International Journal of Science, Engineering and TechnologyAuthors: Khushi Bhandari, Shriraj Uday Shetty, Dr. Jasbir Kaur, Sandhya Thakkar, Ifrah Kampooh
Abstract: Generative AI and professional canvas editing remain largely isolated, forcing creators to juggle multiple tools and lose the benefits of integrated intelligence. We present a full-stack Design Assistant that tightly couples a prompt-expansion engine (GPT-4), a generative image service (DALL·E 3), vision-based asset tagging, and a Fabric.js object-based canvas. Unlike existing platforms that offer only fragmented AI features, our system provides end-to-end prompt refinement, secure back-end AI orchestration, and lossless JSON canvas persistence. We formalise prompt expansion as a structured optimisation that maximises relevance to the user’s intent, and we implement a STRIDE-based threat model to ensure confidentiality and integrity of user assets. In a controlled user study (20 participants), the system improved image relevance from 72% to 94% (Likert scale), reduced task completion time by 38%, and achieved a System Usability Scale (SUS) score of 78.4 (“Good”). We critically compare our approach with commercial AI-design tools (Canva Magic Studio, Adobe Firefly, Figma AI) and openly discuss limitations regarding sample size, API dependency, and scalability. The full architecture, security measures, and evaluation metrics are described in detail, providing a reproducible baseline for future human-AI collaborative design research.
DOI: http://doi.org/10.5281/zenodo.20588710
