what Lab

Artificial Intelligence (AI) has rapidly emerged as a powerful tool with the potential to enhance learning environments. However, effective use of new technologies in education requires a good understanding of the technology and good design for its use. Generative AI such as ChatGPT requires particularly well-designed instructions due to its ease of use in asking and answering questions using natural language. This study investigates the effectiveness of ChatGPT in assisting the learning process of university students enrolled in Python programming courses. The primary question addressed is how differently the use of ChatGPT affects two groups at different levels of programming proficiency—beginners and intermediate. The study compares the impact of ChatGPT on academic performance between these two groups. The results show that for beginners, ChatGPT did not significantly contribute to improved learning outcomes. In addition, a survey was conducted to explore students’perceptions of the benefits and challenges of using ChatGPT, which revealed differences in responses between the two groups of learners. This research provides valuable insights into the potential applications of AI tools in educational settings, particularly in the context of programming education.

While machine learning (ML) is widely used for customer segmentation in e-commerce, its application in offline retail remains challenging due to limited data. This study investigates the efficacy of ML-based segmentation using two years of purchase data from a major department store, comparing traditional methods (Monetary, RFM) with three ML clustering algorithms. Results demonstrate that ML-based methods, particularly Hierarchical Clustering, significantly outperform traditional approaches in creating distinct customer segments. Our findings provide strong evidence that ML techniques can offer significant advantages for customer relationship management, even in data-scarce offline environments.