Exploring Decision-Making Skills With the ChatGPT-Enhanced Decision Tree Interactive Learning Model

Abstract

This study explores the impact of the ChatGPT-enhanced Decision Tree Learning Model on the decision-making skills of Chinese international students. By integrating artificial intelligence with traditional decision tree methods, the model offers an interactive, personalized learning experience to help students navigate complex decision-making scenarios. The study involved 80 students from a university in Thailand, who were randomly assigned to either the experimental group using the ChatGPT model or the control group using traditional teaching methods. A pre-test and post-test design, along with self-assessment tools, were used to evaluate students' decision-making skills. The results showed significant improvements in the experimental group in terms of systematic thinking, autonomous adaptability, and feedback-driven optimization. These findings support the effectiveness of AI-driven models in enhancing key decision-making skills and are consistent with existing research on the role of technology in skill development. The study concludes that the ChatGPT-enhanced model is a valuable tool for developing decision-making abilities in international students, but further research is needed with extended interventions and diverse samples to explore its long-term impact and broader applicability.

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Author Information
Yanli Miao, King Mongkut’s Institute of Technology Ladkrabang, Thailand
Kanyarat Sriwisathiyakun, King Mongkut’s Institute of Technology Ladkrabang, Thailand
Thanin Ratanaolarn, King Mongkut’s Institute of Technology Ladkrabang, Thailand