Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Case study: creating an ‘AI for Academic Writing Skills’ induction session for postgraduate life science courses

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Student use of generative artificial intelligence (GenAI) technology in university is as ubiquitous as it is controversial, and students and staff need guidance on how to use it effectively, responsibly, ethically and critically. In this case study, we present the conception, design and evaluation of a pilot ‘AI for Academic Writing Skills’ induction session designed for postgraduate taught (PGT) students in Medical Sciences. The induction session was designed by an institution-wide collaboration between Master of Science (MSc) programme directors, senior learning technologists and senior business technologists in AI competency to provide approximately 100 PGT students with structured, practical training on ethical GenAI use in academic writing. To ensure equitable access for all students in the pilot, this initiative integrated ChatGPT4 via a paid application programming interface into the institution’s virtual learning environment, Canvas, ensuring all the students had access to the latest version of the chatbot. Evaluation of the ‘AI for Academic Writing Skills’ induction session demonstrated that 100% of survey respondents rated the training positively, 86% found the academic writing lecture beneficial, and 100% found the lecture on general GenAI skills helpful. Furthermore, 82% appreciated interacting with the chatbot in group work, and 70% reported significantly reduced uncertainty about using GenAI in their academic work. This case study details our approach, which first surveyed students to assess current levels of engagement and confidence with GenAI tools. Based on these findings, we developed a scalable, evidence-based induction session on the ethical use of GenAI in academic writing. This report describes the process of creating this training, its impact on student confidence, and our reflections on how we will continue to refine the programme in future academic years. Keywords: academic writing, ethical use, generative AI, induction session, postgraduate taught

What kind of evidence is this?

Conversational & pedagogical agents · Survey / observational study

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The first phase focused on understanding students' existing confidence in academic writing and engagement with GenAI tools. We conducted a pilot study across four 1-year, in-person MSc programmes in Oxford’s Medical Sciences Division, in the final term of their taught programmes from June to August 2024, MSc cohorts 2023–24 (Figure 1). Over this period, students were conducting research projects and writing their dissertations.

Limitations and uncertainty · source passage

Interactive Assignment: Students worked in small groups on an interactive assignment using a university-approved chatbot in Canvas to write an essay outline and short introduction (less than 300 words). Students then completed a short reflection survey on their experience of this task, exploring prompt effectiveness and potential for bias.

Design the system before the pilot

Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Jennifer Carter; Anne Ferrey; Hubert Lam; Kelly Webb-Davies; Damion Young; Barbara Zonta; Delia O' Rourke. Case study: creating an ‘AI for Academic Writing Skills’ induction session for postgraduate life science courses. Emerging Topics in Life Sciences (2025-12-01). https://doi.org/10.1042/etls20253026

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.