Bibliographic record
Abstract
Purpose: The purpose of the study was to determine how Generation Z perceives the benefits and risks associated with the use of artificial intelligence (AI) in recruitment processes. The analysis examined the relationships between respondents’ assessments and their gender, level of education, and professional experience in order to determine whether these characteristics differentiate the perception of AI’s impact on the recruitment process. Design/methodology/approach: The study was conducted in Poland in the second quarter of 2025 using the CAWI method (Computer-Assisted Web Interviewing) with a proprietary online questionnaire. The sample included 463 representatives of Generation Z (individuals born between 1995 and 2012). The sampling was purposive, encompassing both individuals who were professionally active or had experience with recruitment processes, as well as those without any professional experience. Respondents evaluated various aspects of the use of AI in recruitment, including benefits (such as process speed, job-offer matching, and objectivity) and risks (such as algorithmic errors and limited interpersonal contact), using a 5-point Likert scale. Nonparametric tests were applied to analyze the results: the Mann-Whitney U test, the Kruskal Wallis test, and Spearman’s rank correlation coefficient (Rs), which allowed for examining the relationships between variables and verifying (or rejecting) the research hypotheses. Findings: Generation Z generally evaluates the use of artificial intelligence in recruitment positively, primarily recognizing the speed, convenience of applying, and objectivity of the selection process. These assessments do not differ significantly by gender, education level, or professional experience. Among the perceived risks, respondents most often indicated the limitation of interpersonal contact and the risk of algorithmic errors; however, these were not seen as factors that completely disqualify the use of AI. The evaluation of risks also showed no significant differences depending on socio-demographic characteristics. Research limitations/implications: A limitation of the study is the sample size (N = 463), which does not allow for full generalization of the results to the entire Generation Z population. Additionally, due to space constraints, the analysis covered only two risk factors related to the use of artificial intelligence in recruitment—the limitation of interpersonal contact and the risk of algorithmic errors—thus narrowing the scope of result interpretation. In future research, it would be valuable to expand the sample and apply a mixed-methods approach (quantitative and qualitative), such as in-depth interviews or case studies, which would allow for a more comprehensive understanding of candidates’ motivations, emotions, and expectations regarding the use of artificial intelligence in recruitment. Practical implications: The study findings are relevant for employers and recruiters, indicating that: Generation Z expects recruitment processes to be fast, convenient, and transparent, while still maintaining human interaction; Artificial intelligence should be treated as a supporting tool, not as a replacement for recruiters; Combining automation with opportunities for human interaction at key stages of the process can enhance candidates’ experience and their satisfaction with recruitment. Originality/value: The study provides both scientific and practical value, as it analyzes Generation Z’s perception of artificial intelligence in recruitment, taking into account both benefits and risks. The findings offer up-to-date insights into the expectations of young candidates regarding AI in recruitment processes, which can support employers and HR professionals in designing more efficient and transparent recruitment practices. The study also emphasizes the importance of combining automation with human involvement in candidate selection, representing a significant contribution to the literature on modern HR practices. Keywords: artificial intelligence, recruitment, Generation Z, benefits and risks. Category of the paper: science article.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".