Year: 2025
Type: article
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.
Authors: Serene D'souza, Om Awari, Gaurav Kakad, Nikita Khuspe, Pratima Patil
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4410852116