Abstract
To address the need for comprehensive terminology construction in rapidly evolving domains such as blockchain, this study examines how large language models (LLMs), particularly GPT, enhance automatic term extraction through human feedback. The experimental part involves 60 bachelor’s students interacting with GPT in a three-step iterative prompting process: initial prompt formulation, intermediate refinement, and final adjustment. At each step, the students’ prompts are evaluated by a teacher using a structured rubric based on 6C criteria (clarity, complexity, coherence, creativity, consistency, and contextuality), with their summed scores forming an overall grade. The analysis indicates that (1) students’ overall grades correlate with GPT’s performance across all steps, reaching the highest correlation (0.87) at Step 3; (2) the importance of rubric criteria varies across steps, e.g., clarity and creativity are the most crucial initially, while complexity, coherence and consistency influence subsequent refinements, with contextuality having no effect at all steps; and (3) the linguistic accuracy of prompt formulations significantly outweighs domain-specific factual content in influencing GPT’s performance. These findings suggest GPT has a robust foundational understanding of blockchain terminology, making clear, consistent, and linguistically structured prompts more effective than contextual domain-specific explanations for automatic term extraction.