Plagiarism or Productivity? Students Moral Disengagement and Behavioral Intentions to Use ChatGPT in Academic Writing

• URL: http://arxiv.org/abs/2603.19549v1
• Date: Fri, 20 Mar 2026 01:21:34 GMT
• Title: Plagiarism or Productivity? Students Moral Disengagement and Behavioral Intentions to Use ChatGPT in Academic Writing
• Abstract summary: This study examined how moral disengagement influences Filipino college students' intention to use ChatGPT in academic writing.<n>The model tested five mechanisms: moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame.<n>Results suggest students often rely on institutional gaps and peer behavior to justify AI use.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models [56.88565331713778]
  reasoning-oriented training can make traces look more deliberative without amplifying the behaviors most tied to model correctness.<n>We quantify this mismatch with Behavioral Lift, a metric that measures how much correctness changes when a behavior is present versus absent in a model's reasoning trace.<n>We find evidence for an Amplification-Lift Gap, in which thinking models strongly amplify self-awareness, hypothesis testing, and uncertainty acknowledgment.
  arXiv  Detail & Related papers  (2026-08-13T20:37:59Z)
• Warning labels shift perceptions of sycophantic AI, but not its influence [63.79516339973759]
  We test whether warning labels mitigate sycophancy's influence.<n>Labeling the system as sycophantic (...may agree with you and validate you even when you are wrong...'') does shift users' perceptions.<n>But it does not reliably reduce sycophancy's influence on users' self-perceived rightness or their willingness to repair the conflict.
  arXiv  Detail & Related papers  (2026-06-19T11:01:45Z)
• "Stuck in a Spiral": Shame and Guilt as Social Regulators of AI Use in Computing Education [0.20524609401792393]
  We present an interview study with 19 computing students through a functionalist perspective of shame and guilt.<n>Students described shaming themselves, their peers, and even faculty for using AI.<n>Results highlight the need to consider the socio-emotional aspects of AI use, which may be influenced by how AI policies are implemented and enforced.
  arXiv  Detail & Related papers  (2026-06-12T19:55:52Z)
• The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs [60.15472325639723]
  Personality traits have long been studied as predictors of human behavior.<n>Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems.
  arXiv  Detail & Related papers  (2025-09-03T21:27:10Z)
• Investigating VLM Hallucination from a Cognitive Psychology Perspective: A First Step Toward Interpretation with Intriguing Observations [60.63340688538124]
  Hallucination is a long-standing problem that has been actively investigated in Vision-Language Models (VLMs)<n>Existing research commonly attributes hallucinations to technical limitations or sycophancy bias, where the latter means the models tend to generate incorrect answers to align with user expectations.<n>In this work, we introduce a psychological taxonomy, categorizing VLMs' cognitive biases that lead to hallucinations, including sycophancy, logical inconsistency, and a newly identified VLMs behaviour: appeal to authority.
  arXiv  Detail & Related papers  (2025-07-03T19:03:16Z)
• Kernels of Selfhood: GPT-4o shows humanlike patterns of cognitive consistency moderated by free choice [0.5277756703318045]
  We show that GPT-4o exhibits patterns of attitude change mimicking cognitive consistency effects in humans.<n>This result suggests that GPT-4o manifests a functional analog of humanlike selfhood.
  arXiv  Detail & Related papers  (2025-01-27T02:25:12Z)
• AgentPeerTalk: Empowering Students through Agentic-AI-Driven Discernment of Bullying and Joking in Peer Interactions in Schools [0.0]
  This study examined the potential of large language models (LLMs) to empower students by discerning between bullying and joking in school peer interactions. ChatGPT-4 excelled in context-specific accuracy after implementing the agentic approach.
  arXiv  Detail & Related papers  (2024-07-27T05:50:02Z)
• MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks [49.60689355674541]
  A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments. We test whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with human participants.
  arXiv  Detail & Related papers  (2023-10-30T15:57:32Z)
• Moral Sparks in Social Media Narratives [14.025768295979184]
  We examine interactions on social media to understand human moral judgments in real-life ethical scenarios. Specifically, we examine posts from a popular Reddit subreddit (i.e., a subcommunity) called r/AmITheAsshole.
  arXiv  Detail & Related papers  (2023-10-30T05:03:26Z)
• AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays [66.36541161082856]
  ChatGPT and similar generative AI models have attracted hundreds of millions of users. This study compares human-written versus ChatGPT-generated argumentative student essays.
  arXiv  Detail & Related papers  (2023-04-24T12:58:28Z)
• Consistency Analysis of ChatGPT [65.268245109828]
  This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding logically consistent behaviour. Our findings suggest that while both models appear to show an enhanced language understanding and reasoning ability, they still frequently fall short of generating logically consistent predictions.
  arXiv  Detail & Related papers  (2023-03-11T01:19:01Z)
• Moral reinforcement learning using actual causation [0.0]
  We propose an online reinforcement learning method that learns a policy under the constraint that the agent should not be the cause of harm. This is accomplished by defining cause using the theory of actual causation and assigning blame to the agent when its actions are the actual cause of an undesirable outcome.
  arXiv  Detail & Related papers  (2022-05-17T09:25:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.