Analysis of the Effectiveness of Using AI-Generated Instructional Videos through Problem-Based Learning Model on Reconstruction of Fundamental Science Concepts
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The use of artificial intelligence (AI) has grown in elementary schools, but insufficient is known about the practical consequences of this technology, particularly as it pertains to the acquisition of scientific knowledge. Given that pre-service elementary teachers necessitate the cultivation of skills in intricate scientific ideas within problem-based learning (PBL) settings, there is an increasing demand for creative, technology-enhanced instructional resources. This study examines the influence of AI-generated instructional videos, created in accordance with recognized instructional design principles, on task performance, self-efficacy, and learning outcomes in pre-service elementary teachers. The present study utilized a within-subjects design with 186 participants, incorporating pretest, post-test, and transfer evaluations to measure the transferability and durability of learning. Substantial improvements were found from pre- to post-training, including a significant gain in self-efficacy (t(185)=7.12, p<0.001, d=1.04) and learning performance (e.g., immediate post-test t(185)=8.45, p<0.001). However, ANCOVA results indicated no significant advantage of the preview feature, such as in the delayed post-test (F(1,184)=0.65, p=.42) and transfer test (F(1,184)=0.18, p=.67). The results indicate that educational videos produced by artificial intelligence can substantially enhance knowledge transfer, retention, and self-efficacy, qualifying them as valuable resources for elementary teacher education in the field of elementary education.
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