Workshop: Strategic Development of AI in Teaching and Learning
Bibliographic record
Abstract
The workshop session will allow participants to take a strategic view of the development of artificial intelligence (AI) in learning and teaching (T&L). No prior knowledge of artificial intelligence will be required, and a range of experience and knowledge will enhance the experience of participants. The workshop design is based on a new ‘lens’ tool which aligns to the Jisc Beyond Blended strategic pillars (Beetham, MacNeill & McGill, 2024). The tool includes prompt questions which will be used to stimulate discussions about AI in blended learning and teaching. The AI in T&L Strategic Lens is a new adaptation of the existing Jisc Beyond Blended strategic lenses. The lenses are a tool which provide perspectives on designing blended learning and they are positioned within the organisational digital culture and knowledge development components of the Jisc Digital Transformation Framework (McGill, 2023). The lenses were developed by the Jisc Beyond Blended authors (Beetham, MacNeill & McGill, 2024) and include 6 perspectives: holistic strategic issues; learning space design; learning platform design and implementation; teaching time and workload; equality, diversity and inclusion; data collection and analysis. The new AI in T&L Strategic Lens has been created by the workshop facilitator, Alison Purvis, to supplement and complement the existing Jisc Beyond Blended lenses. The development is being shared with the original Beyond Blended authors. The lens been designed to explore strategic aspects of AI in blended learning and teaching. Participants will be able to contextualise their thinking and contributions to discussions based on their role and the strategic projects that they are involved with.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".