Abstract: As generative AI continues to reshape educational landscapes, prompt-to-image technologies offer new possibilities for enhancing visual pedagogy. This study investigates the integration of NanoBanana a lightweight, prompt-driven image generation tool, into higher education settings to support multimodal learning and cognitive scaffolding. Grounded in Dual Coding Theory and Cognitive Load Theory, the research explores how AI-generated visuals derived from student and instructor prompts can improve comprehension, engagement, and retention in complex subjects such as ICT, Game Studies, and Systems Analysis. Using a mixed-methods approach, the study analyzes student performance data, visual rubric evaluations, and qualitative feedback from learners and educators. Findings reveal that NanoBanana-generated images significantly aid in conceptual clarity, reduce extraneous cognitive load, and foster learner autonomy. The paper proposes a practical framework for integrating prompt-to-image tools into curriculum design and instructional workflows, offering actionable insights for educators seeking to advance AI-enhanced teaching practices in line with SDG4 and the evolving demands of the AI era.