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

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.

Figures (5)

• Figure 1: AI tools used, among AI users ($n = 79$).
• Figure 2: Phases of AI use in the thesis workflow, among AI users ($n = 79$).
• Figure 3: Perceived quality improvement (7-point scale), among AI users ($n = 79$). Mean = 6.27; 78.5% selected 6 or 7 (95% CI: 67.8--86.9%).
• Figure 4: Perceived benefits and concerns among AI users ($n = 79$).
• Figure 5: GAMER PAT vs. other AI: respondent ratings on overall preference and two specific capabilities ($n = 35$). Asterisks mark dimensions where decisive pairwise preferences significantly favor GAMER PAT ($p < 0.05$, exact binomial test).