人工智能与初中数学教育研究：回顾与展望——基于中国知网(2016~2025年)期刊与学位论文分析

Research on Artificial Intelligence and Junior High School Mathematics Education: A Review and Outlook—Based on the Analysis of Journal Articles and Dissertations from China National Knowledge Infrastructure (CNKI) from 2016 to 2025

摘要: 系统梳理2016~2025年人工智能与初中数学教育融合研究的现状、热点与趋势，为后续人工智能赋能初中数学教学实践、教学模式优化及实证研究提供参考。以中国知网(CNKI)相关论文为样本，采用文献计量法和内容分析法，从发文趋势、合作网络、关键词演化及研究主题等方面进行分析。该领域经历“萌芽探索–缓慢增长–快速发展”三个阶段，2025年增长明显；研究热点由“大数据”“精准教学”转向教学方式创新、人机协同、个性化学习与核心素养培养，内容集中于精准教学与诊断评价、个性化学习路径、课堂人机协同与教学方式创新、教师专业发展与教学优化、核心素养导向的跨学科项目式学习。现有研究仍存在深层融合不足、实证效度有限、伦理风险关注不够和教师转型支撑不足等问题。未来应加强微观机制研究，完善多元评价，重视算法公平与教育包容性，提升教师在人机协同中的专业判断力。

Abstract: This study systematically reviews the current status, research hotspots, and development trends of the integration of artificial intelligence and junior high school mathematics education from 2016 to 2025, aiming to provide references for subsequent teaching practice, instructional model optimization, and empirical research on AI-empowered junior high school mathematics education. Taking relevant papers indexed in the China National Knowledge Infrastructure (CNKI) as the research sample, this study adopts bibliometric analysis and content analysis to examine publication trends, collaboration networks, keyword evolution, and major research themes. The results show that this field has gone through three stages: initial exploration, slow growth, and rapid development, with a marked increase in publications in 2025. Research hotspots have shifted from “big data” and “precision teaching” to instructional innovation, human-AI collaboration, personalized learning, and the cultivation of core competencies. The existing literature mainly focuses on precision teaching and diagnostic evaluation, personalized learning paths, classroom-based human-AI collaboration and instructional innovation, teacher professional development and teaching optimization, and interdisciplinary project-based learning oriented toward core competencies. However, several problems remain, including insufficient deep integration, limited validity of empirical research, inadequate attention to ethical risks, and insufficient support for teachers’ role transformation. Future research should further explore micro-level mechanisms, improve diversified evaluation systems, emphasize algorithmic fairness and educational inclusiveness, and enhance teachers’ professional judgment in human-AI collaboration.