A Conceptual Framework for Integrating Generative AI in Advertising Image Creation for Graphic Design Education

Generative artificial intelligence (AI) has transformed creative practice, enabling designers to visualise complex ideas through human machine collaboration. Yet, its pedagogical integration within graphic design particularly in advertising image creation remains underdeveloped. This conceptual paper proposes a framework for integrating generative AI into advertising education, aiming to balance technological innovation with human creativity and ethical responsibility. Drawing on constructivist learning theory, the componential model of creativity, and the Technology Acceptance Model, the framework outlines four iterative phases: Concept Formation, AI-Assisted Ideation, Human Refinement, and Critical Evaluation. Each phase is supported by three cross-cutting principles: Ethical Practice, Pedagogical Guidance, and Creative Authenticity. The model positions AI as a cognitive partner that enhances divergent and convergent thinking while preserving human interpretive agency. Discussion of the framework highlights its pedagogical implications for curriculum design, reflective assessment, and educator training. By situating the discourse within global and regional contexts of design education, this study contributes to the evolving scholarship on human-AI collaboration in creative pedagogy. The framework offers a practical and ethical pathway for preparing future-ready designers capable of navigating the hybrid creative ecology of Industry 5.0.

Generative Artificial Intelligence; Graphic Design Education

1. Cui, W., Yuan, R., & Liu, M. (2025). Exploring the integration of generative AI in advertising agencies: A co-creative process model for human–AI collaboration. Journal of Advertising Research. https://doi.org/10.1080/00218499.2024.2445362 [Google Scholar] [Crossref]

2. Grewal, D., Satornino, C. B., Davenport, T., & Guha, A. (2025). How should Gen AI fit into your marketing strategy? Harvard Business Review. [Google Scholar] [Crossref]

3. Hartmann, J., Exner, Y., & Domdey, S. (2025). The power of generative marketing: Can generative AI create superhuman visual marketing content? International Journal of Research in Marketing, 42(1), 13–31. [Google Scholar] [Crossref]

4. Hwang, Y., & Wu, Y. (2025). Graphic design education in the era of text-to-image generation: Transitioning to contents creator. International Journal of Art & Design Education, 44(3), 239–253.* https://doi.org/10.1111/jade.12558 [Google Scholar] [Crossref]

5. Kiliánová, K., Kočková, P., & Kostolányová, K. (2024). Breaking boundaries in education: The AI (r)evolution in the world of graphic design. In ICERI2024 Proceedings (pp. 9286–9292). IATED. [Google Scholar] [Crossref]

6. Lan, Y. S. (2025). Exploring the impact of artificial-intelligence creative tools in graphic design education: A systematic review. Semarak International Journal of Creative Art and Design, 3(1), 1–15.* https://doi.org/10.37934/sijcad.3.1.115b [Google Scholar] [Crossref]

7. McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey Global Institute. [Google Scholar] [Crossref]

8. Melker, S., Gabrils, E., & Villavicencio, V. (2025). Artificial intelligence for design education: A conceptual approach to enhance students’ divergent and convergent thinking in ideation processes. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-025-09964-3 [Google Scholar] [Crossref]

9. Toubia, O., & Bodapati, A. (2025). How Gen AI is transforming market research. Harvard Business Review. [Google Scholar] [Crossref]

10. Yuan, X., & Wu, X. (2024). AI empowerment and graphic design education: Innovation and breakthrough. International Journal of Modern Education, 10(2). [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition