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

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.

Authors

Institutions

Publication Details

Journal
Data & Metadata
Published
2025-02-10
DOI
https://doi.org/10.56294/dm2025203
Citations
4
Primary Topic
Diverse Approaches in Healthcare and Education Studies
Type
article
Field-Weighted Citation Impact
7.51
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Rubén González Vallejo, Ranta Butarbutar
4 citations
Data & Metadata
Diverse Approaches in Healthcare and Education Studies
7.51
article

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

Rubén González Vallejo, Ranta Butarbutar
article en
4 citations

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.

Data & MetadataVol. 4
Musamus Merauke University (ID), Universidad de Málaga (ES)
Openalex Percentile: Top 3%
Diverse Approaches in Healthcare and Education Studies
7.51
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.