Shaping Responsible GenAI Use in Research Through AI Literacy-Oriented Guidelines: Insights From Postgraduate Students

Wei Dai and Cecilia K. Y. Chan (2026) examined how 28 postgraduate research (PGR) students across seven focus groups enacted AI Literacy in their use of generative AI for research, arguing that responsible-use guidance must extend beyond teaching and assessment into the ethically more complex research process. Analysing practices through a four-dimensional AI literacy framework, the study found students' awareness of GenAI's capabilities, limitations, and risks reflected the "Know & Understand" dimension; their diverse, discipline-specific applications across the research workflow reflected "Use & Apply"; their self-defined boundaries between ethical and unethical use reflected "Evaluate & Create"; and concerns about output accuracy, originality, data privacy, and skill degradation reflected the "AI Ethics" dimension. Building on these insights, the authors proposed researcher-oriented GenAI guidelines that foreground each AI literacy dimension across research tasks, positioning the guidelines less as rule enforcement and more as a developmental scaffold for researchers' sustained AI literacy growth.

Key Findings

• Researchers are active, calibrated users, not passive adopters: 27 of 28 participants used GenAI across ideation, literature review, explanation, data processing, programming, academic writing, editing, and translation, but matched tools to tasks based on perceived stakes, intellectual demands, and disciplinary norms — heavier use in low-stakes procedural work and more cautious engagement in tasks central to scholarly contribution.
• Human oversight retained throughout: GenAI was positioned as a supportive, efficiency-enhancing research assistant rather than a substitute for core intellectual work; participants did not adopt AI-generated outputs uncritically, recognising that ideas could be "misleading" or "totally wrong."
• Nuanced boundary judgements on writing: Students distinguished between acceptable drafting assistance and full generation in academic writing, and between AI helping to structure versus substitute their own reasoning.
• AI ethics concerns cluster around scholarly integrity: Worries centred on non-deterministic behaviour, limited capacity for genuine originality, output accuracy, data privacy, and the degradation of researchers' own scholarly competence — concerns that map to the "AI Ethics" dimension.
• Policy gap: Existing institutional GenAI policies focus on teaching, learning, and assessment and remain abstract; research-specific guidance that is practical, task-sensitive, and grounded in researchers' lived experience is largely absent.

Implications for AI in Education

The study contributes an empirical account of how AI Literacy is enacted as a situated capacity in research practice, rather than a static set of competencies, with direct implications for integrity and Ethics policy in graduate education. For institutions it argues that responsible GenAI-use guidelines should move beyond binary rules toward practice-oriented support that scaffolds each AI literacy dimension across the research workflow — a stance that treats Governance and researcher development as mutually reinforcing. For supervisors and research-training programmes it signals a need to cultivate evaluative judgment and self-regulation of GenAI use, echoing calls to embed AI literacy across the entire scholarly lifecycle rather than only in teaching and assessment contexts.

Connected Concepts

• AI Literacy
• Academic Integrity
• Ethics
• Higher Ed
• Generative AI
• Governance
• Self Regulated Learning
• LLM

Connected Articles

• Student Centered GenAI Responsible Framework 2026 — A Student-Centered Framework for Responsible Use of Generative AI in Higher Education
• Ssaho AI Academic Integrity Review 2025 — Reassessing Academic Integrity in the Age of AI
• Pedlow GenAI Selfassessment 2026 — Raising Ethical Awareness of GenAI Use Through Student Self-Assessment
• Taylor Lacroix Purpose Before Policy Academic Integrity 2026 — Purpose Before Policy: Academic Integrity, Generative AI, and Rhetoric

Citation

Dai, W., & Chan, C. K. Y. (2026). Shaping responsible GenAI use in research through AI literacy-oriented guidelines: Insights from postgraduate students. International Journal of Educational Technology in Higher Education, 23, 33.