Using AI in Writing-Intensive Disciplines: Understanding LLM strengths and weaknesses
Bibliographic record
Abstract
This slide deck, Using AI in Writing Intensive Disciplines, introduces students to responsible and critical use of generative AI in academic contexts. It frames AI as a powerful but risky tool—comparable to a chainsaw—that can support tasks like summarisation, style imitation, and proofreading but is poorly suited for creative invention, sustained argumentation, or original analysis. The presentation highlights common misconceptions (e.g., that AI “thinks” or “understands”) and explains limitations such as hallucination, sycophancy, mediocrity, and bias. Students are taught to assign AI tasks that play to its strengths, to document and iterate their interactions, and to distinguish between naïve prompts (e.g., “Write me an essay”) and engineered prompts (e.g., “Review this introduction for clarity”). Exercises encourage students to compare naïve versus engineered prompting, track conversations, and critically evaluate accuracy, bias, and their own intellectual contribution. The deck concludes with a reflective mini-assignment asking students to demonstrate and analyse their AI use.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".