Artificial Intelligence and Résumé Critique Experiences
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.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".