Reflections on co-researching AI literacy
Bibliographic record
Abstract
Students as Partners (SaP) approaches have gained more and more traction in higher education in recent years (Dai & Matthews, 2022). Rooted in values such as reciprocity and shared responsibility, SaP can offer opportunities for internationalizing the curriculum and departing from traditional teacher-student hierarchies (Green & Baxter, 2022). This case study focuses on a SaP project involving international students and their English for Academic Purposes (EAP) teacher, which investigated artificial intelligence (AI) literacy during a UK pre-sessional course in summer 2023. The project identified that learning about the limitations of AI, in addition to developing skills for effective prompt writing, was beneficial to students (Partridge et al, 2023). This case study reflects on the challenges and benefits of SaP for both students and the teacher using the Advance HE (2016) Framework for Student Engagement Through Partnership. Based on these reflections, the case study offers recommendations for future SaP projects including effective scheduling, defining roles, engaging in continual reflection, and formally recognising student input.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →1 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.
Reflective case study on a students-as-partners co-research project, with recommendations on roles and recognizing student input; commentary touching on how collaborative inquiry is conducted, so contextual at most.
The case study explicitly examines co-researching practices and the challenges of partnership in conducting research.
Case reflection on Students-as-Partners pedagogy for AI literacy teaching, not study of research as an object.
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 | 0.047 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.011 | 0.040 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".