How Can Video Generative AI Transform K-12 Education? Examining Teachers' Perspectives through TPACK and TAM

2025-03-11

AI Summary

This study investigates the effective integration of generative AI for video creation in K–12 education, addressing challenges including technical constraints, ethical risks, and insufficient institutional support. Grounded in the dual theoretical frameworks of Technological Pedagogical Content Knowledge (TPACK) and the Technology Acceptance Model (TAM), the research employs teacher semi-structured interviews and hands-on tool experimentation, complemented by TPACK competency mapping and TAM-based assessments of perceived usefulness and ease of use. It systematically elucidates teachers’ acceptance mechanisms and pedagogical adaptation logic. As the first study to integrate these frameworks, it identifies a critical synergistic pathway among pedagogical capacity, ethical awareness, and institutional support; uncovers five high-impact empowerment scenarios and three core challenge categories; and proposes actionable recommendations for teacher professional development, school-level support systems, and AI-in-education policy reform—thereby providing both empirical grounding and theoretical advancement for the responsible implementation of generative AI in education.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The rapid advancement of generative AI technology, particularly video generative AI (Video GenAI), has opened new possibilities for K-12 education by enabling the creation of dynamic, customized, and high-quality visual content. Despite its potential, there is limited research on how this emerging technology can be effectively integrated into educational practices. This study explores the perspectives of leading K-12 teachers on the educational applications of Video GenAI, using the TPACK (Technological Pedagogical Content Knowledge) and TAM (Technology Acceptance Model) frameworks as analytical lenses. Through interviews and hands-on experimentation with video generation tools, the research identifies opportunities for enhancing teaching strategies, fostering student engagement, and supporting authentic task design. It also highlights challenges such as technical limitations, ethical considerations, and the need for institutional support. The findings provide actionable insights into how Video GenAI can transform teaching and learning, offering practical implications for policy, teacher training, and the future development of educational technology.

Problem

• Explores Video GenAI integration in K-12 education
• Examines teacher perspectives using TPACK and TAM frameworks
• Identifies opportunities and challenges for educational transformation

Innovation

• Video GenAI enhances teaching strategies dynamically.
• TPACK and TAM frameworks analyze teacher perspectives.
• Hands-on experimentation identifies engagement and task design opportunities.