Graduation Year

2025

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Leadership, Policy, and Lifelong Learning

Major Professor

Oscar Aliaga Abanto, Ph.D.

Committee Member

Robert F. Dedrick, Ph.D.

Committee Member

Noémi Nagi, Ph.D.

Committee Member

Liliana Rodriguez-Campos, Ph.D.

Keywords

Artificial Intelligece, Facial Analytics, Voice Analytics, Machine Learning, Psychometric Testing, Hard of Hearing

Abstract

This study explores the intersection of Career and Workforce Education, Deaf Education, and artificial intelligence (AI) technologies used in hiring practices. The purpose of this research is to identify potential barriers to equitable employment for Deaf and Hard-of-Hearing (DHH) individuals and to propose effective methods for evaluating and assessing AI and machine learning applications utilized in recruitment and selection processes. Although AI is increasingly embedded in human resource management (HRM), there remains a significant lack of research addressing its use with special populations. In particular, no prior studies have examined how voice recognition software, facial analysis, psychometric testing, and virtual interview platforms impact DHH applicants – an omission that defines the central problem guiding this inquiry.

Grounded in a Critical Realism ontology, this qualitative exploratory study employed a critical analysis lens supported by social justice theory and sociotechnical systems theory. Data were collected through semi-structured interviews with seven hiring managers and one corporate human resource specialist, for seven businesses in the Hospitality and Tourism sector and analyzed using a systematic combining abductive approach. Businesses included one luxury hotel, one midscale hotel, one table service restaurant, one fast casual restaurant, and three quick-service restaurants. This methodological design allowed for continuous interplay between theory and empirical data, enhancing interpretive depth and contextual validity.

Findings indicate that AI technologies are not currently prohibitive for DHH applicants within the hospitality sector. Hiring managers continue to prioritize the interpersonal assessment of candidates achieved through in-person interviews, which they perceive as more authentic measures of suitability and professional competence. Participants recognized certain AI-enabled tools as beneficial in streamlining recruitment processes and expanding applicant reach.

The study concludes that while AI systems present opportunities for efficiency and inclusivity, there remains a need for more robust accountability mechanisms, policy frameworks, and evaluation instruments to govern their use in HRM and job posting platforms. The absence of comprehensive governance and oversight in the United States underscores the importance of continued research and policy development to ensure fair, equitable, and transparent AI-driven hiring practices.

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