Exploring the Impact of Predictive Analytics and AI in STEM Education

Abstract

The demand for STEM education is rising globally, yet high attrition rates among underrepresented groups remain a significant challenge. This paper explores the potential of predictive analytics and learning analytics (LA) to enhance student retention and success in STEM fields. Predictive analytics, leveraging vast datasets including academic performance, engagement metrics, and demographic variables, allows educators to identify at-risk students early and implement targeted interventions. Recent advancements in artificial intelligence (AI) have further transformed these predictive models, enabling real-time adaptation of learning materials and personalized support. However, ethical concerns regarding data privacy, algorithmic bias, and equitable access must be addressed to ensure all students benefit from these innovations. Through a systematic literature review of studies published between 2020 and 2023, this paper highlights the effectiveness of predictive analytics in improving STEM education outcomes while emphasizing the importance of inclusive practices. Ultimately, this research underscores the potential of predictive analytics to revolutionize STEM education, fostering a more equitable and supportive learning environment for all students.

Keywords

• STEM
• Predictive Analytics
• Learning Analytics
• Personalized Learning
• AI