The best research often begins with questions that do not yet have answers.
The AI Research Sandbox is designed as a collaborative space for interdisciplinary thinking, intellectual experimentation, and early-stage research exploration. Participants are encouraged to bring emerging ideas, unresolved puzzles, and bold questions to engage with scholars and practitioners across domains.
This is a space to think before you write — to challenge assumptions, discover new perspectives, and shape meaningful research agendas at the intersection of AI, organizations, and society.
This panel explores the evolving role of AI in education, examining how institutions can balance innovation with responsible governance. Experts will discuss policy frameworks, ethical considerations, data responsibility, and the integration of AI tools to enhance learning while preserving human connection, creativity, and academic integrity.
This panel examines the rise of AI agents and their impact on human decision-making, autonomy, and professional judgement. Thought leaders will explore whether increasing reliance on intelligent systems enhances human potential or risks diminishing critical thinking, accountability, and the uniquely human capabilities required in complex environments.
This workshop critically examines how AI is reshaping research conceptualization, challenging traditional notions of originality, authorship, and responsibility. It invites scholars to rethink intellectual ownership as an epistemic practice within hybrid human-machine reasoning processes.
The conference will feature practical sessions on using AI as a teaching co-pilot, designing strategic prompts that strengthen critical thinking and collaboration, and building campus-wide AI literacy frameworks for both students and faculty.
As a part of RCDC, this workshop guides doctoral scholars and early career researchers in systematically integrating theory into empirical research; demonstrating how patterns, anomalies, and contextual findings inform theory selection, refinement, and extension in rigorous empirical research.
