AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories
Ning Yu, Jie Zhang, Sandeep Mitra, Rebecca Smith, Adam Rich

TL;DR
This paper presents the AI-Educational Development Loop (AI-EDL), a framework integrating classical learning theories with AI to improve feedback and learning in educational platforms, validated through a pilot study showing positive student outcomes.
Contribution
Introduces the AI-EDL framework that combines educational theories with AI, and demonstrates its effectiveness in enhancing learning through a pilot implementation.
Findings
Significant improvement in student performance after iterative AI feedback.
High agreement between student self-assessments and instructor grades.
Students appreciated immediacy and specificity of AI feedback.
Abstract
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…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Explainable Artificial Intelligence (XAI)
