Inadequate conflict resolution skills in automotive engineering students can have negative consequences in the workplace. The development of mathematical logical thinking can help students develop critical analysis skills, improve problem-solving ability, develop reasoning skills, and effective communication, enabling them to deal effectively with conflicts and find creative solutions. This research aims to identify predictors of problem-solving ability using classification algorithms. Methodology: In this study, three classification algo-rithms were applied and the KDD process was used to identify predictors of problem-solving ability. The data set includes 60 records of students from the automotive engineering program at Universidad Equinoccial in Quito, Ecuador, to whom three tools were applied: a sociodemographic card, a Shatnawi test related to mathematical logical thinking, and a Watson Glaser test on conflict resolution ability. Results: The best classification model is the K-nearest neighbors’ algorithm and its predictive ability is very good, with a true positive rate versus false positive rate AUC of 0.75, along with a good performance in classifying negative cases. The model can be improved with increased sampling, cross-validation, or hyper-parameter adjustment. Conclusion: Age and mathematical logical thinking are strongly associated with conflict resolution ability. In future research it is important to consider additional variables such as experience in problem-solving projects, technical knowledge and communication skills; to explore the use of more advanced machine learning algo-rhythms; to design specific educational interventions based on the development of mathematical logical thinking; or to compare conflict resolution ability between different engineering disciplines.
