AI-Driven Stylistic Analysis of PLC Programming Content for Industrial Automation Education

Artificial intelligence (AI) is increasingly being deployed in industrial automation education to enhance the comprehension and delivery of programming content for programmable logic controllers (PLCs), which remain foundational to modern factory systems. However, the rapid growth of PLC-based instructional materials often varies widely in style, complexity, and pedagogy, creating inconsistencies that hinder student engagement, knowledge retention, and adaptability across diverse learning environments. Traditional curriculum designs rarely account for stylistic alignment between instructional text and learner needs, limiting the effectiveness of teaching PLC logic and programming structures. To address this problem, the paper propose an AI-Driven Stylistic Profiling and Adaptation Model (AISPAM), which combines natural language processing (NLP), transformer-based text representation, and unsupervised clustering to analyze and adapt PLC programming content according to stylistic markers such as technical density, instructional clarity, cognitive load, and domain-specific readability. AISPAM classifies content into pedagogical clusters, aligning instructional material style with targeted learner categories ranging from novice technicians to advanced engineering students. Experimental validation on a curated dataset of PLC programming manuals, learning modules, and industrial training documents demonstrated that AISPAM achieved over 87 % accuracy in stylistic clustering, while adaptive transformation improved student comprehension scores by 22 % in test scenarios. These findings confirm that AI-based stylistic analysis can effectively bridge the gap between technical rigor and adaptive pedagogy, providing a robust framework for developing dynamic, learner-centered curricula in automation education. The work highlights future potential for integrating stylistic adaptability into smart industrial training platforms and intelligent tutoring systems for lifelong learning.

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