化学学情诊断的传统路径与AI赋能对比研究

A Comparative Study of Traditional Approaches and AI Empowerment in Chemistry Learning Situation Diagnosis

摘要: 人工智能技术在教育领域正在不断地深入应用，学情诊断的方式正在从传统经验主导到信息技术赋能方面的改变。本文通过比较化学传统学情诊断与人工智能学情诊断在诊断方式、数据采集、应用效果等不同方面的差异，从各方面分析了传统方法存在的主观性、片面性问题，以及人工智能技术在实现精准、全面诊断方面所展现出的显著优势。另外，本文还探讨了人工智能学情诊断目前面临的算法歧视、隐私风险等挑战，并简单提出推动人机协同、加强人工智能算法等未来发展方向，以此为教育工作者能够有效整合两种诊断模式、优化教学决策科学性提供参考。

Abstract: Artificial intelligence technology is being increasingly and deeply applied in the field of education, and the approach to learning situation diagnosis is shifting from traditional experience-oriented methods to information technology-empowered ones. This paper compares traditional chemistry learning situation diagnosis with AI-based learning situation diagnosis across different dimensions, including diagnostic methods, data collection, and application effectiveness. It analyzes the subjectivity and one-sidedness inherent in traditional methods from various aspects, as well as the significant advantages demonstrated by AI technology in achieving precise and comprehensive diagnosis. Additionally, this paper explores the current challenges faced by AI-based learning situation diagnosis, such as algorithmic bias and privacy risks, and briefly proposes future development directions, including promoting human-machine collaboration and strengthening AI algorithms. The aim is to provide a reference for educators to effectively integrate these two diagnostic models and optimize the scientific nature of teaching decisions.