Vol. 5 No. 2 (2026): April
RESEARCH ARTICLES

Real-Time Syntactic, Semantic, and Logical Error Detection Using AI in Multilanguage Code Editors

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  • Thasni Asharaf,
  • Thanusri S,
  • Febin K J,
  • Santhosh S,
  • Sanjay C
Thasni Asharaf
Department of Computer Science and Design, SNS College of Technology, Coimbatore, Tamil Nadu, India.
Thanusri S
Department of Computer Science and Design, SNS College of Engineering, Coimbatore, Tamil Nadu, India
Febin K J
Department of Computer Science and Design, SNS College of Engineering, Coimbatore, Tamil Nadu, India
Santhosh S
Department of Computer Science and Design, SNS College of Engineering, Coimbatore, Tamil Nadu, India
Sanjay C
Department of Computer Science and Design, SNS College of Engineering, Coimbatore, Tamil Nadu, India
DOI: https://doi.org/10.5281/zenodo.18497113

Published 2026-02-05

Keywords

  • AI Code Editor,
  • Real-time Debugging,
  • Syntax & Logic Detection,
  • Machine Learning,
  • Error Explanation,
  • Code Optimization
    • ...More
    Less

How to Cite

Thasni Asharaf, Thanusri S, Febin K J, Santhosh S, & Sanjay C. (2026). Real-Time Syntactic, Semantic, and Logical Error Detection Using AI in Multilanguage Code Editors. International Journal of Computational Learning & Intelligence, 5(2), 981–988. https://doi.org/10.5281/zenodo.18497113

Copyright (c) 2026 Thasni Asharaf, Thanusri S, Febin K J, Santhosh S, Sanjay C

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The rapid growth of programming technology has made debugging and understanding code increasingly challenging for students and new developers. Traditional IDEs only highlight errors without context, forcing learners to search online or ask others for help, which slows learning and reduces productivity. This project aims to build an AI-powered code editor that identifies syntactic, logical, and semantic errors in real time while pinpointing the exact location of issues. It provides simple explanations, suggests fixes, predicts runtime behavior, and analyzes code quality using machine learning and NLP. Supporting multiple languages like Python, Java, C, and JavaScript, the system learns continuously from student error datasets to improve accuracy. With features like intelligent syntax highlighting, style feedback, and optimization suggestions, the AI editor enhances understanding, speeds up debugging, promotes self-directed learning, and ultimately transforms programming education into a more intuitive and efficient experience
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