Agentic education · Learning science

Actionable companion · Learning design & outcomes

From AI evidence to responsible practice: Developing an AI framework for learning in higher education: a humanities perspective from English Literature

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings 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 participants, intervention and comparator before applying this study in a new setting.

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

Abstract Little is known about how to develop frameworks for using AI to support learning in English Literature within higher education. This paper presents research that developed a framework for employing AI in the study of the fourteenth-century English poet Geoffrey Chaucer. Studying Chaucer presents challenges for modern readers, as it requires an understanding of his literary style, spoken Middle English, and the cultural and social context of the late medieval period. The research investigated how AI tools, such as ChatGPT and Amazon Polly, could support the study of Chaucer. It found that while AI can assist with students’ initial familiarisation with Chaucer’s poetry, it lacks the depth and rigour necessary for more advanced study. As a result, students and lecturers still rely on traditional resources, including authoritative print-based and digital scholarly editions. Consequently, the research developed a framework for studying Chaucer's poetry that integrates the guided use of AI through active learning with additional supporting resources, such as digital scholarly editions. Keywords: AI, Active learning, Chaucer studies, Learning and teaching, Higher education, ChatGPT, Amazon Polly

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

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

Development of a digital scholarly edition (creating a database and data admin system to import texts; integrating annotation and editing tools to tie in the data; and using design wireframes to develop the resource).

Limitations and uncertainty · source passage

In general terms, the commercial mainstreaming of Generative AI tools such as ChatGPT and neural language processing services, such as the text-to-speech tool Amazon Polly, raises questions about how AI may feature in learning and teaching within Higher Education. Successfully using Generative AI requires an understanding of its benefits and limitations, as well as the type of learning and teaching framework it necessitates. UNESCO’s AI and Education: Guidance for Policy-Makers (Miao et al., 2021) and its 2024 Competency Frameworks for Students and Teachers advocate for a human-centred approach. This approach emphasises that AI should support human decision-making and intellectual development, which are integral to active learning. Despite growing interest, there remains little empirical research on developing AI for learning and teaching (Chan, 2023). This paper contributes to the field by presenting empirical findings on the use of AI in the study of Chaucer.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

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

Bridgette Wessels. Developing an AI framework for learning in higher education: a humanities perspective from English Literature. International Journal of Educational Technology in Higher Education (2025-11-03). https://doi.org/10.1186/s41239-025-00562-w

Companion prepared 2026-11-01. 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.