AN ARTIFICIAL INTELLIGENCE-BASED FRAMEWORK FOR DEVELOPING STUDENTS’ DIGITAL COMPETENCE IN NETWORK TECHNOLOGIES EDUCATION
Keywords:
Artificial intelligence, digital competence, network technologies, adaptive learning, virtual laboratory, learning analytics, intelligent feedback.Abstract
This study proposes an artificial intelligence-based framework for developing students’ digital competence in Network Technologies education. The framework integrates diagnostic assessment, adaptive learning, virtual laboratories, intelligent feedback, learning analytics, and competency-based evaluation. Students’ digital competence was assessed through theoretical knowledge, network configuration skills, cybersecurity awareness, problem-solving ability, independent learning, and responsible use of artificial intelligence tools. A quasi-experimental design was applied to compare traditional instruction with an AI-supported learning environment. The results indicated that the experimental group demonstrated higher achievement, fewer configuration errors, greater independence, and stronger problem-solving performance. The proposed framework supports personalized learning and enables instructors to monitor students’ progress through continuous data analysis.
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