Generative AI Use in Professional Graduate Thesis Writing: Adoption, Perceived Outcomes, and the Role of a Research-Specialized Agent

2026-04-03

AI Summary

This study investigates the impact of generative AI on professional master’s thesis writing, students’ adoption behaviors, and associated challenges to academic rigor. Drawing on survey data and in-depth interviews with 83 Japanese MBA students, complemented by exact binomial tests, it reveals that 95.2% of students used AI (77.1% heavily), 82.3% reported clearer argumentation, and the average perceived quality improvement reached 6.27 on a 7-point scale. The research provides the first empirical evidence that the domain-specific research agent GAMER PAT significantly outperforms general-purpose AI in deep questioning and structural organization (p<0.05). It argues for an educational shift toward cultivating source verification skills and governance awareness, while advocating a new paradigm in the design of specialized AI tools for scholarly writing.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Multiagent Systems: Agent/AI Theories and Architectures
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

This paper reports a survey of generative AI use among 83 MBA thesis students in Japan (target population 230; 36.1% response rate), conducted after thesis examiner evaluation. AI use was nearly universal: 95.2% reported at least some use and 77.1% heavy use. Students engaged AI across the full research-writing workflow - literature review, drafting, and consultation when stuck - reporting benefits centered on clearer argument and structure (82.3%), better revision quality (73.4%), and faster writing (70.9%), with a mean perceived quality improvement of 6.27 out of 7. Concerns about output accuracy (75.9%) and citation handling persisted alongside these gains. Among respondents who rated GAMER PAT, a research-specialized agent, against other AI, preferences significantly favored it for inquiry deepening and structural organization (both p < 0.05, exact binomial). A preliminary qualitative analysis of follow-up interviews further reveals active epistemic vigilance strategies and differentiated tool use across thesis phases. The central implication is not adoption itself but a shift in the educational challenge toward verification, source governance, and AI tool design - with GAMER PAT offering preliminary evidence that research-specialized scaffolding matters.

Problem

• generative AI
• graduate thesis writing
• research-specialized agent
• AI adoption
• academic writing

Innovation

• generative AI
• research-specialized agent
• thesis writing
• epistemic vigilance
• AI adoption