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Not only can a one-sided choice for abdominal muscles (lack of exercise focused on different core muscles) lead to creating muscle imbalances, however the effectiveness of exercise is also removed from what might be achieved with a balanced workout planning. We imagine that the most important challenge in adapting courses and classes to this brave new world where LLMs with code generation capability are easily accessible is creating exercises that are laborious for an LLM to unravel, however are still accessible and can be solved by college students. However, close to the benefit of be aware taking, most students really feel that videos are usually not as good as textual descriptions. Videos also appear to have a positive influence on students’ motivation (Table 1: Question 3, RQ2). Hands-on workouts are the most direct way to assess these skills, but assessing students’ mastery is a challenging job for instructors. However, these results confirmed that STEs grading stays a difficult process even for a slicing-edge LLM corresponding to GPT-4, when provided with only few-shot examples. As mentioned in Section 5.2, the models showed notably lower performance in grading incorrect responses than in grading correct responses.
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