Christian Enmanuel Lopez

Introduction: This study explores the potential of Large Language Models (LLMs), specifically ChatGPT-4, in generating Python programming questions with varying degrees of difficulty. This ability could significantly enhance adaptive educational applications. Methodology: Experiments were conducted with ChatGPT-4 and participants to evaluate its ability to generate questions on various topics and difficulty levels in programming. Results: The results reveal a moderate positive correlation between the difficulty ratings assigned by ChatGPT-4 and the perceived difficulty ratings given by participants. ChatGPT-4 proves to be effective in generating questions that cover a wide range of difficulty levels.Discussion: The study highlights ChatGPT-4’s potential for use in adaptive educational applications that accommodate different learning competencies and needs. Conclusions: This study presents a prototype of a gamified educational application for teaching Python, which uses ChatGPT to automatically generate questions of varying difficulty levels. Future studies should conduct more exhaustive experiments, explore other programming languages, and address more complex programming concepts.

This work presents a virtual reality (VR) Exergame application designed to prevent work-related musculoskeletal disorders (WMSDs). Moreover, to help adapt the tasks of the exergame, a machine learning model that predicts users’ exercise intensity level is presented. WMSDs are an important issue that can have a direct economic impact to an organization. Exercise and stretching is one method that can benefit workers and help prevent WMSDs. While several applications have been developed to prevent WMSDs, most of them suffer from a lack of immersivity or they just focus on education and not necessarily on helping workers warm-up or stretch. In light of this, an Exergame application that leverages VR and Depth-sensor technology to help provide users with an immersive first-person experience that engages them in physical activities is introduced in this work. The objective of the Exergame is to motivate users to perform full-body movements in order to pass through a series of obstacles. While in the game, users can visualize their motions by controlling the virtual avatar with their body movements. It is expected that this immersivity will motivate and encourage the users. Initial findings show the positive effects that the base exergame has on individuals’ motivation and physical activity. The results indicate that the application was able to engage individuals in low-intensity exercises that produced significant and consistent increases in their heart rate. Lastly, the results show that the machine learning model predicted users’ exercise activity level with an accuracy of 76.67%.