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

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.

Framework; Creativity; Pedagogical Innovation

Guha, 2025; McKinsey & Company, 2023; Toubia & Bodapati, 2025). In advertising, AI is evolving from a

productivity tool into a co-creative partner that shapes concept development, message framing, and aesthetic

direction (Cui, Yuan, & Liu, 2025). Yet, despite its growing industrial influence, design education especially

advertising-oriented courses continues to lack structured pedagogical models to guide its effective and ethical

integration.

Traditionally, graphic design education has emphasised human-centred creative processes that develop visual

literacy, conceptual reasoning, and storytelling. The integration of generative AI challenges these traditions by

enabling instant visualization, potentially disrupting established learning processes and assessment norms.

Emerging research indicates that AI can enhance divergent and convergent thinking when applied within

intentional pedagogical structures, allowing students to iterate ideas more fluently (Melker, Gabrils, &

toward content creation and hybrid skill development, while Lan (2025) emphasises the importance of

innovation that merges creativity, ethics, and digital literacy. These findings underline the urgent need for a

coherent conceptual framework that specifically addresses advertising image creation a critical domain where

creativity, persuasion, and technology intersect most dynamically.

To address this gap, this paper proposes a conceptual framework for integrating generative AI in advertising

image creation within graphic design education. The framework draws upon creativity theory, technology

adoption models, and design pedagogy to define progressive stages of AI-assisted ideation, human

refinement, and critical evaluation. It aims to guide educators in designing curricula that promote human AI

visuals are generated across sectors. McKinsey & Company (2023) report that generative models contribute

substantially to productivity and innovation in marketing, design, and entertainment, demonstrating potential

to reshape creative workflows. In advertising, AI tools increasingly function as co-creators that enhance

ideation, automate repetitive tasks, and optimize message delivery (Cui, Yuan, & Liu, 2025). Similarly,

Grewal, Satornino, Davenport, and Guha (2025) highlight that marketers now integrate generative AI not

merely for efficiency but for expanding creative possibilities and personalization at scale. Hartmann, Exner,

and Domdey (2025) empirically show that AI-generated marketing visuals can rival human work in

persuasion and aesthetic value, suggesting a convergence between computational and human creativity.

Toubia and Bodapati (2025) further note that AI-driven data synthesis offers new insights for concept testing

and consumer research, creating a feedback loop between creative generation and market intelligence.

(2024) similarly contend that AI empowerment in graphic design fosters innovative approaches to

composition and form-finding, enabling learners to explore multiple creative directions rapidly. Yet, the

introduction requires rethinking the role of the instructor from technical expert to creative facilitator who

guides interpretation, ethics, and reflection. Lan (2025) adds that systematic integration of AI tools should be

accompanied by critical discussions about authorship and bias, ensuring that students engage in conscious,

value-driven design. Taken together, the literature underscores that AI’s educational value depends less on

technological sophistication than on how it is pedagogically contextualized.

collaborator that stimulates ideation but still requires human direction to achieve persuasive and contextually

appropriate outcomes. Hartmann et al. (2025) reinforce this by showing that even advanced generative

systems rely on human framing to translate computational novelty into meaningful advertising narratives. The

educational focus thus pivots from technical execution to creative orchestration the capacity to integrate AI

outputs into coherent communicative strategies.

suggesting that pedagogical framing is critical to positive learning outcomes. Moreover, contemporary

relevant skills, creative processes, and motivation each of which can be augmented, but not replaced, by AI

assistance (Melker et al., 2025). These frameworks collectively suggest that effective integration of

generative AI requires balancing automation with agency, ensuring that learners maintain cognitive

ownership of creative decisions.

The reviewed literature converges on three insights: (1) generative AI is rapidly transforming creative

ethical responsibility.

The preceding literature indicates that while generative AI enhances creative efficiency and idea exploration

(Cui et al., 2025; Grewal et al., 2025; Melker et al., 2025), its educational adoption requires a structured

model that safeguards human creativity and critical reflection (Lan, 2025; Yuan & Wu, 2024). The conceptual

integrate generative AI into advertising image creation within graphic-design education. The framework

treats AI as both a creative partner and a learning mediator, positioning students as reflective co-creators who

Students begin with market or client briefs, audience profiling, and message development. At this stage,

learning activities emphasise research, brainstorming, and analog ideation to establish conceptual depth

before any AI intervention (Hwang & Wu, 2025). Educators encourage divergent thinking through sketching,

storyboarding, or word-association exercises, cultivating the human insight that will later steer AI prompts.

The pedagogical intent is to ensure that the creative direction originates from human context and

communicative purpose.

Generative AI tools are introduced to expand visual possibilities rapidly. Students translate conceptual

keywords into structured prompts, exploring composition, mood, and symbolism through multiple AI-

decision-making, it operationalises what Melker, Gabrils, and Villavicencio (2025) describe as “AI-enhanced

experimentation while retaining human authorship through refinement and critique. It also aligns with Hwang

and Wu’s (2025) observation that graphic-design education must transition from image production toward

content curation and contextual reasoning, skills increasingly vital in AI-mediated creative industries.

facilitator who guides meaning-making and ethical reasoning (Kiliánová, Kočková, & Kostolányová, 2024).

Lessons structured around the four phases can help students integrate generative tools within authentic design

briefs thereby promoting situated learning consistent with constructivist theory. Second, assessment methods

capture how students formulate prompts, evaluate AI outputs, and justify aesthetic decisions. Such practices

reflection rather than automation.

educators with a roadmap to structure lessons, define learning outcomes, and assess creative growth within

AI-enhanced curricula. The integration of ethical and pedagogical principles ensures that the framework

remains adaptable to different institutional and cultural contexts, promoting responsible innovation rather

than technological determinism.

pedagogical instrument for developing future-ready designers who can navigate complex intersections

between creativity, ethics, and technology. As highlighted across global studies (Cui, Yuan, & Liu, 2025;

Melker, Gabrils, & Villavicencio, 2025; Hwang & Wu, 2025), educators play a pivotal role in ensuring that

AI integration cultivates critical consciousness rather than passive consumption. By adopting reflective and

iterative teaching strategies, institutions can align creative education with Industry 5.0 values of human-

centric innovation.

Educators should adopt the proposed four-phase model as a scaffold for classroom activities, progressively

introducing AI tools within conceptual and practical assignments. Each phase should include reflection

checkpoints where students articulate creative intent, ethical awareness, and evaluative reasoning. Institutions

may develop teaching guidelines or workshops to familiarise lecturers with prompt design, AI ethics, and

evaluation rubrics suited for advertising courses.

Higher education authorities and curriculum developers should integrate AI literacy and creative ethics as key

learning outcomes within design programs. As McKinsey & Company (2023) and Grewal et al. (2025)

suggest, professionals of the future will require hybrid competencies that merge strategic thinking, data

literacy, and creative expression. Embedding these skills at the diploma and undergraduate levels will ensure

that graduates remain adaptable in technology-driven creative economies.

engineering, model bias awareness, and AI-assisted workflow management and pedagogical literacy, such as

the ability to design reflective assignments and assess hybrid human–AI outputs (Lan, 2025; Yuan & Wu,

2024). Therefore, professional development should move beyond general upskilling to focus on structured

capacity-building initiatives that combine theory, ethics, and hands-on experimentation. Targeted workshops,

interdisciplinary collaborations with computer science or media departments, and ongoing micro-credential

programs can equip educators with practical knowledge of generative systems like DALL·E, Midjourney, or

Gabrils, and Villavicencio (2025) argue, such dual emphasis on technological fluency and reflective

transform the conceptual foundation developed in this paper into evidence-based pedagogical models that

advance the discourse on AI-driven creativity in design education.