Mitigating Disability Bias in Hiring: The Role of Inclusion‐Focused Generative AI in Complex HR Decisions

Artificial Intelligence (AI) has emerged as a transformative force in Human Resource Management (HRM), revolutionizing recruitment, talent acquisition, and employee retention. Organizations are increasingly adopting AI-driven tools such as chatbots, predictive analytics, and machine learning algorithms to streamline HR processes, enhance decision-making, and improve employee engagement. This paper investigates the role of AI in HRM, focusing on recruitment, talent acquisition, and retention. The study uses a mixed-method approach primary data collected through a survey of HR professionals and employees, and secondary data derived from peer-reviewed journals, reports, and industry case studies. Findings reveal that AI improves efficiency, reduces bias in hiring, and enhances employee experience, but challenges remain in terms of data privacy, ethical considerations, and over-reliance on technology. Suggestions are provided for sustainable integration of AI in HR practices.

Generative AI (GenAI) is increasingly deployed in health professions education, particularly for simulated patients and instructional imagery. However, concerns have emerged regarding demographic bias in AI-generated outputs, with potential consequences for equity, realism, and global applicability. This study presents a multi-method analysis of demographic representation across simulated ‘patient’ cohorts (GPT-3.5, GPT-4-mini) and AI-generated ‘clinical’ images (DALL·E 3, Midjourney). Quantitative comparisons against national census and survey benchmarks revealed significant overrepresentation of lighter skin tones, males, and middle-aged adults, alongside the near-complete absence of certain ethnic and age groups. However, prompt-based interventions incorporating demographic data achieved marked improvements in representativeness. These findings raise important questions about the readiness of current GenAI models for use in inclusive medical training environments. Inaccurate or stereotyped representations may undermine educational authenticity, reinforce existing disparities, and skew students’ expectations about the patient populations they will encounter in practice. Building on this analysis, we propose a framework for systematically auditing AI tools in medical education. Central to this is the development of an “AI report card” to evaluate models on key dimensions of demographic safety, regional appropriateness, and educational validity. The report card is designed to support educators and institutions in selecting GenAI tools that align with their curricular and equity goals. This work contributes to ongoing international efforts to ensure that the globalisation of health professions education is underpinned by principles of fairness, inclusivity, and contextual relevance. Future work will validate the framework across diverse educational settings and explore model fine-tuning and prompt engineering strategies to ensure safer, more representative AI-assisted simulation.

The rapid adoption of generative AI (GenAI) has intensified public discourse on its risks and benefits. However, research remains limited on how users perceive these risks and benefits across varying psychological distances and how they balance these perceptions in their adoption decisions. Drawing on construal level theory and regulatory focus theory, we conducted in-depth interviews with GenAI users ( N = 30). Findings reveal that users perceive GenAI's risks and benefits across proximal and distal dimensions concurrently. In their adoption decisions, they demonstrate either promotion-focused orientations (i.e. risk downplaying and strength prioritization) to emphasize GenAI's benefits or prevention-focused orientations (i.e. privacy protection, output scrutinization, and reliance abstinence) to mitigate its risks. This study provides theoretical and practical implications for AI adoption and risk communication, contributing to a deeper understanding of how users navigate the complexities of emerging AI technologies.

The rapid rise of Generative AI (GenAI) tools is reshaping conversations about assessment and feedback in higher education. While much institutional attention focuses on detection, compliance, and academic integrity (Cotton et al., 2024), this presentation shifts the lens to educators and how they are actually using GenAI in assessment practice. We present findings from a grant-funded initiative at UNSW that explores educator-led innovation through a Postcards of Practice approach. The Postcards of Practice are one-page, practice-based narratives where educators document their use of GenAI tools. These postcards highlight applications including formative feedback generation, student prompting literacy, assessment redesign, and co-creation with AI. They reveal how educators are experimenting with GenAI to support student learning while navigating ethical concerns, transparency, and pedagogical alignment. Our study uses a qualitative interpretive methodology, combining thematic analysis of the postcards with follow-up interviews. The analysis draws on theoretical frameworks including feedback literacy (Carless & Boud, 2018), dialogic assessment (Nicol, 2010), and new paradigm feedback design (Winstone & Carless, 2020). We also apply institutional and national GenAI guidelines (Liu & Bridgeman, 2023; Perkins, 2023) to surface shared values such as authenticity, inclusivity, and responsible innovation that guide educators’ decisions. The aim of this study is to explore how educators are experimenting with GenAI in assessment and feedback, and to capture their emerging practices and reflections through the Postcards of Practice initiative. The central research question guiding this work is: How are educators integrating GenAI into assessment and feedback, and what opportunities, challenges, and support needs arise from these practices? This work advances Technology Enhanced Learning (TEL) by providing empirical insights into how GenAI is actually integrated at the coalface of teaching. Educators describe how GenAI supports more frequent, personalised feedback and builds student agency in learning. At the same time, they raise concerns about over-reliance, AI hallucination, and the need for clear pedagogical scaffolding. These reflections point to the need for professional development that is discipline-sensitive, responsive, and grounded in practice. The postcard approach also functions as a professional learning intervention. It prompts reflection, encourages cross-disciplinary dialogue, and helps build a local community of practice around GenAI use. Through this model, we demonstrate an innovative and scalable method of capturing and supporting TEL innovation in real time. The findings suggest GenAI is prompting a rethinking of assessment: from summative, compliance-driven models to more transparent, formative, and student-centred designs. Educators begin to embed feedback literacy, ethical AI use, and critical prompting into their teaching, with clear implications for program-level assessment and graduate capability development. To strengthen clarity, we propose a concise diagram mapping the emerging practices captured in the postcards against the theoretical frameworks of feedback literacy, dialogic assessment, and new paradigm feedback design. This visual representation illustrates how practical insights align with, extend, or challenge these frameworks, making the study’s contribution accessible across diverse tertiary contexts. This proposal offers exemplary innovation in TEL by foregrounding bottom-up, practice-led experimentation with GenAI. It is grounded in strong theoretical frameworks and applicable across diverse tertiary contexts. The Pecha Kucha format will present key insights through rich visual storytelling, including excerpts from the postcards themselves. We conclude by proposing future directions for research and institutional strategy, including how to embed GenAI into assessment ecosystems in ways that enhance learning, uphold integrity, and empower educators to lead digital transformation from within.

This study investigates how a physician’s use of generative AI (GenAI) in medical decision‑making is perceived by peer clinicians. In a randomized experiment, 276 practicing clinicians evaluated one of three vignettes depicting a physician: (1) using no GenAI (Control), (2) using GenAI as a primary decision-making tool (GenAI-primary), and (3) using GenAI as a verification tool (GenAI-verify). Participants rated the physician depicted in the GenAI‑primary condition significantly lower in clinical skill (on a 1–7 scale; mean = 3.79) than in the Control condition (5.93, p < 0.001). Framing GenAI use as verification partially mitigated this effect (4.99, p < 0.001). Similar patterns appeared for perceived overall healthcare experience and competence. Participants also acknowledged GenAI’s value in improving accuracy (4.30, p < 0.002) and rated institutionally customized GenAI more favorably (4.96, p < 0.001). These findings suggest that while clinicians see GenAI as helpful, its use can negatively impact peer evaluations. These effects can be reduced, but not fully eliminated, by framing it as a verification aid.

Generative AI has taken the world by storm. With millions of regular users, billions of requests and corresponding results, tools employing Generative AI are ceaselessly used and abused for a wide variety of purposes. This article focuses on the problem of deception resulting from Generative AI and proposes the notion of quadruple deception to capture a set of related, yet distinctive forms of deception: 1) deception regarding the ontological status of one’s interactional counterpart, 2) deception regarding the capacities of AI, 3) deception through content created with Generative AI as well as 4) deception resulting from integration of Generative AI into other software. Arguing that deception severely challenges practices of assessing trustworthiness and placing trust wisely, I assess the epistemic, ethical and political implications of misplaced trust and distrust resulting from these four kinds of deception. The article concludes with some suggestions on how the trustworthiness of Generative AI could be increased to ground more justified trust and sketches corresponding duties for the design, development and deployment of Generative AI, the discourse about Generative AI, as well as the governance of Generative AI.

As technology continues to revolutionize the workplace, the role of HR professionals is undergoing a profound transformation. In the future, HR professionals will play a pivotal role in leveraging advanced technologies to drive strategic HR initiatives and enhance organizational performance. This abstract explores the key aspects of this evolving role, focusing particularly on the imperative for HR professionals to enhance their technical skills. One critical area where HR professionals will need to excel is in data analysis and analytics. By harnessing the power of data-driven insights, HR professionals can make informed decisions, predict future workforce needs, and optimize workforce management strategies. Additionally, as artificial intelligence (AI) and automation become increasingly prevalent, HR professionals must understand how to effectively implement and manage these technologies to streamline HR processes and enhance efficiency. Furthermore, in the realm of recruitment and talent acquisition, HR professionals must adapt their strategies to the digital age. This involves leveraging digital platforms, social media, and applicant tracking systems to attract, engage, and retain top talent in an increasingly competitive landscape. Moreover, the rise of remote work necessitates proficiency in collaboration tools and virtual communication platforms. HR professionals must ensure that remote teams remain connected, engaged, and productive by leveraging tools such as Slack, Zoom, and Microsoft Teams. In addition to these technical skills, HR professionals must prioritize cybersecurity and data privacy to protect sensitive employee information in an increasingly digitized HR environment. By implementing robust security measures and complying with data protection regulations, HR professionals can safeguard employee data and maintain trust within the organization. Finally, proficiency in HR information systems (HRIS) and other HR technology platforms is essential for streamlining HR processes, managing employee data effectively, and driving operational excellence. Keywords- HR professionals, technical skills, Data analysis, Artificial intelligence, Recruitment, Talent acquisition, Digital platforms, Remote work, Collaboration tools, Cybersecurity, Data privacy, HRIS, HR technology.

The influx of younger fans into the KBO necessitates improvements in existing character designs. Therefore, this study aims to leverage generative AI for effective KBO team character design to gain an efficiency advantage. The research methods included a literature review on generative AI, KBO, and character design. It also analyzed changes in character designs from past to present KBO teams, determining new design directions through SWOT and design analysis of existing characters. Two approaches were employed to develop character design drafts: using generative AI and idea sketches. Based on expert opinions, six directions were selected. Comparative analysis revealed that the draft 2-2 using generative AI, with its smiling expression and detailed facial features, effectively fostered emotional connections with consumers. In conclusion, generative AI can quickly visualize users' creative ideas economically, proving more efficient compared to character designs through idea sketches. Thus, the use of generative AI can contribute to enhancing competitiveness in the market.

BackgroundArtificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medico-legal implications, integration into healthcare service delivery and workforce utilisation. Also, there is not a clear pathway to implement and integrate generative AI in healthcare delivery.MethodsThis article aims to provide a comprehensive overview of the use of generative AI in healthcare, focusing on the utility of the technology in healthcare and its translational application highlighting the need for careful planning, execution and management of expectations in adopting generative AI in clinical medicine. Key considerations include factors such as data privacy, security and the irreplaceable role of clinicians’ expertise. Frameworks like the technology acceptance model (TAM) and the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) model are considered to promote responsible integration. These frameworks allow anticipating and proactively addressing barriers to adoption, facilitating stakeholder participation and responsibly transitioning care systems to harness generative AI’s potential.ResultsGenerative AI has the potential to transform healthcare through automated systems, enhanced clinical decision-making and democratization of expertise with diagnostic support tools providing timely, personalized suggestions. Generative AI applications across billing, diagnosis, treatment and research can also make healthcare delivery more efficient, equitable and effective. However, integration of generative AI necessitates meticulous change management and risk mitigation strategies. Technological capabilities alone cannot shift complex care ecosystems overnight; rather, structured adoption programs grounded in implementation science are imperative.ConclusionsIt is strongly argued in this article that generative AI can usher in tremendous healthcare progress, if introduced responsibly. Strategic adoption based on implementation science, incremental deployment and balanced messaging around opportunities versus limitations helps promote safe, ethical generative AI integration. Extensive real-world piloting and iteration aligned to clinical priorities should drive development. With conscientious governance centred on human wellbeing over technological novelty, generative AI can enhance accessibility, affordability and quality of care. As these models continue advancing rapidly, ongoing reassessment and transparent communication around their strengths and weaknesses remain vital to restoring trust, realizing positive potential and, most importantly, improving patient outcomes.

- Traditional recruitment processes are plagued by inefficiencies, biases, and limited inclusivity, impeding effective talent acquisition. To address these challenges, we present AI Recruiter, a generative AI-powered platform that redefines hiring through advanced automation and data-driven insights. Built on Generative AI , ZeroTouch Hire features a dual-portal system for HR professionals and student candidates. HR users post job listings, triggering an AI-driven pipeline that screens resumes using keyword matching, filters candidates, and initiates fully automated interviews. These interviews, conducted via text or voice, include scenario-based, technical, and logical questions, with real-time monitoring through facial recognition, motion detection, and screen sharing to ensure integrity. Post-interview, AI generates detailed reports, scoring candidates out of 10 across knowledge, critical thinking, English proficiency, and attitude, accompanied by personalized improvement suggestions. HR receives a recommendation to guide final decisions, preserving human oversight. The student portal enables candidates to practice by uploading resumes, participating in mock interviews, and receiving actionable feedback to enhance their skills. By automating screening, interviews, and evaluations, ZeroTouch Hire reduces HR workload, mitigates biases, and promotes inclusivity, offering a scalable, equitable solution that streamlines recruitment and empowers candidates for modern workforce demands. Key Words: Artificial Intelligence , Generative AI , Recruitment, Resume Analysis, Candidate Monitoring, Feedback System, Hr Automation, Natural Language Processing, Talent Acquisition

The advent of Generative Artificial Intelligence (Generative AI or GAI) marks a significant inflection point in AI development. Long viewed as the epitome of reasoning and logic, Generative AI incorporates programming rules that are normative. However, it also has a descriptive component based on its programmers' subjective preferences and any discrepancies in the underlying data. Generative AI generates both truth and falsehood, supports both ethical and unethical decisions, and is neither transparent nor accountable. These factors pose clear risks to optimal decision-making in complex health services such as health policy and health regulation. It is important to examine how Generative AI makes decisions both from a rational, normative perspective and from a descriptive point of view to ensure an ethical approach to Generative AI design, engineering, and use. The objective is to provide a rapid review that identifies and maps attributes reported in the literature that influence Generative AI decision-making in complex health services. This review provides a clear, reproducible methodology that is reported in accordance with a recognised framework and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 standards adapted for a rapid review. Inclusion and exclusion criteria were developed, and a database search was undertaken within four search systems: ProQuest, Scopus, Web of Science, and Google Scholar. The results include articles published in 2023 and early 2024. A total of 1,550articles were identified. After removing duplicates, 1,532articles remained. Of these, 1,511 articles were excluded based on the selection criteria and a total of 21 articles were selected for analysis. Learning, understanding, and bias were the most frequently mentioned Generative AI attributes. Generative AI brings the promise of advanced automation, but carries significant risk. Learning and pattern recognition are helpful, but the lack of a moral compass, empathy, consideration for privacy, and a propensity for bias and hallucination are detrimental to good decision-making. The results suggest that there is, perhaps, more work to be done before Generative AI can be applied to complex health services.

Loneliness and Social Isolation from elder people present a critical factor in different diseases of aging, necessitating tailored intervention for individuals to effectively engaging with interventions and interacting to their stakeholders. A great deal of research efforts has been made to tackle the challenging issues related to loneliness and social isolations of elder people, mainly focusing on develop various personal and group interventions such as social robots, group/community activity interventions, serious games, and Chatbots. The key barrier to have long-term impacts from these interventions is to make interventions adaptable to individual’s personal needs, scenarios/environments and contexts. To overcome the barrier, how to design adaptable interventions and implement interventions adaptably to meet a person’s needs is a great research question. This research aims to address this research question by (1) applying adaptable design principles into smart intervention design of a smart flowerpot or pet plant, enabled by generative AI and reconfigurable design, (2) applying digital twin technology to create a user’s personal replica with only needed information to support personalized intervention adaption. The objective is to create a smart service system/platform based on Digital Twins and Generative AI to effectively design and deliver smart interventions, for and with elderly individuals and their stakeholders in a smart service ecosystem way. The smart flowerpot is purposely designed to make it like a normal pot flower like most families having them at home, its visitor/user can trim, water, move and care it. The home users typically visit their home flowerpots regularly if not daily. And if a flowerpot becomes very attractive to its users, it can become a pet plant befriending with its users. Some reports already indicates that pet plant can be a useful means for companying elder people at home and make flowerpot-based intervention easier to adapt. On the other hand, to turn an ordinary home flowerpot into a smart flowerpot, we design a special structure for a flowerpot, which not only support a normal flower plant to grew but also can smartly interact with its users/visitors/cares. Within the structure, we equip some IoT sensors to sense a visitor presence, and then connect to the visitor’s personal digital twin to gain better understanding of the visitor’s needs, and finally feed the user’s needs into generative AI and reconfigurable designed elements to provide personalised interventions such as coloured light, display patterns, background music and chats generated by generative AI, etc. This paper presents our research processes, methods and evaluations via a smart flowerpot design, development and testing. The qualitative evaluation shows that our proposed adaptable intervention design and implementation system enabled by digital twin and generative AI technologies has a great potential of bettering understanding of a user’s needs, user engagement with interventions and user experience. Thus, this research has the potential to herald a new era in healthcare for the aging population, fostering smooth smart service and improved quality of life for older adults.

Thus, the appearance of the Generative Artificial Intelligence opened up a great turn in many areas, including education and creative industries. This paper seeks to understand the deep impact that Generative AI is going to have on learning and creating processes for the social context of Generation Z (Gen Z) students – born in digital culture. The work looks into the possibilities and challenges that Gen Z in collaboration with Generative AI leads to the future of learning and creativity. This paper is relevant as it offers some understanding of the ongoing changes in the education and creativity together with the escalating growth in technology. The nature of the association between the members of Generation Z and the Generative AI needs to be known by the educational stakeholders, policymakers, and business executives to leverage value from the existing and upcoming technologies together with dealing with possible negative impacts. The purpose of this study was to explore the nature and uses of Generative AI, and its effects on the learning and creativity of Gen Z, in addition to identifying the advantages, disadvantages, opportunities, and risks/partities’ concerns that are commensurate with the integration of this technology in teaching/learning and creative processes. To achieve the objectives of the study the following research methodology was used: The research used both a literature review and documentary research. The materials used included academic publications, Industry reports, books and other credible internet sources on Generative AI and its impact on the education and creativity of the Gen Z. The document analysis included policy papers, educational technology reports, case studies and white papers from academic and professional bodies as well as other industries that involve Generative AI. Several insights show that using Generative AI can positively impact learners’ experiences, engagement, and creativity. However, there was some controversy about the excessive usage of AI and claimed that because of it people may get worse at critical thinking. The following were noted to be major concerns; Ethical Issues: they included issues to do with bias in the algorithms as well as the right to privacy of data. Thus, the findings of this research point to a three-way settlement with respect to the use of Generative AI in education and creative industries. It underlines the guideline of how human creativity and critical thinking ought to be sustained, while using AI tools. Proposals include, the need to teach critical thinking alongside AI use, fostering ethical AI consciousness, surged AI education, appropriate non-ethnical AI data set, strong AI policies and pro positive AI inspires and creative constructive use. The research implications for future studies include studying the changes in the achievement of learning outcomes over a period of time, wherein Generative AI has been incorporated and understanding how this technology influences different learning styles and needs, the issues of ethical and privacy concern, the requirement of professional development to educators in relation to Generative AI and finally, the comparison information and communication technology for learning between different cultures. Related to that, further studies on the effectiveness of AI in approaches like collaborative learning, its potential on preparing learners for employment, and on the psychology of students would be helpful in informing the future advancement of Generative AI in school and particular creative areas.

The rapid integration of artificial intelligence into organizational processes has redefined the landscape of human resource management (HRM), giving rise to a new paradigm, Cognitive HRM. This study explores how Generative AI technologies can be harnessed to cultivate emotionally intelligent and inclusive workforce ecosystems. Drawing upon theories of emotional intelligence (EI), cognitive systems, and socio-technical integration, the paper conceptualizes a Cognitive HRM framework that aligns algorithmic cognition with human empathy and ethical inclusivity. Using a conceptual and analytical approach, this research synthesizes contemporary literature on AI-driven HR practices, emotional analytics, and bias mitigation to propose an integrative model where generative AI enhances human understanding, adaptability, and fairness in decision-making. The findings suggest that when cognitive technologies are designed to emulate emotional reasoning and cultural awareness, they can augment HR functions such as recruitment, talent development, and employee engagement. The study concludes that embedding emotional intelligence and inclusion principles into generative AI systems can transform HRM into a more equitable, responsive, and human-centered domain. The proposed framework provides strategic and ethical guidance for organizations seeking to balance automation with empathy, thereby fostering sustainable and inclusive digital workforce ecosystems.

This study synthesizes empirical evidence on AI-supported skill assessment systems in higher vocational education through a systematic review and meta-analysis. Despite growing interest in generative AI within higher education, empirical research on AI-enabled assessment remains fragmented and methodologically uneven, particularly in vocational contexts. Following PRISMA 2020 guidelines, 27 peer-reviewed empirical studies published between 2010 and 2024 were identified from major international and Chinese databases and included in the analysis. Using a random-effects model, the meta-analysis indicates a moderate positive association between AI-supported assessment systems and skill-related learning outcomes (Hedges’ g = 0.72), alongside substantial heterogeneity across study designs, outcome measures, and implementation contexts. Subgroup analyses suggest variation across regional and institutional settings, which should be interpreted cautiously given small sample sizes and diverse methodological approaches. Based on the synthesized evidence, the study proposes a conceptual AI-supported skill assessment framework that distinguishes empirically grounded components from forward-looking extensions related to generative AI. Rather than offering prescriptive solutions, the framework provides an evidence-informed baseline to support future research, system design, and responsible integration of generative AI in higher education assessment. Overall, the findings highlight both the potential and the current empirical limitations of AI-enabled assessment, underscoring the need for more robust, theory-informed, and transparent studies as generative AI applications continue to evolve.