人工智能时代下学习评价与学生自我调节学习：相互作用与影响

Learning Assessment and Students’ Self-Regulated Learning in the Era of Artificial Intelligence: Interactions and Impacts

摘要: 本文聚焦人工智能时代教育评价方式的转型，探讨学习评价与自我调节学习之间的作用机制。针对现有研究多侧重技术应用效果、缺乏对学习者内部调节过程系统分析的问题，本文以Zimmerman自我调节学习三阶段模型为理论基础，构建“人工智能学习评价–自我调节学习过程”的整合分析框架。在此基础上，系统分析了人工智能学习评价在目标设定、自我监控与反思调节等不同阶段中的作用路径，并从正负双重视角探讨其对学习过程的影响。研究表明，人工智能学习评价通过数据驱动的目标支持、实时反馈机制及个性化资源推荐，能够有效促进学习者自我调节能力的发展，但同时也可能带来自主性弱化、外部依赖增强等潜在风险。最后，本文从数据伦理、评价适配性及学习者技术能力等方面分析了现实挑战，并提出相应优化策略。研究结果为人工智能支持下学习评价与自我调节学习的协同发展提供了理论参考。

Abstract: This study investigates the interaction mechanism between learning assessment and students’ self-regulated learning (SRL) in the era of artificial intelligence (AI). Addressing the limitation of existing studies that primarily focus on technological effectiveness while overlooking learners’ internal regulation processes, this paper develops an integrated analytical framework of “AI-based learning assessment-SRL process” based on Zimmerman’s three-phase model. The study systematically examines the role of AI-driven assessment across the forethought, performance, and self-reflection phases, and analyzes its impact from both positive and negative perspectives. Findings indicate that AI-based assessment can effectively enhance SRL through data-driven goal setting, real-time feedback, and personalized resource recommendation, while also posing potential risks such as reduced learner autonomy and increased external dependence. Finally, this paper discusses practical challenges related to data privacy, assessment-regulation alignment, and learners’ technological adaptability, and proposes corresponding strategies. The findings provide theoretical support for the coordinated development of AI-supported assessment and self-regulated learning.