Teaching Computer Science in the Digital and AI Era: Current Methods, Pedagogical Challenges, and Future Directions

Abstract

This article critically reviews the current state of computer science and informatics teaching and develops a practical framework for its improvement in the digital and artificial-intelligence era. The study adopts a structured narrative-review approach and synthesizes international standards, systematic reviews, and recent scholarship on programming education, computational thinking, active learning, mobile learning, digital competence, data literacy, Internet of Things (IoT), and artificial intelligence in education. The analysis shows that effective computer science teaching is not defined by the presence of technology alone. Learning outcomes depend on the alignment of pedagogy, content, learner readiness, teacher competence, assessment, and technological infrastructure. Direct explanation and demonstration remain valuable for introducing unfamiliar concepts, but they are most effective when followed by purposeful practice, debugging, problem solving, collaborative work, projects, and reflective feedback. Recent evidence supports flipped learning, gamification, mobile tools, and other active methodologies when they are connected to clearly specified learning objectives rather than used as isolated innovations. The review also argues that contemporary curricula should extend beyond software operation toward computational thinking, data reasoning, AI literacy, digital ethics, cybersecurity, and the ability to design and evaluate technological solutions. Based on the synthesis, the article proposes a balanced instructional model that combines explicit teaching, guided practice, inquiry, collaborative construction, authentic projects, and formative assessment. It concludes that the future of computer science education depends on pedagogically justified technology integration, continuous teacher development, inclusive access, and the responsible use of AI.