpadajo z, v obstoječi literaturi identificiranimi pozitivnimi lastnostmi pristopa in orodij za razvoj aplikacij z malo ali nič programiranja. Z uporabo uveljavljenih modelov kot so SUS (angl. system usability scale), UMUX (angl. usability metric for user experience) in TAM (angl. technology acceptance model) smo ovrednotili uporabnost predstavnika tovrstnih orodij ter tako potrdili njihovo zrelost in primernost za širšo vpeljavo.