Reform and Practice of an AI-Driven Discrete Mathematics Teaching Model from the Perspective of the OBE Concept

Abstract

As a core foundational course for computer-related disciplines, reforming the teaching model of Discrete Mathematics is critically important. Driven by the Outcomes-Based Education (OBE) concept and the national “Artificial Intelligence (AI) + Education” strategy, there is a compelling need to transform traditional pedagogical approaches towards data-intelligence. Leveraging the OBE framework and AI technologies, this study focuses on the teaching reform of Discrete Mathematics. It constructs a “Data-Driven–Cognitive Computing–Precise Intervention” theoretical framework and establishes three progressive objectives: (1) developing an AI-assisted integrated framework for “learning-oriented teaching”; (2) designing personalized teaching strategies; and (3) innovating precision intervention models. The research specifically addresses four key issues: dynamic analysis of learning states, generation of personalized strategies, human-computer collaborative mechanisms, and an effect evaluation system. Empirical evidence demonstrates that the proposed AI-driven OBE teaching model significantly enhances instructional precision, improves student competency outcomes, and promotes the achievement of course objectives. This study provides substantial theoretical and practical support for the digital transformation of Discrete Mathematics instruction and the broader intelligent teaching reform initiative in higher education.