Abstract: The demand for personalized learning experiences that cater to the unique needs of individual learners has increased with the emergence of data science. This paper investigates the potential use of ChatGPT, a generative AI tool, in providing personalized learning experiences for data science education, specifically focusing on Deep Learning. The paper presents a case study that applies the 5Es model to test personalized learning for students using ChatGPT. The study aims to answer the question of how educators can leverage ChatGPT in their pedagogy to enhance student learning, and whether ChatGPT can provide a better learning experience than traditional teaching methods. The paper also discusses the limitations faced during the study and the findings. The results suggest that ChatGPT can be a valuable resource for data science education, providing personalized and instant feedback to learners. However, ethical considerations such as the potential for biased or inaccurate responses and the need for transparency in AI-generated content should be carefully ad-dressed by educators. The study highlights ChatGPT’s potential as a research tool for data science educators to investigate the effectiveness of AI in personalized learning experiences. Overall, this paper contributes to the ongoing dialogue on the role of AI in data science education and provides insights into how educators can utilize ChatGPT to enhance student learning and engagement.

Raneem N. Albdrani and Amal A. Al-Shargabi, “Investigating the Effectiveness of ChatGPT for Providing Personalized Learning Experience: A Case Study” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01411122

@article{Albdrani2023,
title = {Investigating the Effectiveness of ChatGPT for Providing Personalized Learning Experience: A Case Study},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01411122},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01411122},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Raneem N. Albdrani and Amal A. Al-Shargabi}
}