# Teamwork Conflict Management Training and Conflict Resolution Practice via Large Language Models
## 4 Conflict Management Training and Practice
### Operationalizing Transformative Learning Pedagogy
#### 4.2.2 Step 2: Practice
Examining these deviations allows for the identification of potential gaps in the training approach and the development of targeted interventions to better equip students to navigate challenging scenarios. Future training could involve additional practice in high-pressure simulations, explicit coaching on emotional regulation techniques, or post-interaction reflections to help students recognize and learn from instances where they strayed from best practices. Discussing these deviations openly can help normalize the ongoing learning process inherent in developing conflict resolution skills and emphasize the importance of continuous self-reflection and improvement. ### Implications for Conflict Management Training

The implications for teaching and learning relate to the integration of a transformative learning approach that combines conflict management training with practice and concludes with reflection. This approach guided our learning design, aiming to engage students in reflective practices based on their experiences [57]. However, the existing literature presents mixed views on the effectiveness of conflict management training in enhancing individuals' abilities to manage and resolve conflicts [77]. Studies have pinpointed more effective training methods that involve educational activities aimed at improving interpersonal communication and conflict negotiation skills [78]. The novelty of our approach is that it provided students with some level of practice to apply taught strategies. Through this practice and the subsequent reflection on negotiation outcomes, students could assess the efficacy of their applied strategies. Therefore, training and practice in conflict management can provide the individual with an improved sense of control over the conflict and confidence in handling it, increasing the chances of reaching an amicable resolution. However, the use of LLMs in simulating human conversations comes with limitations [79]. One significant challenge is the lack of emotional depth and non-verbal cues that are crucial in human communication [80]. LLMs currently do not possess the ability to fully replicate the emotional nuances of human interactions, which can lead to a gap in learning experiences where understanding emotions plays a crucial role. Additionally, the interactions with LLMs can sometimes be too structured or predictable, which may not adequately represent the complexity and unpredictability of real-life human interactions [81]. This limitation could potentially lead to a learning outcome where students are well prepared for simulated scenarios but are less prepared for the dynamism of actual human conflicts. Despite these limitations, LLMs can serve as valuable tools for practicing conflict management by allowing students to refine their strategies in a risk-free environment. Thismethod enhances confidence and preparedness for real-life conflicts while fostering critical thinking and problem-solving skills essential for effective conflict resolution. ### Ethical Implications of the Study

The use of AI, particularly large language models (LLMs), in educational settings raises important ethical considerations [41]. While the study demonstrates the potential of LLMs to provide immersive, personalized conflict resolution training, it is crucial to address the broader implications of relying on these technologies. One concern relates to the privacy and security of the users [82]. Educators are recommended to instruct students not to share any personal or confidential details with the LLM during their interactions to mitigate these concerns. The potential for LLMs to perpetuate or amplify societal biases is another significant issue [82; 83]. Efforts should be made to create neutral, broadly applicable conflict scenarios, and the training data used to develop the LLM should be carefully evaluated and refined iteratively through thorough testing to minimize biases.