LC-IAF: Low-Code AI Framework for Image Analysis in the Context of Digital Learning Materials

This paper proposes a Low-Code Image Analysis Framework (LC-IAF) to address teachers' rules when analyzing images in learning materials. This work is time-consuming and subjective, while AI is too complex. LC-IAF bridges the gap between AI's image analysis capabilities and pedagogical needs. This framework analyzes deep learning and computer vision to extract attributes of images in digital learning materials, providing a low-code interface that allows teachers without programming expertise to analyze and develop image content in teaching materials. Based on visual learning theory, LC-IAF provides actionable insights, supports design guidance, personalized learning, and automated assessment, empowering teachers to improve visual teaching materials.