AI-Driven Dynamic Competency Graph Model for Bridging Industry-Academia Gaps in Software Engineering Education

The widespread adoption of artificial intelligence (AI) tools has fundamentally reshaped software engineering practices. According to GitHub's State of the Octobers’ report (2023), 92% of professional developers regularly employ AI-assisted coding tools, with code auto-completion reaching an adoption rate of 87.3% (M ± SD = 2.1). This study introduces the Dynamic Competency Graph Model (DCGM), a comprehensive, student-centered framework that integrates real-time technology monitoring, dynamic competency mapping, generative AI-driven personalized learning, and continuous evaluation. DCGM introduces a fully automated update mechanism that evolves educational content in parallel with industry advancements. A three-blind randomized controlled trial (TRCT) results indicate that students in the DCGM group achieved a 28.6% higher average salary (t = 4.32, df = 238, p < 0.001) and reduced the median time to master new frameworks from 14.3 days to 6.7 days (Mann-Whitney U = 1024, p = 0.003). The findings demonstrate that DCGM significantly shortens the curriculum-industry alignment cycle and enhances both employability and sustainable innovation. Future work will explore integrating federated learning to enable secure cross-institutional knowledge sharing.