How Can Video Generative AI Transform K-12 Education? Examining Teachers' Perspectives through TPACK and TAM
Project Overview
The document explores the transformative potential of Video Generative AI (GenAI) in K-12 education, focusing on its ability to create dynamic, customized visual content that enhances teaching strategies, student engagement, and authentic task design. It examines teachers' perspectives on the educational applications of Video GenAI through the TPACK and TAM frameworks, revealing opportunities for personalized learning and creative expression while also acknowledging challenges such as technical limitations, ethical concerns, and the necessity for institutional support. Additionally, it discusses various applications of generative AI in education, including video content creation, teacher training, and improving student engagement, underscoring the importance of digital competence among educators. The document identifies barriers to effective implementation, such as technical issues and educators' resistance to change, highlighting the need for ongoing support and resources to fully leverage the capabilities of generative AI in educational settings. Overall, the findings suggest that while generative AI holds significant promise for enhancing educational experiences, careful consideration of the challenges is essential for successful integration.
Key Applications
Generative Video Content for Enhanced Learning
Context: Applied in K-12 education across various subjects to enhance student engagement and understanding through video content generation.
Implementation: Teachers and educators utilize Video Generative AI tools and prompts to create engaging video content from scripts or educational materials, aimed at illustrating complex ideas and supporting diverse learning styles.
Outcomes: ['Increased student engagement', 'Improved understanding of complex concepts', 'Higher retention of information', 'Enhanced teaching strategies', 'Fostered personalized learning experiences']
Challenges: ['Technical limitations and difficulties with video generation tools', 'Dependence on technology', 'Need for teacher training and institutional support', 'Potential for content bias']
AI-Assisted Teaching Support
Context: Used in various educational settings to assist educators with lesson planning and instructional strategies.
Implementation: Educators leverage ChatGPT as a resource for generating lesson plans, teaching strategies, and answering pedagogical questions, enhancing their instructional quality and confidence.
Outcomes: ['Enhanced lesson preparation', 'Increased teacher confidence', 'Improved instructional quality']
Challenges: ['Concerns over content accuracy', 'Reliance on AI for teaching material development', 'Need for ongoing professional development']
Implementation Barriers
Technical barrier
Performance limitations such as slow rendering times, quality inconsistencies in generated videos, language support issues, and dependence on stable internet and technical infrastructure for effective AI implementation.
Proposed Solutions: Developing advanced technologies to improve the performance and quality of Video GenAI outputs, investing in reliable technology, and providing training for educators to effectively utilize AI tools.
Policy barrier
Stringent age restrictions and parental consent requirements complicate the adoption of Video GenAI in educational settings.
Proposed Solutions: Advocating for policy adjustments that allow equitable access to AI tools across various educational levels.
Ethical barrier
Concerns regarding data privacy, algorithmic bias, and the potential for generating inappropriate content, as well as bias in AI-generated content and the potential for misuse.
Proposed Solutions: Implementing robust content filtering mechanisms, establishing clear ethical guidelines for the use of Video GenAI, and promoting transparency in AI operations.
Cultural Barrier
Resistance from educators to adopt new technologies due to lack of trust or comfort with AI.
Proposed Solutions: Implement professional development programs to increase AI literacy and address concerns.
Project Team
Unggi Lee
Researcher
Yeil Jeong
Researcher
Seungha Kim
Researcher
Yoorim Son
Researcher
Gyuri Byun
Researcher
Hyeoncheol Kim
Researcher
Cheolil Lim
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Unggi Lee, Yeil Jeong, Seungha Kim, Yoorim Son, Gyuri Byun, Hyeoncheol Kim, Cheolil Lim
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai