TEACHING ASSISTIVE TECHNOLOGIES: HOW AI CAN PERSONALIZE LEARNING IN COMPUTER SCIENCE

Abstract

Rapid technological advances and the increasing demand for skilled computer science (CS) professionals have led educators to explore innovative strategies for effective learning. One such strategy is the use of artificial intelligence (AI) in teaching assistive technologies that personalize learning experiences. These AI-driven tools tailor instructional content, pacing, and support to individual student needs, thereby fostering deeper engagement and knowledge retention. Current applications of AI in computer science education range from predictive analytics and adaptive feedback systems to intelligent tutoring platforms. Despite promising early results—such as improved academic performance, motivation, and learner autonomy—questions remain regarding the ethical handling of student data, equitable access to AI resources, and teacher readiness to deploy these tools in varied educational contexts. This article examines existing literature on AI-driven personalized learning within CS education and discusses key challenges, emerging trends, and potential pathways for the implementation of assistive technologies. Results from initial classroom pilots highlight AI’s capacity to address diverse learning styles and encourage meaningful participation. The article concludes by recommending a holistic approach that combines robust technical infrastructures, continuous teacher development, and policy frameworks to ensure effective, ethical, and sustainable adoption of AI in computer science curricula.