Blueprint for Structured Group Discussion Preparation

Group discussions (GDs), valued for assessing leadership, communication, and teamwork, remain a core component of recruitment and assessment centre processes. With advances in artificial intelligence (AI), traditional observer-based evaluations are increasingly supplemented by automated scoring, anomaly detection, and real-time feedback systems. This chapter examines the intersection of AI and GD assessment by synthesizing insights from 21 academic, professional, and industry sources, organized around five themes: the validity and reliability of GDs; AI-enabled scoring and anomaly detection; legal, ethical, and fairness concerns; candidate experience and trust; and AI-assisted preparation and training. The discussion introduces the DPAC (Definition, Positive Aspect, Anecdote, Conclusion) framework developed by Prof. Ranu Srivastava and Dr. Nrashant Singh as a structured, AI- and human-evaluable approach to GD participation that enhances clarity and engagement. The chapter concludes by identifying research gaps, practical implications, and strategies for aligning academic preparation with evolving industry expectations.