Learners' Decision-Making Processes in Title Generation Using Generative AI: Specification of Attributes and Context, and Convergence Toward "Appropriateness"

Abstract

　This study analyzes the decision-making process of title creation using generative AI, focusing on a poster production task within a computer science course for university students. In addition to the final documents, students submitted comprehensive worksheets recording their AI interaction logs, selection processes, and justifications for their choices. The analysis identified a four-stage deepening process of editorial judgment. It revealed that proactive learners intervened in AI’s suggestions by specifying target audiences, supplementing usage contexts, and imposing formal or emotional constraints. This demonstrated a process of converging AI outputs into a range of "appropriateness" envisioned by the students. These findings suggest that learners do not merely accept AI proposals but prioritize "editorial judgment" to adapt content to specific social and cultural contexts. This report details the mechanisms of student decision-making and the diverse context specifications provided. Furthermore, it discusses the necessity of evaluating the "process" of AI interaction in education and explores the establishment of new assessment frameworks, including the essential role of faculty feedback in fostering information literacy.