Teaching Software Engineering with LLM and MCP Integration: From Classroom to Industry Practice.
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. Index Terms-Large Language Models, Model Context Protocol, Software Engineering Education I. I NTRODUCTION Large Language Models (LLMs) have become fundamental auxiliary tools in modern software engineering, supporting code generation, debugging, and technical documentation [1]. However, standalone LLM applications face inherent limitations including context fragmentation, tool isolation, and untraceable generation results, which restrict their industrial deployment. As a newly proposed industrial standard, Model Context Protocol (MCP) provides a standardized Host-ClientServer three-tier architecture to unify context generation, encapsulation, distribution, and tool collaboration throughout the software development lifecycle [2] [3]. It effectively solves tool heterogeneity and context inconsistency problems in LLM intelligent development. Despite its advantages, practical industrial adoption of MCP still faces numerous unresolved challenges in real-