Editorial: The future of research on artificial intelligence in conflict management

摘要

The unprecedented development and rapid growth in popularity of ChatGPT models have led the world to recognize AI’s importance, relevance and utility. Driven by the confluence of increased big data availability, advances in natural language processing (NLP) and enhanced computing power, AI has generated levels of excitement and investment that few technological innovations have achieved.Scholars in many fields are beginning to integrate AI to assist and augment their research. Within the field of conflict management, research on AI is emerging. As is often the case, researchers trained in one discipline are reluctant to adopt and integrate new paradigms from other fields. However, AI has great potential to drive significant advances in our understanding of conflict management. Therefore, this editorial helps to bridge the gap between AI and conflict management research by providing an explication of key AI paradigms and illustrating how they can be usefully adopted and integrated into our field. This results in a vision for a better future in which conflict management scholarship advances more rapidly and in more profound and sophisticated ways.Based on a careful review of the literature in AI, conflict management and related fields, five AI paradigms were identified. These paradigms are Machine Learning, Generative AI, AI Agents, Agentic AI and Physical AI (Hofmann and Kruhse-Lehtonen, 2025). Each of these five paradigms presents a critical area for integrating AI into conflict management research. Below, each paradigm is discussed. This discussion includes definitions, an overview of mechanisms, examples and future research questions.Machine Learning (ML) is a process by which systems analyze data and learn how to improve (Cummins and Jensen, 2024). ML goes beyond aggregating data; it also learns from data. This paradigm differs from prior efforts to create computer-based expert systems designed by humans to facilitate decision-making and prediction. Instead, ML identifies and learns patterns in the data to make predictions. ML can use unsupervised, supervised or reinforcement learning. After learning, ML enables data-driven prediction. Instead of being given rules of reasoning and knowledge from humans, the system is instructed to discover its own rules to follow by identifying patterns and correlations in large data sets. It often creates its own rules to make predictions that can be applied to new cases. ML has become increasingly valuable and important because of the growing accessibility of big data, large language models (LLMs), advances in NLP, the sophistication of software and the rapid growth of computing speed and capacity.For example, some recent research indicates that ML can predict whether a conflict will be violent or non-violent (Hundt et al., 2025; Lata and Garg, 2023). However, we should recognize that ML may not lead to the more sophisticated and nuanced judgments, creativity and human intuitions that experienced conflict management scholars and practitioners possess. We cannot always rely on ML to properly analyze and assess conflicts. Therefore, future research should examine the risks of using ML for conflict management while integrating human insight and oversight (Cummins and Jensen, 2024).This leads to several new and interesting research questions. For example, ML uses historical LLM training data to generate and create rules that summarize historical data on conflicts. Will this impair the identification of potentially novel trajectories and solutions for future conflicts? Can ML techniques accurately predict conflict styles and outcomes in volatile and changing environments? Are there biases built into ML models that could impair the effectiveness of conflict management for some groups or in some contexts? These are just a few examples of how scholars could integrate the ML paradigm to conduct studies that would generate significant new insights in conflict management.Generative AI is a process i