Artificial Intelligence and Résumé Critique Experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Where résumés are concerned, student supports tend to include tactical feedback that addresses issues in students’ writing and strategic feedback aimed at coaching critical self-reflection. However, there is not always time to cover all that could be offered by both kinds of feedback in a single résumé critique. Given demands on staff time, many career services administrators are considering opportunities to leverage artificial intelligence-based (AI) products that might offer tactical feedback and allow staff to focus on offering strategic feedback. In a field experiment, we explored how novice job seekers’ use of an AI-based résumé critique product influenced their subsequent face-to-face résumé critique experiences, especially the kinds of feedback offered and learning outcomes that resulted from this. As expected, the AI offered substantial tactical feedback and less strategic feedback. Students’ use of the AI did not result in greater opportunity for strategic feedback and associated learning outcomes. Rather, the AI rendered issues in students’ writing more salient. In turn, this invited more attention to tactical aspects and less attention to strategic aspects of students’ résumés.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it