Enhancing student understanding of artificial intelligence through practical neural network applications

Abstract

The teaching of artificial intelligence in higher education increasingly requires methods that bridge the gap between abstract theory and practical understanding. This article examines how the integration of practical neural network applications can significantly enhance student comprehension, engagement, and creativity in learning AI. By moving beyond traditional lecture-based instruction, practice-oriented approaches enable students to interact with data, construct and train neural models, and observe their outcomes in real time. Such experiential learning fosters deeper cognitive connections, critical thinking, and interdisciplinary collaboration. Through project-based tasks, visualization tools, and cloud-based platforms, students develop a more intuitive grasp of neural network concepts while gaining awareness of the ethical and societal implications of AI. The paper emphasizes that practical engagement not only improves technical proficiency but also cultivates reflective and responsible learners prepared for innovation in an AI-driven world. Ultimately, the integration of hands-on neural network experiences transforms AI education from theoretical exploration into active, meaningful participation in the creation of intelligent systems.