AI-Powered Personalized Learning Ecosystems: Building a Computer Science Framework for Scalable, Adaptive, and Next-Generation Education

In this study, we have examined how artificial intelligence can be used to design personalized learning ecosystems in computer science education to eventually develop a scaled and flexible framework to support teaching in the next generation. The study question was whether the AI-based personalization is superior on matters of learning engagement and performance compared to the traditional version. The data was collected among 480 undergraduate students with three various universities and followed using AI-facilitated platform which observed the presence, flexibility and outcomes. T-tests, ANOVA, regression analysis, factor analysis, reliability testing, correlation analysis were used in SPSS and R to run the statistical analysis. According to the information provided by the study, more involved students achieved those performance scores that were significantly higher. According to ANOVA, adaptive model Algorithm C was far much better than other adaptive models. The regression analysis demonstrated engagement and adaptive difficulty explained 42 percent of the performance and the correlation analysis indicated the existence of the strong positive correlation. These results suggest that in addition to promoting engagement and results, adaptive personalization can be reliably and validly measured. The research opens the door to the application of AI-powered ecosystem in order to scale individualized education.