Innovation In Computer Science Learning Through Artificial Intelligence And Machine Learning Technologies - Tomi Yulianto; Eri Kristiono; Nada Ratković; Tonia Sharlach (Marlajar: Journal of Science, Technology, and Innovation, 2026)
This conceptual paper proposes a novel framework for integrating artificial intelligence (AI) and machine learning (ML) technologies to innovate computer science education. The study employs a systematic literature review methodology, synthesizing recent advances in deep learning, transfer learning, and lifelong learning paradigms from peer-reviewed sources [1]–[22]. The findings reveal that AI-driven adaptive learning systems significantly enhance personalized instruction and student engagement, while ML-based predictive analytics enable early intervention strategies for at-risk learners. However, critical challenges persist, including algorithmic bias, data privacy concerns, and the need for continuous curriculum updates to maintain relevance. The novelty of this work lies in its unified framework that bridges theoretical underpinnings, such as unsupervised representation learning and continual learning—with practical implementations in intelligent tutoring, automated content generation, and ethical assessment systems. The primary contribution is a comprehensive roadmap for educators and policymakers to deploy AI/ML technologies responsibly, addressing both pedagogical efficacy and ethical considerations. This framework further identifies gaps in current evaluation metrics and faculty development initiatives, offering actionable directions for future research. By synthesizing interdisciplinary insights from computer science, education, and ethics, this paper advances the discourse on sustainable innovation in computer science learning.