Leveraging Unstructured Teaching Data and AI for Enhanced Pedagogical Practices: A Data-Driven Approach

As data science and artificial intelligence (AI) gain traction in education, their effectiveness remains uneven, particularly in capturing student comprehension. While tools like facial expression analysis offer insights, they cannot replace the nuanced judgment of experienced teachers. This study leverages unstructured teaching data from nationally recognized teachers—combining audio and textual content—to enhance instructional practices. We utilize Hidden Markov Models (HMM) and the Jieba segmentation algorithm for speech and text processing, supported by bitmap, hash, BTREE, and inverted indexes for efficient data retrieval. Our approach highlights how AI can align with educational needs by uncovering implicit teaching patterns, promoting classroom reforms, and improving pedagogical strategies. This work underscores the importance of discipline-specific data applications to effectively integrate AI into teaching practices.