InspireTrace: A Generative AI System for Creative Scaffolding with Source Attribution

Abstract

The rapid integration of generative AI into education, industry, and everyday creative practices has introduced significant efficiencies, and yet it has also raised ethical concerns around authenticity, originality, and the erosion of human creativity. Many generative AI systems prioritize speed and content production over user development. Simply delivering synthesized outputs tends to limit opportunities for users to focus on the creative process and creative thinking. Additionally, users might unintentionally plagiarize others’ ideas from the synthesized output from AI systems without realizing it. In this paper, we present InspireTrace, an early-stage generative AI prototype designed to support creativity through guided inspiration rather than direct solution, intentionally leaving synthesis and ideation to the user. By explicitly attributing source materials, InspireTrace aims to reduce plagiarism risks and foster a healthier, more trustworthy creativity ecosystem. We discuss the system’s design rationale and its implications fro creative practice in human-AI collaboration.