The use of artificial intelligence in the recruitment of Generation Z
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».