用AI+教育理论构建可迭代的学习闭环，提升反馈质量与学习效果。
AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories
- 结合经典教育理论与人机协作AI，实现反思性、迭代式学习
- 学生二轮提交成绩显著优于首轮，自评与教师评分高度一致
- 适合关注AI辅助教学、教育公平与智能反馈系统的研究者
本研究提出人工智能-教育发展环（AI-EDL）框架，将经典学习理论与人机协同人工智能相结合，支持反思性、迭代式学习。该框架在EduAlly平台中实现，用于写作密集型与反馈敏感型任务，强调透明性、自我调节学习与教学监督。在一所综合性公立大学开展的混合方法研究评估了AI生成反馈、教师评价与学生自评的一致性；迭代修改对表现的影响；以及学生对AI反馈的感知。定量结果显示，第二轮提交成绩显著优于首轮，学生自评与最终教师评分高度一致。定性分析表明，学生认可AI反馈的即时性、具体性及成长机会。研究验证了基于发展理论、伦理一致且可扩展的AI反馈系统在提升学习成效方面的潜力。最后讨论了未来跨学科应用与智能教育技术优化的启示。
原文摘要 · Abstract (English)
This study introduces the AI-Educational Development Loop (AI-EDL), a theory-driven framework that integrates classical learning theories with human-in-the-loop artificial intelligence (AI) to support reflective, iterative learning. Implemented in EduAlly, an AI-assisted platform for writing-intensive and feedback-sensitive tasks, the framework emphasizes transparency, self-regulated learning, and pedagogical oversight. A mixed-methods study was piloted at a comprehensive public university to evaluate alignment between AI-generated feedback, instructor evaluations, and student self-assessments; the impact of iterative revision on performance; and student perceptions of AI feedback. Quantitative results demonstrated statistically significant improvement between first and second attempts, with agreement between student self-evaluations and final instructor grades. Qualitative findings indicated students valued immediacy, specificity, and opportunities for growth that AI feedback provided. These findings validate the potential to enhance student learning outcomes through developmentally grounded, ethically aligned, and scalable AI feedback systems. The study concludes with implications for future interdisciplinary applications and refinement of AI-supported educational technologies.
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