Optimization and Improvement Strategies of Blended Teaching in Management Information System Courses Empowered by AI

Abstract

The comprehensive integration of artificial intelligence technology is facilitating a paradigm shift in higher education from “digital assistance” to “intelligent reconstruction”. Management information system courses represent a critical intersection of management science and information technology. However, the traditional hybrid teaching model encounters structural challenges, including outdated content, superficial practical applications, limited evaluation metrics, and a lack of ethical education. This research draws upon disciplinary insights, integrating social cultural theory and distributed cognition theory to develop a theoretical framework centered on “cognitive spiral development”, with AI embedded within the four-dimensional integration of “teaching, learning, evaluation, and research”. Based on this framework, four primary optimization strategies are proposed: dynamic content generation and knowledge graph construction render course content dynamic; human-machine collaborative exploratory learning design deepens the practical process; multi-dimensional data-driven precise evaluation enhances learning feedback accuracy; and the integration of technology and ethics establishes a clear educational orientation. This research aims to offer a systematic solution that combines theoretical depth with practical applicability for the intelligent transformation of management information system courses empowered by AI, thereby providing an operational reference paradigm for cultivating interdisciplinary talent in the context of emerging business and engineering fields.