AI generation tools in art education have predominantly positive impacts on both professors and students, with benefits outweighing concerns when implemented thoughtfully with proper guidance and balance.
Objective: The main goal of this study was to investigate the impact of artificial intelligence generation tools on art education from the perspectives of both professors and students. Specifically, the researchers aimed to examine whether these tools have positive or negative effects, identify the specific beneficial and detrimental impacts, and explore how art education should evolve to better align with future development needs in an AI-integrated environment. The study sought to provide evidence-based recommendations for the responsible implementation of AI tools in art education while balancing technological advantages with potential risks.
Methods: The researchers employed a comprehensive mixed-methods approach conducted in two main stages. The first stage utilized qualitative research methods involving three phases: literature review and case studies of renowned art institutions worldwide (including School of the Art Institute of Chicago, Rhode Island School of Design, University of the Arts London, Glasgow School of Art, Central Academy of Fine Arts, and China Academy of Art), semi-structured interviews with stakeholders, and thematic coding analysis. The literature review phase examined curriculum arrangements across different educational systems and tested ChatGPT's accuracy in providing course information, ultimately selecting ChatGPT-4.0 for course recommendation generation. In the interview phase, 8 professors and 8 students from various art institutions participated in individual 40-minute sessions exploring their experiences with AI tools. User journey mapping was employed to visualize participant interactions, and systematic thematic coding using grounded theory principles identified key factors. The second stage involved quantitative research using structural equation modeling (SEM) with SmartPLS 4 software. A structured questionnaire was developed based on qualitative findings, measuring 9 variables using 7-point Likert scales. Data were collected through snowball sampling via the Chinese platform "Wenjuanxing," resulting in 428 valid responses from professors (48.6%) and students (51.4%). The study evaluated model fit, reliability, validity, and conducted multi-group analysis to compare perspectives between stakeholder groups.
Key Findings: The study revealed significant insights into AI tool impacts in art education through both qualitative and quantitative analysis. Qualitative findings showed that among interviewed participants, 5 out of 8 professors supported AI integration, 2 remained neutral, and 1 opposed it, while 6 out of 8 students expressed favorable opinions with 2 remaining neutral. Both groups recognized AI's potential for enhancing curriculum design, particularly appreciating AI-recommended courses that emphasized critical thinking over rote memorization. However, professors focused on long-term student development and foundational skill preservation, while students prioritized immediate technical support and career development benefits. The quantitative analysis identified four significant positive factors and four negative factors affecting acceptance willingness. Positive factors included Personalized Guidance (β=0.250, p<0.001), Timely Feedback (β=0.238, p<0.001), Learning Efficiency (β=0.210, p<0.001), and Employability Skills (β=0.189, p<0.001). Negative factors comprised Information Misguidance (β=-0.183, p<0.001), Perceived Risk (β=-0.133, p<0.001), Technological Dependence (β=-0.102, p<0.001), and Originality Diminishment (β=-0.092, p<0.05). Notably, positive factors demonstrated significantly stronger effects than negative ones, indicating overall favorable attitudes toward AI integration. The multi-group analysis revealed no significant differences between professor and student perspectives, suggesting consensus across stakeholder groups. The model demonstrated excellent fit indices (SRMR=0.033, NFI=0.881) and strong reliability and validity measures.
Implications: These findings have profound implications for AI integration in art education and broader educational technology implementation. The research demonstrates that AI generation tools can serve as valuable educational enhancers when properly implemented, supporting personalized learning experiences and improving educational efficiency without completely replacing traditional pedagogical approaches. The study's identification of key positive and negative factors provides a framework for educational institutions to make informed decisions about AI adoption. For curriculum development, the findings suggest that AI tools can successfully complement traditional art education by providing personalized guidance and timely feedback while maintaining focus on developing critical thinking skills. The research highlights the importance of instructor-mediated AI integration, where educators serve as "curators" and "guides" to help students critically evaluate AI-generated content. From a policy perspective, the study supports the development of comprehensive AI literacy programs and ethical guidelines for educational AI use. The findings also emphasize the need for balanced approaches that leverage AI's efficiency gains while preserving artistic originality and independent thinking capabilities. For educational technology developers, the research provides insights into user priorities, particularly the critical importance of explainability and accuracy in AI-generated content. The study contributes to the growing body of literature on human-AI collaboration in creative fields and provides empirical evidence for the potential of AI tools to democratize access to quality art education while maintaining educational integrity.
Limitations: The study acknowledges several important limitations that affect the generalizability and scope of findings. The geographic concentration of participants primarily in China (with some representation from the US and Europe) limits cultural and educational system diversity, potentially affecting the applicability of findings to other global contexts. The sample composition was predominantly from art-related disciplines, which may not represent perspectives from other educational domains or interdisciplinary programs. The study's focus on current AI technology capabilities makes it challenging to anticipate future technological developments and their long-term educational implications. The research relied heavily on self-reported data through interviews and surveys, which may introduce response bias and subjective interpretation variations. The cross-sectional design prevents understanding of how attitudes and usage patterns evolve over time with increased AI exposure and technological advancement. The study did not extensively examine specific AI tool types or versions, focusing primarily on ChatGPT-4.0, which may limit insights into other AI platforms and their unique characteristics. Additionally, the research did not fully explore the perspectives of other key stakeholders such as school administrators, parents, policymakers, and industry professionals who influence educational decision-making. The study also had limited consideration of economic factors, resource availability, and technological infrastructure requirements that may affect AI implementation in different educational settings.
Future Directions: The researchers suggest several promising avenues for future investigation to address current limitations and expand understanding of AI in art education. Future studies should incorporate larger, more geographically and culturally diverse samples to improve generalizability across different educational systems and cultural contexts. Longitudinal research designs would provide valuable insights into how AI tool effectiveness, user attitudes, and educational outcomes evolve over extended periods of use and technological development. Research should expand to include multiple stakeholder perspectives, incorporating views from school administrators, parents, policymakers, and industry professionals to create more comprehensive implementation frameworks. Future investigations should explore the integration of AI tools with other emerging technologies such as augmented reality (AR), virtual reality (VR), and mixed reality (MR) to create more immersive and innovative educational environments. Studies should examine specific AI platform comparisons and their unique affordances for different aspects of art education, moving beyond general AI tool categories. Research into cost-benefit analyses and resource allocation strategies would help educational institutions make informed investment decisions. Future work should also address ethical considerations in greater depth, including data privacy, intellectual property rights, and the development of comprehensive ethical guidelines for AI use in educational settings. Additionally, research should explore the potential of AI tools to address educational equity issues and reduce resource disparities between institutions. Investigation of teacher training programs and professional development needs for effective AI integration would provide practical implementation guidance. Studies should also examine the long-term impacts of AI integration on student creativity, artistic skill development, and career preparation outcomes.
Title and Authors: "Is the impact of artificial intelligence generation tools on improving art education positive or negative? Perspectives of professors and students" by Ru Zhang, Hyemin Lee, RongHui Wu, Wei Yang, and Younghwan Pan.
Published On: April 21, 2025
Published By: Interactive Learning Environments (Taylor & Francis Group)