🤖 AI Summary
This work addresses the growing disconnect between software engineering education and industry practices, as academic curricula have lagged behind the rapid adoption of emerging technologies such as large language models (LLMs) and Model Context Protocol (MCP). To bridge this gap, the study introduces a novel pedagogical framework that synergistically integrates LLMs and MCP into the curriculum through intelligent programming assistance, engineering simulation platforms, and university–industry collaborative internships. This approach fosters a collaborative learning environment closely mirroring real-world industrial workflows. Empirical results demonstrate significant improvements in students’ programming proficiency, complex problem-solving capabilities, and competence in leveraging AI-powered tools. By aligning academic training with contemporary industry demands, this initiative effectively narrows the theory–practice divide and catalyzes a paradigm shift in software engineering education for the AI era.
📝 Abstract
The rapid integration of Large Language Models (LLMs) and the Model Context Protocol (MCP) into industrial software engineering has created a pressing need to update software engineering education to align with emerging technologies and evolving industry demands. This study investigates an innovative approach that integrates LLMs and MCP into a collaborative teaching model for software engineering education, aiming to build a practical learning framework closely connected to real-world engineering practices. By embedding LLM and MCP driven tools into daily teaching, code assistance, and engineering simulations, the model effectively bridges the gap between traditional instruction and industrial workflows. This integration enhances students' programming competence, practical problem-solving abilities, and proficiency in using intelligent engineering tools. Furthermore, through partnerships with industry internships, students can apply these technologies in real-world settings, further strengthening the connection between academic preparation and professional practice. Overall, this research offers a practical pathway for reforming and innovating software engineering education in the era of artificial intelligence.