Bibliographic record
Abstract
Large language model (LLM) enabled tools are increasingly omnipresent in our teaching and learning environments. Most of the focus so far has predominantly been on the impacts on assessment and ensuring the security of those assessments. However, there are increasing questions being asked around impacts on learning. Since the 2010s we have been aware of risks to atrophy in the hippocampus due to changes in how we navigate when using GPS devices compared to when we do not (Stromberg, 2015). We are also aware that how we approach reading is different dependent on whether it is digital or in-print, with digital engagement often being quicker and of less depth, with potential impacts on learning (Allcott, 2021). Research by Kaufman and Flanagan (2016) found that students reading digitally did well on answering concrete questions. However, those reading in print did better on abstract questions needing inferential reasoning. A recent paper by Fan et al. (2024 found that ‘AI technologies such as ChatGPT may promote learners’ dependence on technology and potentially trigger “metacognitive laziness”’. How learners engage with these new platforms and capabilities is increasingly important. When students seem increasingly willing to cognitively offload problem solving, what approaches could we take to enable the development the levels of critical engagement required to engage with these tools in a productive manner when many are novices and do not yet have the foundation knowledge and critical literacies to do so? In this interactive workshop you had the opportunity to discuss key issues related to lazy cognition and co-create learning development guidelines for enhancing critical literacies and fostering deep learning. Session outcomes are being collated and will be shared as a community resource. Workshop attendees had the opportunity to be named as co-authors.
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".