Empowering Faculty in Creative and Cultural Disciplines: AI Literacy and Image Generator Integration in Higher Education

Aim/Purpose: This study empirically analyzes how a tailored AI literacy initiative can empower higher education faculty in creative and cultural-focused disciplines to adopt AI image generators into their courses. It achieves that by first identifying the foundational components required for developing an AI literacy initiative for integrating AI image generators into creative and cultural courses (RQ1) and then assessing how an AI literacy workshop designed around these components influences faculty’s perceived confidence, understanding, and readiness to adopt AI image generators (RQ2). Background: The proliferation of AI image generators has disrupted the Creative and Cultural Industries (CCI), creating both opportunities and challenges for educators. Faculty face barriers such as limited technical skills, ethical concerns, and a lack of pedagogical strategies. Despite growing interest, few AI literacy initiatives are tailored to the unique needs of creative and culturally-focused faculty. Our study is presented against such a backdrop. Methodology: A two-phase mixed-methods approach was conducted. Phase One involved focus group interviews (Activity 1) with faculty in CCI-focused disciplines (e.g., arts, design, architecture, marketing, music, fashion, film, IT, and the like) to identify key components for designing an AI literacy initiative. In Phase Two, the designed workshop (Activity 2) was delivered to 51 CCI faculty from a midwestern university. Data was collected through pre- and post-workshop surveys, as well as live polls to assess changes in participants’ perception around AI image generators’ adoption into their courses. Contribution: This study offers multiple contributions. The systematic data collection and analysis of participants’ data (empirical contribution) through a mixed-methods approach, which combines statistical analysis and human-AI thematic analysis (methodological contribution), informed the design and development of an AI literacy workshop (artifactual contribution) tailored for higher education faculty in CCI disciplines. Additionally, it contributes to the underexplored area of adopting image generators ethically and via pedagogical best practices into creative and cultural education. Findings: In Activity 1, the key components for designing an AI literacy initiative tailored for CCI faculty include technical skills, ethical awareness, and pedagogical strategies combined with practical applications and continuous AI Literacy support/resources. In Activity 2, faculty showed a positive attitudinal shift. They reported increased confidence, pedagogical and ethical awareness, access to institutional resources, and practical applications post-workshop. They expressed appreciation for image generators as complementary tools and readiness to integrate them into their teaching. Recommendations for Practitioners: Institutions should support faculty development through accessible AI tools, discipline-based literacy resources, hands-on training, and ongoing learning opportunities tailored to them. Interdisciplinary collaboration and developing ethical guidelines are essential for responsible GenAI adoption. Recommendation for Researchers: Future research should examine the long-term impacts of AI literacy initiatives on teaching and learning outcomes, exploring other multimodal GenAIs, and include broader stakeholders (e.g., students, administrators, and even technologists). Impact on Society: Equipping higher education faculty with AI literacy around GenAIs fosters responsible technology adoption and use, enhances teaching practices and learning outcomes, and prepares students to thrive in an AI-driven workforce, where humans and AIs can co-exist, collaborate, and co-create. Future Research: Future research should include longitudinal studies to assess faculty engagement and student outcomes. Expanding future research across institutions and academic levels (e.g., K-12) will help develop scalable, discipline-specific AI literacy resources, initiatives, and frameworks.