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.

翻译：本研究提出了人工智能-教育发展循环（AI-EDL），这是一个理论驱动的框架，旨在将经典学习理论与人在回路的人工智能相结合，以支持反思性、迭代式的学习。该框架在EduAlly——一个面向写作密集型与反馈敏感型任务的人工智能辅助平台——中得以实现，强调透明度、自我调节学习及教学监督。在一所综合性公立大学开展的混合方法试点研究中，评估了人工智能生成反馈、教师评价与学生自我评估之间的一致性、迭代修改对表现的影响，以及学生对人工智能反馈的感知。定量结果显示，首次与第二次尝试之间存在统计学上的显著改善，且学生自我评估与教师最终评分具有一致性。定性研究发现，学生重视人工智能反馈所提供的即时性、具体性及成长机会。这些发现验证了通过基于发展理论、符合伦理规范且可扩展的人工智能反馈系统提升学生学习成果的潜力。研究最后探讨了该框架对未来跨学科应用及人工智能支持的教育技术优化的启示。