This chapter examines the transformative role of large language models (LLMs) in job interviews and human resource management by discussing their conceptual foundations, technical mechanisms, and practical applications. It highlights how rapid advances in AI and the rise of transformer-based architectures have reshaped recruitment processes. The chapter outlines three core uses of LLMs in interviews: question generation, candidate response analysis, and post-interview evaluation. Drawing on recent academic research, it summarizes findings related to efficiency gains, predictive validity, ethical concerns, candidate experience, and improved decision-making.

The analysis shows that LLMs accelerate interview workflows, enable scalable assessment, and provide personalized feedback to candidates. However, major risks—including bias, lack of explainability, data privacy challenges, and technical limitations—remain significant. For these reasons, fully removing human oversight from recruitment processes is deemed neither feasible nor advisable.

The chapter also explores the collaboration between HR professionals and LLMs, emphasizing that while these models can support evaluation tasks, they cannot replace human intuition, emotional intelligence, or contextual judgment. Finally, it discusses future prospects, suggesting that multimodal LLMs will enhance analysis by incorporating video and audio cues, yet ethical, transparency, and accountability concerns will continue to shape their responsible use.