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📄 Research Article

Is It Ethical for Teachers to Use AI for Student Feedback?

◐ medium · 2026-08-20 · feedback, ai-feedback-quality, ethics, teacher-role, generative-ai, higher-ed

Synthesis: Luo and Eaton (2026) examine whether it is ethical for teachers to use AI to provide Feedback to students, an important yet time-consuming part of teachers' work now increasingly automatable through Generative AI. Addressing a gap in empirical research on the ethical implications of this practice, the authors draw on a diverse dataset — university AI policy reviews from 50 leading institutions, Reddit social media posts, and interviews with university teachers and students in Southern China — to extrapolate eight key areas of ethical consideration for teachers' use of AI for feedback. Rather than offering a simple binary answer, they call for a nuanced understanding that weighs contextual complexities such as assessment purposes, assignment types, and the kinds of feedback being automated, and emphasize designing AI-enabled feedback activities that preserve the care, trust, and human connections central to effective feedback processes in higher education.

Key Findings

  • There is no simple binary answer to whether it is ethical for teachers to use AI for student feedback; ethical use depends on multiple contextual factors including how AI use is communicated, assessment purpose, assignment type, student privacy, and teacher-student trust.
  • The study identifies eight key areas of ethical consideration: (1) professionalism (AI use may conflict with teachers' professional responsibilities and erode professional identity and critical-evaluation skills), (2) accountability (teachers must remain answerable for and exercise human oversight over AI-generated feedback), (3) transparency (teachers should clearly communicate how AI is used and obtain student consent before uploading work), (4) effectiveness (impacts on teacher-student relationships, feedback quality, and teachers' instructional insight), (5) inclusivity (systemic AI bias, uneven application across students, but also potential to soften feedback language), (6) security (use of secure AI platforms and protection of student data and intellectual property), (7) contextual (assessment purpose, assignment complexity, and feedback type shape ethicality), and (8) policy and resource (alignment with institutional governance, access to secure platforms, and training).
  • Transparency and consent emerged as central: eight universities advised transparency about AI use, four required student consent, and informed consent itself carries workload costs, leading some to propose "passive consent" (opt-out) models.
  • Teacher-student trust and relationships are a recurring concern, with students expressing that AI-generated feedback can feel "disingenuous," eroding the trust essential to meaningful feedback uptake.
  • Feedback as a relational practice: the authors frame feedback not as one-way information transfer but as a dialogic, relational practice, arguing effective feedback relies on human connections that AI may struggle to replicate.
  • Policy gaps: only 14 of 50 top universities had specific guidance on teachers' AI use for feedback, and interviewed teachers reported little awareness of relevant internal policies — signalling a need for clearer governance and teacher training.

Study Design & Method

The study addresses the research question "What are the key ethical considerations for teachers when using AI to provide students with feedback?" using four datasets: (1) institutional AI policies and guidelines from 50 leading universities (2025 QS rankings), searched Jan–Feb 2025, with only 14 found to have specific feedback-related guidance; (2) Reddit social media posts — three topic posts on teachers' AI use for feedback (including one with 791 responses), yielding 76 analysed responses; (3) interviews with 33 teachers; and (4) interviews with 23 students from Southern China institutions, where "AI+Education" national policy encourages AI integration. Purposive sampling ensured diverse disciplines. Drawing on Mahony's (2009) definition of Ethics as "what we ought to do," the analysis used open coding in NVivo, aggregating meaning clusters into eight overarching themes that were triangulated across datasets (all themes supported by at least two data sources; half by all four). Ethical approval was obtained (Approval Nos. 2023-2024-0134 and 2024-2025-0174).

Implications for AI in Education

The study reframes the question of AI use in Feedback from whether teachers should use AI to how AI-enabled feedback activities can be designed and operated to maintain — or even enhance — care, trust, and human connections. It shifts the focus of AI-use debates in higher education from students' use of AI to teachers' own professional use, showing that teachers who scrutinize students' AI use are often less reflective about the Ethics of their own. The findings support the development of institutional policies and professional development around AI for feedback, promoting transparency, accountability, human oversight, secure platform use, and context-sensitive judgment. Ethical AI use requires a collective effort from multiple stakeholders in and beyond higher education, and the authors connect it to the broader need for AI Literacy among educators. The CC BY-ND (no derivatives) licence of the paper should be noted when adapting its materials.

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Citation

Luo, J., & Eaton, S. E. (2026). Is It Ethical for Teachers to Use AI for Student Feedback?. Journal of University Teaching and Learning Practice. https://doi.org/10.53761/887m5346. CC BY-ND 4.0.