Harnessing Generative Artificial Intelligence to Advance Education for Sustainable Development
Bibliographic record
Abstract
In this paper we report on a study of the impact of the CoDesignS ESD AI Coach on academic and practitioner perspectives regarding the integration of Education for Sustainable Development (ESD) within teaching and curriculum design. Employing activity theory as the analytical framework, we investigate both the opportunities and limitations associated with the use of generative AI (GenAI) in educational contexts to support ESD.Eighteen participants from a range of higher education (HE) institutions in the UK engaged in the research through a workshop setting. The cohort represented a breadth of disciplinary backgrounds, including engineering, medical sciences, sustainability and veterinary sciences.The workshop introduced participants to concepts of sustainability and ESD, followed by the CoDesignS ESD Framework and its Role, Objective, Community, Key, Shape (ROCKS) method for effective prompting.We employed a mixed-methods research design, incorporating pre-and-post surveys alongside focus groups. Discussion focused on the extent to which the tool enhanced or hindered participants’ understanding of sustainability concepts, and how well it aligned with disciplinary expectations. Reflections also addressed the perceived relevance and accuracy of GenAI-generated content.Survey data revealed that participants felt more confident and better equipped with resources to embed sustainability into their curriculum, and they reported increased assurance in using GenAI tools for curriculum development.Based on focus group analysis, we conclude that the CoDesignS ESD AI Coach holds particular value as a prompt for idea generation. However, it should not be used in isolation. Those specialising in sustainability and curriculum design should guide its effective implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".