Factors Influencing AI-Assisted Thesis Writing in University: A Pull-Push-Mooring Theory Narrative Inquiry Study

Ranta Butarbutar and Rubén González Vallejo

Data and Metadata, 2025, vol. 4, 203

Abstract: This study aims to examine the factors that motivate, attract, and anchor students to adopt AI tools during the writing process in the context of push-pull-mooring (PPM) theory. Utilizing a narrative inquiry research approach, this study employed observation, in-depth interviews, and document analysis for data collection. The analysis identified the key factors through reflexive thematic methods. Key pull factors include the generation of credit authorship contributions and the integration of AI into academic writing. The pull factors encompass topic selection, dynamic literature review, research questions, proposal conceptualization, designing research methods, data analysis, revising drafts, and managing references. AI integration incorporates active learning, self-regulated learning (SRL), inquiry-based learning, and overcoming linguistic challenges. The push factors identified include reference inaccuracies, confidentiality of research, and overreliance on AI. Three anchoring principles guide the ethical incorporation of AI in thesis writing: institutional academic policies, AI augmentation, and comprehensive contextual learning approach. But the study's limitations include the small sample size of ten students from a single university, which affects the generalizability of the results.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:203:id:1056294dm2025203

DOI: 10.56294/dm2025203

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