Influence of AI-driven Inquiry Teaching on Learning Outcomes

Abstract

In the field of educational informatization, the integration of information technology with education and teaching is deepening. Rich information technologies, such as artificial intelligence (AI), have provided efficient support for optimizing the teaching process and improving teaching quality. Inquiry teaching aims to cultivate students’ learning abilities in all aspects. AI can assist teachers in organizing effective inquiry activities, formulating scientific explanations, highlighting the relationship between problems and assumptions, and utilizing empirical evidence to solve related problems, thereby enhancing the teaching effectiveness of the course. In this study, we comprehensively examined the teaching process of inquirybased teaching. We analyzed the impact of four components of AI-driven inquiry teaching (questioning, evidence acquisition, explanation focus, and evaluation summary) on learning outcomes. Additionally, we investigated the variations in learning outcomes resulting from college students’ familiarity with artificial intelligence. Results show that the Cronbach’s α coefficient of the questionnaire is 0.863 and the KMO value is 0.865. The four components of inquiry-based teaching, namely questioning, evidence acquisition, explanation focusing, and evaluation summary, have been found to enhance learners’ learning outcomes by 10%, 5%, 1%, and 10%, respectively. The level of familiarity of college students with AI displays a significance level of 0.05 (F = 2.682, p = 0.032). The study results have significant reference value for analyzing the appeal of AI-driven education and teaching reform, summarizing the process of AI-driven inquiry teaching, and assisting teachers in using AI technology to enhance classroom teaching and improve teaching effectiveness.