Exploring Higher Education Faculty Insights on Generative AI in Creative Courses
This study examined the understudied perceptions of higher education instructors on the use of art-based AI generators in digital art, design, and creative-based courses and answered the research questions: (1) how disruptions by generative artificial intelligence (GenAIs) are impacting teaching, and (2) what are the major factors that contribute to a healthy digital art ecosystem in higher education.
While GenAI has attracted widespread public attention, there is insufficient research on integrating art-based AI generators in digital art and design classrooms. Concurrently, there is a demand for collecting and integrating faculty and educators’ perspectives, which are key stakeholders in preparing future art and design professionals for the GenAI-driven workforce. Our study is presented against such a backdrop.
The study incorporated a mixed-method approach to analyze survey data collected from higher education faculty on their perception of text-to-image generators. Quantitative data was analyzed through statistical analysis, and qualitative data was analyzed through a blend of human-AI thematic analysis.
The study provides empirical data on higher education faculty’s perspectives regarding the implications of art-based AI generators through survey and mixed-method analysis, serving as a baseline for further research and the development of AI literacy interventions. Additionally, the research identifies effective pedagogical strategies and best practices for embedding generative AI into teaching and learning, contributing to the field of education.
Art-based GenAIs create both positive disruptions (e.g., improved ideation, problem-solving, and creative processes) and negative disruptions (e.g., ethical implications, technical limitations, and pedagogical concerns) in higher education. Insufficient AI literacy and inadequate resources among faculty significantly set back the effective adoption of GenAIs in classrooms. Ethical issues, including academic integrity, copyright, and bias, emerge as prominent issues requiring the implementation of responsible AI frameworks and policies. Adopting pedagogical strategies, such as action-based learning, experimental learning, and active learning, can help optimize student engagement and enhance learning outcomes. Last but not least, a healthy digital art ecosystem in higher education hinges on responsible AI use and standards, continuous technological improvement, effective educational support, a human-centered approach, and a strong sense of community and collaboration.
The paper recommends increasing AI literacy among faculty through professional development programs and collaborative learning initiatives; developing and implementing responsible AI use policies, guidelines, and frameworks to address ethical concerns and ensure the effective and ethical use of GenAIs in classrooms; and integrating pedagogical strategies such as action-based learning, experimental learning, and active learning to enhance student engagement and learning outcomes with GenAIs.
The paper recommends conducting further research on the integration of art-based generative AI in digital art and design classrooms across all academic levels; further exploring faculty and educators’ perspectives on GenAI use to develop best practices and frameworks for effective and ethical adoption in higher education; and investigating the long-term impacts of GenAI technologies on teaching and learning in art, design, and creative-integrated disciplines through longitudinal studies.
The larger implications of the paper’s findings include promoting awareness and education on the ethical implications, benefits, and limitations of GenAIs to foster responsible use and acceptance; encouraging interdisciplinary collaboration to address the challenges and opportunities presented by GenAIs in the creative and cultural industries; and supporting the development of a healthy digital art ecosystem that balances human creativity with technological advancements, ensuring inclusivity, accessibility, and sustainability.
Based on drawbacks that emerged from the study, such as the sampling method and the sample size, future research should focus on targeting larger and more diverse samples from across different regions of the United States, as well as integrating objective measures to complement self-reported data. As this research is focused on text-to-image generators, future research should expand to additional GenAI types and models to deepen our understanding of their potential benefits and use impacts. Additionally, future research would benefit from studying the long-term impacts of GenAIs on education and the development of human-centered solutions and interventions tailored for faculty and students.
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