Influence of AI-driven Inquiry Teaching on Learning Outcomes
DOI:
https://doi.org/10.3991/ijet.v18i23.45473Keywords:
AI-driven inquiry teaching, learning outcome, questionnaire technique, analysis of varianceAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Xiaofang Xie
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.