This study examines the impact of Artificial Intelligence (AI) use on the learning behaviors of higher education students in the United States. The rapid growth and adoption of AI stresses the need for a balanced use of AI tools within higher education. The literature review highlights the concerns of AI dependency and the potential decline in students’ active learning and autonomy. The hypothesis suggests AI use is associated with a decrease in active learning and an increase in passive learning and laziness among students. The data was collected via survey, incorporating qualitative and quantitative data used to analyze responses from 64 higher education students across the U.S. The findings of the linear regression analysis indicate that AI use is associated with a decrease in active learning behaviors and an increase in passive learning, as well as laziness among higher-education students. This study emphasizes the need for a balanced and careful incorporation of AI tools within education systems. As AI rapidly grows, changes in educational law and regulations need to be made to reflect its impact on this sector. Adapting educational frameworks to account for AI’s influence will be essential in maximizing its benefits while mitigating potential drawbacks.

This project delves into stock price data to explore historical trends, volatility, and seasonal patterns using Python’s data visualization and analysis tools. By conducting a comprehensive exploratory data analysis (EDA), we aim to uncover key insights into stock price movements over time, with a particular focus on periods of heightened market activity. Notably, our analysis reveals that the highest levels of volatility occurred during the 2008 global financial crisis, highlighting the profound impact of economic downturns on market behavior. This project emphasizes skill-building in data visualization and the application of Pandas, and it is intended purely as an educational exercise rather than a basis for financial advice or decision-making.