Assessment of the Impact of Artificial Intelligence on College Student Learning Based on the CRITIC Method
Xinyu Huang, Yiwei Zhang, Xunheng Lyu · 2023
This paper investigates the impact of artificial intelligence (AI) on college students' learning. It employs a questionnaire survey to collect data and establishes an evaluation index system. The survey data is visualized through charts and analyzed for correlations with students' characteristics and responses. Single-choice questions are quantified using positive values, while multiple-choice questions use a numerical accumulation approach. The survey questionnaire is divided into four key indicators: autonomous learning ability, learning motivation, learning efficiency improvement, and learning progress. These indicators are rigorously analyzed for correlation and are found to be effectively uncorrelated, forming a robust evaluation index system. The CRITIC analysis method assigns objective weights to these indicators (0.2118, 0.1924, 0.3540, and 0.2419, respectively) for each survey sample. A group decision-making model is applied to explore AI's impact on students' learning. This study offers valuable insights into the influence of AI on college students' learning experiences, providing a foundation for educational decision-making to enhance the overall education system and improve students' learning outcomes.