HR Professionals Research Articles

The Impact of Artificial Intelligence on Talent Acquisition and Employee Experience in Modern Organizations

ABSTRACT In the digital age, Artificial Intelligence (AI) has emerged as a transformative force in reshaping human resource management, particularly in the domains of talent acquisition and employee experience. This study investigates the impact of AI-powered tools and technologies on recruitment strategies, candidate engagement, and the overall experience of employees in modern organizations. The research aims to assess how AI enhances recruitment efficiency, reduces biases, and improves decision-making processes while also evaluating its influence on employee satisfaction, onboarding, engagement, and workplace support. A quantitative methodology was adopted, using a structured Google Form survey distributed among HR professionals, recruiters, and employees from various sectors. The survey collected insights into the use of AI tools such as applicant tracking systems (ATS), chatbots, automated screening platforms, and sentiment analysis tools in HR operations. The results indicate a growing reliance on AI to streamline hiring, improve job-candidate matching, and personalize employee support systems. However, concerns around ethical implications, data privacy, and the potential depersonalization of human interaction were also highlighted. This research contributes to the academic and practical understanding of AI's dual role in optimizing recruitment workflows and enriching the employee journey. It emphasizes the need for a balanced approach that integrates technological innovation with human- centered HR practices. The findings offer actionable insights for HR professionals, policymakers, and organizational leaders aiming to leverage AI for sustainable talent management and enhanced workplace experiences.

Impact of Green HRM Practices on Organization Sustainability of Apparel Industry: Special Reference to Koggala Export Processing Zone, Sri Lanka

This study investigates the key factors influencing organizational sustainability in the apparel industry, with a focus on Green Training, Green Rewards, and Green Employee Performance Management. The research addresses a gap in the literature regarding the impact of green HRM practices on sustainability outcomes in Sri Lanka's apparel sector. Drawing on social learning theory and expectancy theory, the study explores the relationship between these independent variables and organizational sustainability. Data were collected through a structured questionnaire from 162 middle-level employees at the Koggala Export Processing Zone. The reliability and validity of the instruments were confirmed, and data were analyzed using correlation and multiple linear regression analysis. The findings reveal a significant positive relationship between Green Training, Green Rewards, and Green Performance Management with organizational sustainability. Specifically, Green Training showed a strong positive impact on sustainability, followed by Green Rewards and Green Performance Management. These results highlight the importance of integrating green HRM practices into HR policies and organizational strategies. The study suggests that HR professionals and apparel industry leaders can leverage these findings to enhance sustainability practices and foster healthier, more secure work environments.

Neoliberalism and atomised, disaggregated Gig Academy workers: a challenge for HR professionals in the Higher Education sector?

ABSTRACT In this article the role of Human Resource Management (HRM) in the UK Higher Education (HE) sector is interrogated, with a focus on the Gig Academy. A literature review of the casualisation of academics in the sector is undertaken and critiqued through the consultative unitarist values and behaviours of the Chartered Institute of Personnel and Development (CIPD), predicated on the mutual gains model of good HRM being good for both employer and employee. It is argued that the Gig Academy, situated within a neoliberal context, emphasises the needs of the education market but is at odds with these values and behaviours. More particularly, disaggregated, atomised labour is used to meet the needs of the performative university at the expense of gig academics, particularly women and ethnic minority academics, experiencing precarity and a lack of mutuality. Precarity experienced by gig academics further contributes to the de-politicising of academic staff as a means of meeting government metrics, at the expense of other stakeholders. This article argues that to address these issues, cultural rather than disaggregated HR practice is required in the HE sector, based on a commitment to HR professionalism and the values and behaviours of the CIPD.

Impact of AI and Automation on Talent Acquisition and Employee Retention

ABSTRACT This study looks into how artificial intelligence (AI) and automation are changing the way companies hire and keep their employees. Over the last few years, businesses have started using technology to make HR processes faster and smarter. AI tools are now helping with everything from shortlisting candidates to predicting who might leave the company. This research combines insights from existing studies and a small survey conducted among HR professionals and employees. While AI clearly makes recruitment quicker and more data-driven, there are also concerns—like the lack of human connection or the fear of job loss. The aim of this paper is to explore both sides of the coin and help understand how AI can be used thoughtfully in the workplace.

Digital Collaboration in Remote-First HR Firms: Key Drivers of Global Team Effectiveness

The debate over remote work continues to intensify as major global companies call for a return to in-person operations, driven by concerns over disrupted communication and productivity. However, technological advancements, access to a global talent pool, and growing employee demand for flexibility suggest that remote work is a long-term trend. This study investigates collaboration in remote-first, globally distributed teams, with a focus on the HR professional services industry. Based on a survey of employees across 45 countries, the study offers practical recommendations for managing global virtual teams across time zones, such as adopting culturally agile leadership, utilizing advanced digital platforms, and implementing clear communication protocols. The findings highlight the critical role of strong leadership, effective communication, inclusivity, and trust in fostering successful collaboration. While technology plays a key role, leadership’s ability to cultivate trust and encourage knowledge sharing emerged as the most significant factor. The study also found that cultural diversity did not significantly hinder collaboration, but it reinforced the importance of inclusive leadership and structured digital workflows to address potential challenges. These insights contribute to the growing body of knowledge on remote work models and emphasize the need for further research into long-term global virtual collaboration, particularly in non-tech industries. The findings provide valuable guidance for managers and HR professionals navigating the complexities of managing remote work in a globalized context.

Staff Engagement Impact on Employee Performance in the HealthCare Sector in Saudi Arabia

Employee Engagement has a positive impact not only on individuals but also on healthcare organizational outcomes’ efficiency and effectiveness, hence increase overall performance and ensures long-term survival in a competitive environment. The study is conducted with the aim of investigating the Staff Engagement Impact on Employee Performance in HealthCare Sector in Saudi Arabia. Variables are employee engagement, performance (productivity, Retention Commitment, intent to leave), and job satisfaction. Participants in this research were 186 employees working in Saudi Arabia. The data were collected by using an online questionnaire consisting of three sections and analyzed by SPSS. The key findings are employee engagement is positively associated with employee performance, which was measured through three key items: self-reported productivity, retention commitment, and intent to leave. Specifically, employee engagement showed a strong positive relationship with productivity and retention commitment, while an inverse relationship was observed with intent to leave, highlighting its role in improving retention within the Saudi Arabian healthcare sector. Furthermore, employee engagement was found to have a positive effect on job satisfaction. However, when job satisfaction was examined as a mediating variable, employee engagement had no significant effect on employee performance, further results are detailed in the main text. 3 Hypothesis and 3 sub-hypotheses were developed to test the variables relation. Moreover, the recommendations may assist managers and HR professionals in understanding and adopting employee engagement strategies among healthcare providers, both now and in the future.

The Role of Employee Engagement & Employee Retention: A Study of Indian Start-ups

Abstract: Employee retention has become a growing concern in the dynamic and fast-evolving landscape of Indian start-ups, where limited resources, high competition, and a constantly shifting market environment challenge organizational stability. This study investigates the critical role that employee engagement plays in influencing retention decisions within Indian start-ups, particularly in sectors such as technology, e-commerce, fintech, and healthcare. Grounded in the theoretical framework of the Utrecht Work Engagement Scale (UWES) by Schaufeli and Bakker, this research explores three core dimensions of engagement—vigor, dedication, and absorption—and their individual and collective impact on employee retention. A quantitative research methodology was adopted, involving structured surveys administered to a purposive sample of 300 full-time employees working in various start-ups across India. The data collected were analyzed using correlation and regression analysis to determine the strength and nature of the relationship between engagement and retention. The findings of the study reveal a statistically significant and positive relationship between employee engagement and retention, with highly engaged employees reporting a stronger intent to remain in their current organizations. Among the multiple drivers of engagement, organizational culture, leadership style, and career development opportunities were found to be the most influential in determining retention outcomes. Furthermore, the study found that while financial compensation plays a role, it is often outweighed by intrinsic motivators such as recognition, meaningful work, autonomy, and alignment with organizational values. Start-ups that cultivate a supportive work environment, offer learning opportunities, and maintain open lines of communication are better positioned to retain top talent. These findings have significant implications for start-up founders, HR professionals, and policymakers who aim to reduce attrition, improve organizational performance, and foster sustainable business growth in emerging economies.

Empowering the Workforce: Evaluating AI-Driven Employee Self-Service Portals in the Digital HR Era

ABSTRACT: This study investigates how Artificial Intelligence (AI) is transforming Employee Self-Service (ESS) portals and their impact on empowering employees in today’s digital HR environment. The main aim is to evaluate how AI features such as automation, chatbots, and smart assistance help employees manage HR tasks independently, leading to greater efficiency, satisfaction, and engagement. A mixed-method approach was adopted, combining quantitative data from 150 employee questionnaires and qualitative insights from interviews with HR professionals. Descriptive statistics (mean and standard deviation) and inferential methods (correlation and regression analysis) were used to analyze survey data using SPSS. Thematic analysis was applied to qualitative data to identify patterns and employee perceptions. Findings reveal that AI-driven ESS portals significantly improve service speed, employee autonomy, and overall satisfaction. There is a strong positive relationship between the usability of AI tools and employee empowerment. However, challenges such as data privacy concerns, limited training, and occasional technical issues were also reported. A small percentage of employees prefer traditional human interaction for certain HR processes. The study concludes that AI-powered ESS portals are effective tools for improving employee empowerment and HR service delivery. To maximize impact, organizations must focus on user-friendly design, continuous training, and ethical data handling to ensure trust and inclusive benefits for all users. Keywords: Artificial Intelligence, Employee Self-Service, Digital HR, Employee Empowerment, HR Technology, Automation, Chatbots.

Analysis of Funding for HRM and Its Relationship with Brain Drain in Greece from 2020 to 2024

This study investigates the relationship between human resource management (HRM) practices and the phenomenon of brain drain in Greece during the period 2020–2024. In the context of economic uncertainty and demographic shifts, the emigration of skilled professionals has posed serious challenges to the country’s labor market and long-term development. Employing a mixed-methods approach, the research combines quantitative data from national labor force surveys and HR statistics with qualitative insights gathered through semi-structured interviews with HR professionals and expatriates. The study applies descriptive and inferential statistical methods, including regression analysis, to examine how key HRM dimensions—such as workplace flexibility, career development, and performance-based incentives—affect employee retention. Results reveal a significant inverse relationship between HRM quality and brain drain rates, with workplace flexibility and career development emerging as critical predictors. The findings highlight the need for strategic HRM reforms tailored to the Greek context and offer evidence-based recommendations for mitigating talent outflows. This research contributes to both academic discourse and policy design by clarifying the role of HRM in supporting workforce stability in crisis-prone economies.

AI in Recruitment Enhancing Efficiency or Replacing Human Judgement

The rapid advancement of Artificial Intelligence (AI) technologies is reshaping traditional business processes, and recruitment is no exception. As organizations strive to streamline hiring and improve talent acquisition outcomes, AI-driven recruitment tools have gained significant momentum. From résumé parsing and predictive analytics to chatbots conducting initial candidate screening, the application of AI in recruitment has ushered in unprecedented levels of speed, accuracy, and data handling capabilities. This research paper critically examines the dual-edge nature of AI in recruitment — exploring whether it genuinely enhances hiring efficiency or threatens to supplant the nuanced judgment and empathy inherent to human recruiters. The study begins by mapping the current landscape of AI tools in recruitment, highlighting leading applications such as applicant tracking systems (ATS), machine learning algorithms for skills matching, and AI-based video analysis software for behavioral assessments. Through case studies across multiple industries — including technology, finance, and healthcare — the research investigates how AI has transformed operational aspects like time-to-hire, cost-per-hire, and quality-of-hire metrics. These quantitative improvements are then juxtaposed with qualitative insights gathered from HR professionals and candidates, exploring their perceptions of fairness, transparency, and trust in AI systems. A core focus of the research lies in interrogating the limitations and ethical challenges of AI recruitment. The paper identifies key risks, such as algorithmic bias, lack of contextual understanding, over-reliance on historical data, and the reduction of candidate evaluation to quantifiable metrics. The concern that AI may inadvertently replicate existing prejudices or disregard unique human potential — traits that seasoned recruiters might otherwise detect — is explored in depth. Furthermore, the paper discusses regulatory and legal implications, especially in the context of data privacy laws and equal employment opportunity standards. It also evaluates the extent to which AI can augment human decision-making rather than replace it, proposing a hybrid model where AI handles repetitive, data-intensive tasks while human recruiters focus on interpersonal evaluation, cultural fit, and strategic talent alignment. This paper concludes that while AI presents transformative opportunities for recruitment efficiency, its optimal utility lies not in replacing human judgment but in reinforcing it. Responsible integration, combined with ethical oversight and human-AI collaboration, can create a more inclusive, agile, and data-informed recruitment ecosystem that serves both organizations and job seekers effectively.

Addressing Algorithmic Bias in AI‐Driven HRM Systems: Implications for Strategic HRM Effectiveness

ABSTRACTAI and machine learning algorithms are revolutionising the modern workplace by transforming HR functions to deliver superior outcomes for both employees and organisations. However, research shows that these algorithms often fail to deliver optimal HR solutions, primarily due to inherent biases. Developing capabilities to overcome algorithmic biases is critical for firms, as these biases present significant challenges to fairness and inclusivity in HR decision‐making, ultimately impacting the effectiveness of HR practices. To address this challenge, our study, grounded in the dynamic capability perspective, presents a model to address algorithmic biases in people management and achieve superior strategic HR outcomes. To test our theoretical model, we collected survey data using a two‐wave, time‐lagged approach from HR professionals and employees working in firms within the Australian financial and insurance industries. The key findings reveal three critical dimensions of HR algorithmic bias management capability: data bias, model bias, and deployment bias management capabilities, which significantly influence AI‐enabled high‐performance HR practices and, in turn, positively impact strategic HRM effectiveness. Our novel findings on the dimensions of HR bias management capability contribute to advancing the dynamic capability view in HRM research. They also offer a comprehensive bias management framework that allows HR professionals to address the strategic, ethical, and operational challenges emerging from the use of AI‐augmented HR practices in the dynamic workplace, helping sustain a competitive advantage.

Enhancing Fairness in HR Recruitment: A Hybrid AI-DSS Model vs. Traditional Methods Evaluated with DIR and EOD Metrics for Effective Recruitment

The implementation of Artificial Intelligence-based Decision Support Systems (AI-DSS) in recruitment has significantly enhanced efficiency; however, concerns regarding algorithmic bias persist. Existing AI-DSS models primarily emphasize explicit data, often neglecting psychological and behavioral factors essential for fair recruitment. This study integrates Person-Job Fit and Person-Organization Fit theories into AI-DSS while employing adaptive learning techniques to mitigate bias. Using a mixed- methods approach with an explanatory sequential design, this research combines quantitative analysis (statistical comparisons of AI-DSS and traditional hiring methods, bias evaluation using fairness metrics) with qualitative insights (interviews with HR professionals and candidates). The findings indicate that AI-DSS improves selection efficiency and candidate performance yet remains susceptible to biases derived from historical data. Adaptive learning enhances fairness; however, ethical concerns about transparency and accountability persist. This research strengthens the AI recruitment debate by suggesting a comprehensive model that balances the operational efficiency, ethical needs, and fair practices. Explaining AI paradigms requires additional research to establish trust and flexibility for AI recruitment systems.

Artificial Intelligence in HR: Examining the Use of AI in Recruitment, Performance Evaluation, and Employee Development Including Ethical Considerations

The adoption of Artificial Intelligence in Human Resource procedure is transforming the management of staffing, performance assessment, and facilitating professional development. With rising data conversion of the global business ecosystem, HR operations are adopting AI technologies to enhance efficiency, precision, and strategic decision making. The implementation of AI also presents formidable ethical challenges related to bias, transparency, privacy, and responsibility. This research aims to locate the challenging position of AI in HR with particular focus on three vital aspects: recruitment, performance management, and employee development, and also addressing the ethical aspects related to the use of AI in the mentioned processes. The primary objective of this research is to look into the extent to which AI technologies are utilized by HR professionals at present and assess the seeming advantages and drawbacks of their usage. For the purpose of guiding this research, two different objectives are formulated, along with two questions and two hypotheses. The study uses a quantitative study design with survey method for collecting data from HR practitioners in various organizations in the public and private sectors. Purposive sampling is used to collect a representative sample of 150 respondents with an assurance of having hands on experience with AI tools being used in HR practice. Data are collected using a standardized questionnaire and analyzed using descriptive statistics. Results are shown by the utilization of pie charts in graphically depicting the distribution of responses across key variables. The findings indicate that AI is increasingly being applied within the recruitment processes in the guise of automated resume screening, chat bots for participants' communications, and estimated analytics for matching employees. With these significance, respondents nevertheless worry about ethical concerns such as algorithmic bias, cloudiness of decision making, and potential subversion of employee autonomy and privacy. The research affirm the hypothesis that AI has notable impact on HR functions but also determine that ethical considerations play a critical part in influencing its uptake and effectiveness. The research finds that AI is extremely hopeful to increase HR results, but its effective use depends on establishing ethical safe, transparent algorithms and constant human monitoring. It is proposed that HR departments establish special policies, spend on personnel AI literacy programs, and perform regular checks of AI systems.

Strategic drivers of AI-based recruitment system adoption in organizations

This study explores the fundamental strategic drivers of organizations adopting AI-based recruitment systems. It introduces novel insights into the factors leading to AI adoption and use in HRM as AI technology continues to evolve. Employing an integrated framework consisting of the Technology-Organization-Environment (TOE) model and the Technology Acceptance Model (TAM), the research investigation outlines critical technological, organizational, and environmental factors influencing the intent to adopt. This quantitative research design used data obtained through a survey of HR and IT professionals from various industries. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed model, we examined the strength and significance of the hypothesized relationships. The results show that technological readiness, top management support, perceived usefulness, and external pressure explain adoption intent. The takeaway from these findings is the strategic importance of collaborating innovation with organizational capacity and environmental factors. The study adds to the emerging knowledge on digital transformation within HRM. It gives practitioners and policymakers practical insights into using AI technologies to improve recruitment processes. The research emphasizes essential adoption enablers and enables informed decision-making and strategic planning regarding AI integration.

Exploring emotional intelligence and conflict management through a systematic literature review and bibliometric analysis

PurposeEven though many studies have highlighted the importance of emotional intelligence and conflict management in an organization, only a few scholars have systematically investigated the integrated knowledge landscape of this area. Therefore, this paper aims to comprehensively review emotional intelligence (EI), emotional intelligence measures, and conflict management through a systematic literature review.Design/methodology/approachThe selected 122 articles published between 1995 and 2023 are analysed in two phases. In the first phase, a bibliometric analysis is conducted using CiteSpace and VOSviewer, and in the second phase, a detailed analysis based on the results is done. VOSviewer was used to perform bibliometric coupling of journals and nations, while CiteSpace was used for keyword and author analysis to identify potential research areas.FindingsThe keyword co-occurrence analysis of 122 articles using CiteSpace resulted in eight research clusters representing research areas related to individual outcomes, organizational outcomes, measurement scales of EI and conflict management, moderators and mediators, conflict management styles, type of conflicts and research methods. It is observed that the EI and conflict management research mainly focused on specific groups or industries, and a generalized framework is limited in the literature.Research limitations/implicationsThere are a few minor limitations to this study. Due to the fact that this study solely considered English-language articles, we may have left out some of the literature. This analysis takes only journal articles into account, and the search is limited to the literature in Scopus databases. It would be ideal to have more different publications, like books, reports, conference papers, etc., taken into account in the future in order to obtain a more complete picture of knowledge development. Despite these limitations, this paper aims to shed light on the research on emotional intelligence, conflict management, and emotional intelligence measurement scales.Practical implicationsThis study, based on existing research in emotional intelligence and conflict management, two rapidly expanding fields, has major implications for researchers, HR professionals, managers, policymakers, academicians, and society. It also holds significant economic and commercial implications for organizations. The research highlights the importance of EI training, particularly for managers, to improve their conflict management skills. Moreover, the study emphasises the importance of HR professionals in developing better empathetic and employee-focused strategies for effective conflict management. The bibliometric analysis in this research offers insights into prevailing research methodologies, publication trends, and key contributors in the field. The insights and research gaps identified in this study pave the way for exciting future research that can contribute to a more empathetic, emotionally aware society, reducing the negative impacts of unmanaged conflicts in various social settings.Originality/valueThis review is the first attempt to systematically examine emotional intelligence and conflict management knowledge structure by adhering to PRISMA guidelines and visually analysing the literature using VOS viewer and Cite Space software. The results provide the major research areas and highlight the gaps and potential future research directions.

Application of Competency Model Among Selected Educational Enterprises in China: A Proposed Pipeline Development Strategies

This dissertation examines the application of the competency model in Chinese educational enterprises, focusing on its role in talent pipeline development—encompassing talent selection, development, and retention. The study aims to assess the effectiveness of competency-based frameworks in aligning human capital strategies with organizational goals in the education sector. A quantitative research design was employed, utilizing stratified random sampling to collect data from five leading educational enterprises. The sample size of 250 respondents was determined using Raosoft statistical analysis, ensuring robust statistical power for hypothesis testing. Descriptive statistics, ANOVA, and multiple regression analysis were applied to evaluate competency model applicability and its impact on talent management outcomes. Findings indicate that competency model application significantly influences talent pipeline development, with job-specific knowledge, adaptability, and communication competencies playing a crucial role in shaping recruitment, training, and retention strategies. The study also identifies demographic variations in competency model effectiveness, emphasizing the need for tailored HR interventions. Regression analysis results confirm a strong predictive relationship between competency model applicability and talent development outcomes, reinforcing the strategic value of competency-driven HR frameworks in educational enterprise. Based on the findings, this dissertation proposes a competency-driven pipeline development model, offering a structured approach to enhancing talent acquisition, workforce development, and retention strategies in the Chinese education sector. The study contributes both theoretically—by enriching the literature on competency-based talent management—and practically, by providing data-driven recommendations for HR professionals and educational organizations seeking to optimize workforce sustainability and long-term competitiveness.

<b>Exploring Canadian Organizational Behaviour in SMEs: A Qualitative</b> <b>Study in Ottawa, Ontario</b>

This study investigates organizational behaviour (OB) within small and medium enterprises (SMEs) located in Ottawa, Ontario, Canada. Emphasizing a qualitative research approach, the study is grounded in 62 face-to-face interviews with managers and employees across various sectors, including retail, technology, healthcare, education, and hospitality. The research uncovers critical behavioural dimensions—such as inclusive leadership, communication openness, cultural diversity, and work-life balance—that define workplace interactions in these enterprises. By conducting a thematic analysis, the study presents how Canadian societal values and multicultural frameworks influence SME dynamics, productivity, and staff engagement. This research contributes to the limited qualitative OB literature on Canadian SMEs and offers practical implications for managers, policymakers, and HR professionals aiming to improve organizational culture and effectiveness.

Conversational AI in Talent Acquisition: The Role of Chatbots in Candidate Engagement and Pre-Screening

Abstract- Artificial Intelligence (AI) is transforming recruitment by automating time-consuming processes and enhancing candidate experience. This study examines the role of AI-powered chatbots in candidate engagement and pre-screening during the recruitment process. Through a qualitative analysis of existing literature and corporate case studies, the paper explores how chatbots improve response time, standardize applicant screening, and personalize candidate communication. While the benefits are clear in terms of efficiency and scalability, the paper also addresses concerns regarding data privacy, algorithmic bias, and impersonal interactions. The findings provide strategic insights for HR professionals looking to integrate conversational AI into talent acquisition workflows. Keyword - AI in HRM, Chatbots, Candidate Pre-Screening, Conversational AI, Recruitment Automation, Talent Acquisition, HR Technology.

The Moderating Role of Emotional Intelligence in the Relationship Between Employee Resilience, Perceived Organizational Support, and Work Engagement: A Multi-Sector Study in Saudi Arabia

Employee engagement plays a crucial role in organizational success, influencing productivity, retention, and overall workplace performance. This study examines the impact of employee resilience and perceived organizational support (POS) on work engagement, with emotional intelligence (EI) as a moderating factor, across multiple sectors in Saudi Arabia. Grounded in the Job Demands-Resources (JD-R) model, the study hypothesizes that resilience and POS positively influence engagement, while EI moderates these relationships by enhancing employees’ ability to leverage resilience and support effectively. A quantitative research approach was employed, using a structured survey distributed to 450 full-time employees across industries such as healthcare, education, finance, manufacturing, and IT. Data were analyzed through structural equation modeling (SEM) to assess the relationships among the variables. The findings confirm that employee resilience and POS significantly enhance work engagement, supporting the direct effects. Additionally, EI moderates these relationships, indicating that employees with higher emotional intelligence are better equipped to utilize resilience and organizational support to sustain engagement. These findings contribute to Saudi Vision 2030, emphasizing workforce development and employee well-being. The study provides practical insights for HR professionals on fostering engagement through resilience training, supportive workplace policies, and emotional intelligence development programs.

AI-BASED BODY LANGUAGE ANALYSIS FOR INTERVIEW FEEDBACK

ABSTRACT: Body language is a vital communication component, particularly in job interviews, where non-verbal cues significantly influence a candidate’s perception and evaluation. This project proposes an AI-powered system that analyzes candidates’ body language during interviews to deliver structured feedback. Leveraging computer vision and machine learning, the system evaluates facial expressions, gestures, posture, and eye contact to assess confidence, engagement, and professionalism. By processing video inputs, it extracts behavioral patterns and generates personalized, data-driven insights to help candidates improve their non-verbal communication. The system benefits job seekers, HR professionals, and training institutions by offering unbiased, automated feedback to identify strengths and areas for improvement—promoting more effective interview preparation and decision-making. Keywords: Body Language, Interview Feedback, Machine Learning, Computer Vision, Posture Analysis, Facial Expression Recognition.