International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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CodeForesight: AI – Powered Learning and Coding Assistant

Author(s) Ms. Serene D'souza, Mr. Om Awari, Mr. Gaurav Kakad, Ms. Nikita Khuspe, Prof. Pratima Patil
Country India
Abstract The increasing complexity of cybersecurity concepts poses a challenge for learners and professionals who seek clear, concise, and visual explanations. This paper presents CodeForesight, an AI-powered learning and coding assistant designed to generate visual representations and theoretical explanations of cybersecurity topics based on user prompts. Traditional learning methods often lack interactivity and adaptability to individual learning styles, especially when dealing with technical subjects like cybersecurity. Existing AI tools provide text-based assistance but rarely offer simultaneous visual support tailored to the query context. CodeForesight addresses this gap by delivering customized visual aids, helping users grasp abstract security mechanisms more intuitively. This dual-modality approach aims to improve cognitive retention and foster a deeper conceptual understanding. The system leverages a fine-tuned LLama model to provide responses with added capabilities for diagrammatic output and natural language processing. We describe the model training pipeline, data sources, architectural design, and interface implementation. Results indicate high user satisfaction and meaningful learning enhancement through visual outputs. This research contributes to the field of educational AI by integrating generative models into cybersecurity pedagogy.
Keywords CodeForesight, Cybersecurity education, Generative AI, LLaMA model, Visual explanation, Educational assistant, Diagram generation, Natural language processing, Fine-tuned language model
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-23
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.45552
Short DOI https://doi.org/g9mnxm
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