Trustworthy Integration of AI in Online Learning: Supporting Student Agency and Inquiry through Open Pedagogy
Bibliographic record
Abstract
Open pedagogy is a transformative educational approach that fosters innovative, inclusive, and sustainable instructional practices while addressing persistent challenges such as accessibility, equity, and the affordability of educational resources. This qualitative study explores how open pedagogy can develop AI literacy skills while supporting student agency and inquiry in online graduate education. The collaborative initiative involved two cohorts of graduate students (n = 39) who co-authored openly licensed textbooks: AI-Enhanced Instructional Design (ETAD 873, 2023) and Streamlined Instructional Design with AI (ETAD 873, 2024). A multi-phase data collection approach included analysis of student-authored chapters, structured peer review processes, and open-ended focus group interviews. The resulting open textbooks demonstrate critical engagement with generative AI technologies, highlighting the value of knowledge co-creation and innovative approaches to instructional design. Findings are synthesized into practical guidelines for empowering adult learners to responsibly and creatively utilize AI as a partner in co-designing open educational resources (OER). A significant contribution of the study is the development of a framework comprising ten open pedagogy attributes that facilitate the trustworthy integration of AI, enabling deep, meaningful, and authentic learning experiences in higher education.
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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 | 0.023 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".