Computer Science education, particularly at the beginner level, often presents challenges due to vague and unhelpful error messages. This problem is particularly significant for students with low self-efficacy, leading to hindered learning experiences. Large Language Models (LLMs) offer a promising solution by generating more comprehensible and supportive error messages. This study aims to assess whether the rewriting of error messages using LLMs can improve self-efficacy among high school students, focusing on self-efficacy and study success as indicators of improved learning experiences. Through in-class experiments with 32 participants, the findings revealed that LLM rewritten error messages, although consistent with existing research, did not produce statistically significant effects. Therefore, more research is needed to evaluate their impact on learning outcomes and explore the most effective types of prompt. This research contributes to understanding the role of LLMs in educational settings, providing empirical insights into their effectiveness in real-world scenarios.

To reduce food waste, it is important to know what strawberries to prioritise for harvesting. Size is an important quality attribute for strawberry. In order to know the size, the depth of the strawberry in the image must been known. To estimate the depth, stereovision gets utilized using binocular images. Since classic stereovision methods are quite inaccurate in predicting small areas in images, a technique by [Mustafah et al., 2013] is used. Using given segments of the strawberries, the left and right strawberry images will get matched. With the matched strawberries, the disparity can be calculated and thus the depth. Using the depth, the size can be estimated.