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Shaping responsible GenAI use in research through AI literacy-oriented guidelines: insights from postgraduate students
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DOI：10.1186/s41239-026-00609-6.png)
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Abstract
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The widespread adoption of generative Artificial Intelligence (GenAI) among researchers has intensified the need for guidelines that safeguard academic integrity and ethical scholarly practice. However, existing GenAI policies tend to focus on teaching, learning and assessment rather than on research, where ethical concerns are considerably more complex. Given this complexity, such guidelines should move beyond rule enforcement to scaffold the AI literacy researchers need when deciding how, when, and whether to use GenAI across the full research process. To address this gap, this study conducted seven focus groups with 28 postgraduate research (PGR) students, who are among the most active adopters of GenAI tools in academic work. It examined how they used GenAI in research, how they interpreted ethically acceptable practice, what concerns they hold, and what institutional support they expected. Drawing on a four-dimensional AI literacy framework, the study analysed PGR students’ practices and perceptions through thematic analysis to examine how each dimension of AI literacy was reflected within the research setting. The findings showed that students’ awareness of GenAI’s capabilities, limitations, and risks corresponded to the “Know & Understand AI” dimension, while their diverse, discipline-specific applications of GenAI across the research workflow reflected the “Use & Apply AI” dimension. Their nuanced boundaries between ethical and unethical use demonstrated the “Evaluate & Create AI” dimension, and concerns about output accuracy, originality, data privacy, and skill degradation highlighted the “AI Ethics” dimension. Building on PGR students’ research practices and support expectations, the study proposed researcher-oriented GenAI guidelines that foreground each AI literacy dimension across diverse research tasks, with the aim of informing more applicable policy and sustaining researchers’ AI literacy development.
Keywords:
AI policy
AI literacy
Postgraduate research
Generative AI
Research integrity
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