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Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly changing software engineering practices, especially in the education sector. This study examines how artificial intelligence (AI) and machine learning (ML) are transforming traditional software development through intelligent automation, predictive analytics, and learning-driven designs. Unlike traditional educational software, AI-powered platforms constantly analyze learner behavior, assessment performance, engagement, and cognitive progress. This allows the creation of personalized learning experiences in real time.
On the engineering side, AI-driven tools, such as GitHub Copilot, Tab Nine, and Code Whisperer, accelerate software development by automatically generating optimized code, identifying vulnerabilities, and improving maintainability. Machine Learning-based testing systems enhance quality assurance by predicting high-risk modules and generating relevant test cases automatically. In addition, AI-enabled DevOps pipelines provide high availability, cybersecurity, and performance improvement through automated cloud management and real-time issue detection.
The study also emphasizes how AI affects teaching by enabling student profiling, early dropout prediction, and personalized support for students. However, it highlights challenges such as bias, privacy concerns, and lack of transparency in black-box models. The study concludes that integrating AI and ML is leading educational systems toward more autonomous, data-driven, and constantly evolving learning environments, which is reshaping the future of intelligent academic infrastructure.
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