Exploring the Applicability of Artificial Intelligence in Recruitment and Selection Processes: A Focus on the Recruitment Phase
Bibliographic record
Abstract
This study explores the use of artificial intelligence (AI) in recruitment and selection (RS) within the context of Human Resource Management (HRM). Contrast to past studies, this study makes a valuable theoretical contribution of a conceptual model to understand the effective use of AI in recruitment & selection processes by investigating the underexplored impact of the recruitment phase and the critical perspective of recruitment professionals. In so doing it builds on and extends technology adoption theory within the information systems by integrating HRM literature. The findings of this qualitative study indicate that AI is suitable for use in specific recruitment phases such as sourcing, pre-screening/pre-selection, and candidate engagement. However, there is a reluctance to use AI in the recruitment pre-planning and interview stages. It contributes valuable insights to inform HRM practitioners and organizations seeking to integrate AI into their recruitment processes.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".