This research critically examines the transformative role of AI chatbots in recruitment compared to traditional methods, with specific focus on candidate experience. Through systematic analysis of existing literature and industry case studies, the study reveals that AI chatbots reduce recruitment time by 43 and costs by up to 50 while providing 24 7 candidate engagement. However, significant challenges persist including algorithmic bias, lack of human empathy, and transparency issues. Traditional recruitment methods, while offering personal interaction, remain time consuming and prone to human bias. The study synthesizes findings from three foundational research papers spanning 2021 2025 to identify technological growth patterns, including advancements in Natural Language Processing, predictive analytics, and conversational AI. Case studies from Indian organizations including Infosys, Reliance Industries, and Flipkart demonstrate successful AI implementation, with Unilever reporting 75 reduction in hiring time. The research identifies critical research gaps including missing candidate centric perspectives, cross cultural insights, and underexplored hybrid models. Findings suggest that optimal recruitment outcomes require balanced integration of AI efficiency with human judgment, supported by ethical oversight and continuous bias auditing. The study concludes with recommendations for developing hybrid recruitment frameworks and global ethical standards for AI powered hiring. AI Chatbots, Traditional Recruitment, Candidate Experience, Algorithmic Bias, Hybrid Recruitment Model, Natural Language Processing, Recruitment Automation, Conversational AI, Ethical AI, Talent Acquisition, Predictive Analytics, Candidate Engagement, Human Judgment, Recruitment Technology, Bias Mitigation. 30-34 International Conference on Reimagining Management-Sustainability and Innovation in the AI Era Special Issue Prabhpreet Kaur Nagpal