Artificial Intelligence in Human Resources Management: A Review and Research Agenda
Bibliographic record
Abstract
Background: Researchers and practitioners both exhibit a growing interest in the application of Artificial Intelligence in Human Resources Management. However, research shows that there remains a substantial gap between the promise of AI and its practical application in organizations. Previous research has identified some of the challenges facing the application of Artificial Intelligence in Human Resources Management. Among these challenges is the varied nature of Human Resources functions. To address this, we adopt the Human Resource Life Cycle, which is composed of 6 dimensions that closely mirror the Human Resource functions that exist in many organizations: 1) Strategic Planning, 2) Recruitment and Deployment, 3) Training and Development, 4) Performance Management, 5) Compensation Management, and 6) Human Relations Management. Method: Through a scoping literature review, we have identified 85 articles on the topic and classified them based on the 6 dimensions of the Human Resource Life Cycle. Results: Our scoping review found that Artificial Intelligence has already been studied in relation to all 6 dimensions of the Human Resource Life Cycle. In addition, a seventh dimension was identified and integrated into the existing Human Resource Life Cycle framework: Legal and Ethical Issues. Based on the scoping review, a research agenda is presented to provide guidance for future research in the field of Artificial Intelligence in Human Resources Management. Conclusion: All 6 dimensions of the Human Resource Life Cycle, along with the seventh dimension – Legal and Ethical Issues – are already present in the literature. Future research could focus on the impact of AI on connections between dimensions, as well as the impact on HR-specific outcomes. Practitioners must recognize the limitations related to the application of AI in Human Resources Management, even though AI should still be viewed as a solution to many challenges facing Human Resources Management in organizations.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".