TITLE:
“Teacher-Student-Machine” Ternary Synergy: A Conceptual Framework for AI-Empowered Classroom Reform in Numerical Analysis Courses
AUTHORS:
Wenwen Xu, Xindong Li, Xiaoli Wang, Miaomiao Yang, Qing Guo, Sen Xu
KEYWORDS:
Numerical Analysis, AI-Empowered Education, Ternary Synergy, Conceptual Framework, Curriculum Reform
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.10,
October
13,
2026
ABSTRACT: The integration of artificial intelligence into higher education has become a strategic priority worldwide, with China’s Ministry of Education launching the National Digital Education Strategic Action 2.0 in 2025. Numerical Analysis, as a core course for Information and Computing Science majors, faces persistent challenges: highly abstract algorithms impose heavy cognitive loads, unidirectional classroom interaction limits personalization, and students struggle to bridge the gap from understanding algorithms to applying them effectively. This paper proposes a conceptual “Teacher-Student-Machine” ternary synergy framework for AI-empowered classroom reform in Numerical Analysis courses. The framework encompasses four interconnected dimensions: knowledge graph-based intelligent curriculum restructuring, ternary synergy teaching model construction, virtual-real integrated smart learning environment development, and multidimensional intelligent assessment systems. For each dimension, the paper specifies data inputs, AI outputs, teacher decision points, and the theoretical grounding that links proposed activities to intended learner behaviors. A governance subsection addresses student-data protection, AI error handling, academic integrity, and human review of consequential assessment decisions. This paper presents a conceptual implementation proposal rather than evaluated empirical results; the anticipated outcomes and planned indicators are intended to guide subsequent design-based research. The framework aims to provide a replicable model for AI-empowered foundational STEM course reform.