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Cited by 56Visual and Cognitive Learning ProcessesArtificial Intelligence in EducationProblem and Project Based Learning

The Impact of AI-Generated Instructional Videos on Problem-Based Learning in Science Teacher Education

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Pellas, Nikolaos·2025·Education Sciences
DOI 10.3390/educsci15010102
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Abstract

Artificial Intelligence (AI) has gained significant prominence in science education, yet its practical applications, particularly in teacher training, remain underexplored. Specifically, there is a lack of research on AI’s potential to support personalized professional development through automated analysis of classroom interactions and tailored feedback. As science teacher education requires skill development in complex scientific concepts within problem-based learning (PBL) contexts, there is a growing need for innovative, technology-driven instructional tools. AI-generated instructional videos are increasingly recognized as powerful tools for enhancing educational experiences. This study investigates the impact of AI-generated instructional videos, designed using established instructional design principles, on self-efficacy, task performance, and learning outcomes in science teacher education. Employing a within-subjects design, the current study included pre-test, post-test, and transfer assessments to evaluate learning durability and transferability, consistent with design-based research methodology. Moreover, this study compares the effectiveness of two AI-generated instructional video formats: one with an embedded preview feature allowing learners to preview key concepts before detailed instruction (video-with-preview condition) and another without this feature (video-without-preview condition). It specifically examines the role of preview features in enhancing these outcomes during training on scientific concepts with 55 Greek pre-service science teachers (n = 55; mean age 27.3 years; range 22–35). The results demonstrated that the videos effectively supported self-efficacy, task performance, and knowledge retention. However, no significant differences were observed between videos with and without preview features across all assessed metrics and tests. These findings also indicate that AI-generated instructional videos can effectively enhance knowledge retention, transfer, and self-efficacy, positioning them as promising assets in science teacher education. The limited impact of the preview feature highlights the need for careful design and evaluation of instructional elements, such as interactivity and adaptive learning algorithms, to fully realize their potential.

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Authors
Pellas, N.
Affiliations
Department of Primary Education, University of Western Macedonia, 53100 Florina, Greece
ORCID
Pellas, Nikolaos
Venue
Education Sciences
Publisher
MDPI AG
Published
2025-01-18
Volume
15
Issue
1
Pages
102
ISSN
2227-7102 (ISSN-L)
DOI
10.3390/educsci15010102
OpenAlex
W4406624665
Cited by
56
References
53
Citations by year
2026: 34 · 2025: 22
FWCI
59.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Education Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Education › Problem and Project Based Learning
Keywords
problem-based learning, science teacher education, pre-service science teachers, self-efficacy, task performance, knowledge retention, transfer of learning, instructional design, personalized professional development

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