Leveraging Neuro technologies to Assess Cognitive Load in AI-Driven Hiring Systems
Faidat Bello Faidat Bello · International Journal of Advances in Engineering and Management · 2025
The adoption of AI-driven hiring systems for modern recruitment purposes becomes widespread because these systems optimize candidate assessment and selection processes. The issues related to candidate experience together with transparency and fairness continue to represent crucial problems in such hiring approaches. The research examines methods for using neurotechnology to measure cognitive load during AI-driven recruitment processes to better understand assessment-related cognitive stress on candidates. The main purpose of this study involves investigating how cognitive load shifts when different AI-processed interview approaches are used and exploring if neurological data collections help measure candidate involvement and anxiety levels effectively. Users participated in an experimental data collection phase followed by statistical analysis to accomplish the research findings. Participating candidates went through automated interviews with structured and unstructured questioning while brain signals were tracked through electroencephalography (EEG) and eye-tracking and heart rate variability (HRV) monitored their cognitive load. The researchers added self-report surveys to the study as supplemental data collection after participants finished interviewing with AI. The research data shows that AI interview systems which operate using only computer-generated questions produce increased cognitive challenges than traditional human-assisted virtual screening. Job candidates developed elevated cognitive stress when working with AI systems which failed to show their decision-making parameters. Candidate performance improved when AI systems gave live feedback along with descriptions of evaluation metrics during the interview process. An evaluation of neurotechnological data during AI-driven hiring systems provides practical information for optimizing both candidate interviews and recruitment assessment frameworks. Research demonstrates how neurotechnologies can boost the development of AI-based hiring systems which become more favorable for candidates while remaining transparent in their evaluations. Researchers should investigate ethical factors with potential biases alongside scalability prospects in AI recruitment procedures to maintain ethical and fair hiring processes.