A Cognitive AI-Based Personalized Learning Environment for Data Structures and Algorithms

Data Structures and Algorithms is one of the key constituents of computer science education, and many of the current learning systems are based on predefined problem sets and offer limited feedback to the learners. To meet this requirement, I have incorporated an AI-assisted learning portal specifically intended to satisfy the needs of more guided and adaptive learning. This learning portal includes a web-based interface along with automated code execution and analysis, thus allowing the learner to analyze and adjust their code in the same interface. There is also React frontend integration along with Monaco Editor support for programming. Furthermore, They have also applied Spring Boot technology along with PostgreSQL and Judge0 execution engine support to execute codes in more than one programming language. Learner activity analysis along with error accuracy is analyzed to determine problem recommendations to the learner rather than depending on random problem recommendations. There is also analytics support to identify learner performance by analyzing his or her weaknesses and performance throughout time periods. Being based on containerized services, scalability is also incorporated along with easy access. Preliminary findings indicate effective engagement and marked accuracy improvements by providing feedback to the learner to adaptively learn algorithmic developments.