Student Evaluative Judgements of Writing and Artificial Intelligence: The Disconnect between Structural and Conceptual Knowledge
Bibliographic record
Abstract
This paper reports on how undergraduate students evaluated writing outputs created with and without generative artificial intelligence (AI). The paper focuses specifically on two aspects of writing and AI: how prior writing knowledge influenced students’ thinking about AI tools, and how the writing skills to which they were exposed in the writing classroom helped them work with AI-generated materials. This research builds upon Bearman et al.’s (2024) work on evaluative judgement as a pedagogical tool to support learners as they work with AI-mediated texts. The paper uses this lens to identify challenges that learners have in applying writing knowledge to AI-mediated situations and to devise pedagogical means to support student learning in these contexts. We found that, while students could typically evaluate structural components of writing, they struggled to evaluate conceptual ideas both for AI and human generated texts. The findings speak more generally to the need for students to develop their evaluative abilities, as well as ways that AI may reveal and amplify existing challenges that learners have with evaluating the quality of writing, engaging with source materials, and applying genre knowledge to create meaning.
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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 | 0.022 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".