Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.19549v1</id>\n    <title>Plagiarism or Productivity? Students Moral Disengagement and Behavioral Intentions to Use ChatGPT in Academic Writing</title>\n    <updated>2026-03-20T01:21:34Z</updated>\n    <link href='https://arxiv.org/abs/2603.19549v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.19549v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>This study examined how moral disengagement influences Filipino college students' intention to use ChatGPT in academic writing. The model tested five mechanisms: moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame. These mechanisms were analyzed as predictors of attitudes, subjective norms, and perceived behavioral control, which then predicted behavioral intention. A total of 418 students with ChatGPT experience participated. The results showed that several moral disengagement mechanisms influenced students' attitudes and sense of control. Among the predictors, attribution of blame had the strongest influence, while attitudes had the highest impact on behavioral intention. The model explained more than half of the variation in intention. These results suggest that students often rely on institutional gaps and peer behavior to justify AI use. Many believe it is acceptable to use ChatGPT for learning or when rules are unclear. This shows a need for clear academic integrity policies, ethical guidance, and classroom support. The study also recognizes that intention-based models may not fully explain student behavior. Emotional factors, peer influence, and convenience can also affect decisions. The results provide useful insights for schools that aim to support responsible and informed AI use in higher education.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CY'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.ET'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='stat.AP'/>\n    <published>2026-03-20T01:21:34Z</published>\n    <arxiv:comment>5 pages, 1 figure, 2 table, conference proceeding</arxiv:comment>\n    <arxiv:primary_category term='cs.CY'/>\n    <arxiv:journal_ref>2025 International Workshop on Artificial Intelligence and Education (2026) 383-387</arxiv:journal_ref>\n    <author>\n      <name>John Paul P. Miranda</name>\n    </author>\n    <author>\n      <name>Rhiziel P. Manalese</name>\n    </author>\n    <author>\n      <name>Mark Anthony A. Castro</name>\n    </author>\n    <author>\n      <name>Renen Paul M. Viado</name>\n    </author>\n    <author>\n      <name>Vernon Grace M. Maniago</name>\n    </author>\n    <author>\n      <name>Rudante M. Galapon</name>\n    </author>\n    <author>\n      <name>Jovita G. Rivera</name>\n    </author>\n    <author>\n      <name>Amado B. Martinez</name>\n    </author>\n    <arxiv:doi>10.1109/WAIE67422.2025.11381217</arxiv:doi>\n    <link href='https://doi.org/10.1109/WAIE67422.2025.11381217' rel='related' title='doi'/>\n  </entry>"
}