The present study aims to develop a qualitative model of the interactions and outcomes of artificial intelligence-based human resource management (AI-HRM). In terms of purpose, the research is exploratory, and in terms of implementation, it adopts a qualitative approach. The study population consisted of 18 university scholars, managers, and human resource management experts who were selected through purposive sampling. Data were collected using semi-structured interviews and analyzed through thematic analysis. The findings led to the identification of 183 codes, categorized into 28 basic themes and 27 organizing themes. The results indicated that AI-based human resource management comprises four principal dimensions: interactive algorithms in the interview screening process, including the interactive enhancement of interviews, intelligent matching of applicants with organizational competencies, and interactive analysis of résumé data; dynamic performance appraisal, including interactive evaluation of work quality, interactive prediction of long-term performance, and automated continuous feedback; employee interaction with the learning and development system, including interactive career path recommendation, adaptive learning content, and soft-skills simulation; and interactive human resource planning systems, including workforce estimation, turnover intention prediction, and improvement of HR processes. These four dimensions directly affect three major outcomes: operational productivity, with emphasis on automating administrative decrees and reducing leave-processing time; improvement in HR decision-making, with emphasis on the unbiased standardization of interviews and monitoring of behavioral data; and human resource value creation, with emphasis on shared value creation and alignment with strategic objectives.