From Strategy to Judgment: Developing Contextual Judgment through AI-Assisted Reflective Negotiation Learning

DOI: https://doi.org/10.62517/jhet.202615511

Author(s)

Min Li, Nannan Yang

Affiliation(s)

Beijing Institute of Fashion Technology, Beijing, China

Abstract

Negotiation pedagogy has traditionally treated strategy as its core product: curricula deliver frameworks, tactics, and preparation heuristics, and students rehearse them in role-play simulations followed by instructor-led debriefing. Yet research on negotiation and on expert performance suggests that the binding constraint on negotiator effectiveness is not strategic knowledge but contextual judgment, the calibrated ability to read a situation, select among known moves, and revise one's approach in real time. This conceptual paper argues that judgment develops through reflective cycles in which experience is converted into calibrated intuition, and that AI-assisted reflective learning offers a scalable mechanism for compressing those cycles in negotiation education. Drawing on the expertise literature on deliberate practice and intuitive judgment, the feedback literature, and emerging evidence on large language models in education, the paper develops a framework in which AI systems function as reflective partners that capture decisions, generate counterfactuals, pose Socratic questions, and audit judgment patterns. Five design principles for curriculum integration and the principal risks of over-reliance, validity, and authenticity are discussed. The argument redirects negotiation education from the transmission of strategy toward the deliberate cultivation of judgment.

Keywords

Negotiation Education; Contextual Judgment; Reflective Learning; Large Language Models; Expertise Development

References

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