AI-Driven Blended Teaching Innovation: Design and Evaluation of the “1235” Model in a Human Resource Management Course

With the rapid development of artificial intelligence (AI), blended teaching has become a pivotal paradigm for enhancing instructional quality in higher education. However, existing literature predominantly focuses on isolated AI tools or short-term learning performance, leaving a critical gap in systematic pedagogical frameworks and empirical evidence on how AI-driven blended teaching supports student-centered learning, particularly in large undergraduate classes. To address this gap, this study proposes an AI-driven blended teaching framework, termed the ‘1235’ model, which integrates student-centered development as its core, problem- and goal-oriented teaching as dual directions, three-dimensional learning objectives (knowledge, skills, and literacy), and five instructional integration paths (ideological education, online-offline modalities, knowledge-ability development, research-active learning, and practical application). Grounded in Kirkpatrick’s four-level training evaluation model, a quasi-experimental mixed-methods study was conducted in an undergraduate Human Resource Management course (N = 143) to compare the AI-driven model with traditional teacher-centered instruction. Quantitative results demonstrate that the experimental group achieved significantly higher levels of engagement, proactive learning behaviors, and academic performance (p < 0.01) with a substantial effect size (Cohen’s d = 0.69). Qualitative thematic analysis further reveals that the strategic integration of AI across pre-class, in-class, and post-class stages effectively supports personalized learning, self-regulation, and higher-order cognitive development. This study contributes to the educational technology literature by providing a model-based, mechanism-oriented explanation of AI-enabled instructional innovation and offers actionable guidelines for practitioners.

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