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Abstract

This paper introduces the 4C (compile, curate, challenge, contemplate) pedagogical framework for integrating generative artificial intelligence (AI) into advertising education through adaptive case studies. Our framework positions AI not as a shortcut to answers, but as a tool for supporting creativity, strategic thinking, and problem-solving. We describe how educators can use AI to generate flexible, goal-oriented case studies, and how students can draw on AI tools when developing solutions. To illustrate the application of the 4C framework, we provide a concrete step-by-step demonstration (with AI prompts) of how the approach can be implemented in practice. In doing so, the approach aims to cultivate an adaptive mindset—shifting attention from what AI can replace to what new possibilities it enables. By aligning classroom experiences with real-world demands, this model helps students move toward AI fluency, preparing them to create meaningful value in a rapidly evolving advertising industry.

Keywords

artificial intelligence case studies experiential learning problem-based learning

Introduction

The rapid advancement of artificial intelligence (AI) is reshaping the landscape of advertising education (e.g., Habib, 2025; Kim et al., 2022; Pavlik, 2023; Wagler, 2025) and advertising research (e.g., Huh et al., 2023; Van Berlo et al., 2024). Tasks that once defined the core of entry-level roles—such as writing tailored copy, identifying audience segments, or drafting media plans—are increasingly being delegated to generative AI systems. This reality fundamentally challenges the traditional model of advertising education. Iyer and Bright (2025), for example, argue that AI is transforming skill requirements in advertising practice and call for revised curricula that integrate strategy and AI technology. Likewise, Huh et al. (2023) emphasize the need for new educational frameworks to guide the responsible use of AI in advertising. Following these calls, we propose integrating AI into case-based learning practices in advertising education.

Case-based learning has long been central to advertising education because it allows students to grapple with practice-oriented problems that resemble those they will encounter in agencies and client organizations. Earlier work argued that carefully designed cases encourage students to connect theory and practice, assume the role of decision makers, and engage more deeply with ethical, strategic, and creative trade-offs than traditional lecture formats permit (Gale & Kreshel, 2006). Furthermore, case-based learning can accommodate diverse learning styles, foster higher-order reasoning, and help students “learn how to think” (Hachtmann, 2016). More recently, Whaley’s (2021) review of advertising teaching cases documented both the growth of case-based resources and persistent gaps, including uneven coverage of emerging topics and limited opportunities for educators to flexibly tailor cases to their own course objectives. In this paper we propose a framework that addresses these gaps by integrating AI into case-based learning.

This paper proposes a pedagogical framework that integrates AI into two key stages of case-based learning: case study generation by the educators and case analysis by students. The framework enables educators to generate a case study that is directly aligned with the learning goals of the entire course or a specific class. Such structural alignment is paramount to the effective learning (Biggs, 1996) and before AI it was remarkably difficult to achieve with the limited available “off the shelf” case studies. Today AI enables educators to create flexible learning environments responsive to shifting educational goals, instead of having to rely on fixed scenarios with predetermined answers. In addition to flexibility, our framework contributes to inclusivity in education, by introducing a cost-effective alternative to buying case studies for institutions with limited budgets. For example, just as subscription fees and article processing charges limit Global South participation in scholarly publishing (Powell et al., 2020), per-student licensing fees for commercial teaching cases can limit the adoption of the case method, or push educators toward ad-hoc compromises (e.g., using outdated or non-contextual material).

The proposed framework also addresses several educational goals identified in prior advertising education research, while integrating AI in ways that align with emerging pedagogical recommendations. As scholars have noted, one of the central challenges facing contemporary educators is teaching students how to incorporate AI into their work without diminishing their own creative and strategic contributions (Huh et al., 2023). Our framework directly responds to this challenge by creating a learning environment in which students must use AI for ideation, creative execution, and analytical support, yet still make independent, value-driven strategic decisions.

In line with research on AI-assisted creativity, Wagler (2025) demonstrates that generative AI can enhance ideation and streamline creative workflows in advertising design courses. Our framework similarly integrates AI into the ideation phase, but expands its use to include strategy development, feasibility testing, and iterative refinement. Likewise, Yang (2023) emphasizes that structured experimentation with AI tools helps students better understand both the capabilities and limitations of these systems, fostering a more critical and informed perspective on their role in industry practice. This critical engagement is an essential component of our approach.

More broadly, scholars in marketing education have argued that generative AI necessitates a rethinking of pedagogical design, shifting from viewing AI as a shortcut to recognizing it as a tool that can support creative and analytical thinking (Acar, 2024). The framework supports this shift by prompting students to reframe their relationship with AI—from asking “What can AI do instead of me?” to “What can I do now with AI that I could not do before?” When students work through complex, AI-supported case studies, they begin to recognize that while AI can assist with tasks, it cannot replace human insight, judgment, ethical reasoning, or originality—skills that remain distinctly human and crucial in professional practice (Millet et al., 2023).

Importantly, the framework also cultivates critical evaluation skills. As Sundar and Liao (2023) point out, the increasing ease of AI-generated answers heightens the need for students to assess AI outputs with skepticism and discernment. By requiring students to critique, refine, and justify AI-assisted solutions, the framework strengthens their ability to recognize the limits and potential pitfalls of automated outputs.

Finally, by encouraging students to employ AI for both problem-solving and creative execution tasks, the framework prepares them for the realities of future workplaces where AI is an integral part of the advertising process. In this way, advertising education becomes not only more aligned with contemporary professional practice, but also more empowering—helping students develop confidence in their uniquely human capabilities in a rapidly evolving, AI-mediated industry.

The 4C Framework for Adaptive Case-Based Learning

In this section, we present the conceptual foundation of the 4C Framework, outlining its four phases at a theoretical level before turning to implementation details later in the paper.

Overview of the Framework

To help educators prepare students for the realities of an AI-integrated advertising industry, we introduce the 4C Framework for Adaptive Case-Based Learning (see Figure 1). This four-phase model supports the purposeful integration of generative AI into case-based learning by guiding educators and students through a structured, iterative process. Each phase (i.e., compile, curate, challenge, and contemplate) represents a distinct stage in the instructional process: from aligning case content with learning goals, to refining it for pedagogical fit, engaging students in strategy development, and finally reflecting on both outcomes and the role of AI. Together, these phases transform case studies from static exercises into dynamic, adaptive learning environments that mirror the complexity of contemporary advertising practice.

Figure 1.

The 4 C framework for adaptive case-based learning

Phase 1: Compile—Structuring Inputs for AI-Assisted Case Generation

The first phase of the 4C adaptive case study generation framework is the compile phase, which involves organizing instructional inputs in a way that enables meaningful and pedagogically aligned use of generative AI. This phase is critical because it sets the foundation for both the educational effectiveness of the case study and the quality of the AI-generated output. Rather than asking AI to “create a case study” in a vacuum, educators must provide it with structured, purpose-driven content that reflects the goals and design of the course. At its core, this first phrase is about constructive alignment (Biggs, 1996). This means that the case study must not only reflect course content, but also actively support the intended learning outcomes and assessment criteria.

Educators begin by gathering all relevant instructional components that define the scope and purpose of the learning experience. These can include (but are not limited to): course-level learning objectives; class-specific objectives; relevant academic literature; key concepts or tools; case-specific learning outcomes; value-based goals, etc. For instance, if the learning objective is to help students understand how brands can respond to consumer backlash after a poorly received campaign, the educator might compile materials on brand identity, message framing, cancel culture, and crisis communication strategies. These would form the basis for asking AI to search for a real-world case involving a brand navigating such a backlash.

Once instructional materials and goals are compiled, the next step is to translate them into structured prompts suitable for use with generative AI. In order to compile the materials together, several prompts might be needed to ask AI to consider all the relevant information, ensuring the resulting case study is aligned with the curriculum rather than being an off-the-shelf scenario.

Importantly, the compile phase helps position AI not as a shortcut to lesson planning, but as a tool for amplifying the educator’s design intentions. It ensures that AI searches for cases and compiles information about the case not simply if a form of an engaging narrative, but as study material that is pedagogically sound, contextually appropriate, and aligned with course goals. Without this scaffolding, AI output risks being too generic or misaligned with the depth and nuance required in advertising education. Once the scaffold is introduced to the AI, the curate phase can start.

Phase 2: Curate—Iterative Refinement Toward Pedagogical Fit

The second phase of the 4C adaptive case study generation framework is the curate phase, in which the educator asks the AI to search for real-world cases that are aligned with the learning objectives outlined in Phase 1. It is widely recognized the prompt curation is essential for generating the desired educational content outcome (Singh & Pathania, 2024). Therefore, the educator must takes an active, iterative role in refining the AI-generated outputs, using their domain knowledge and instructional goals to sculpt the raw output into a usable and meaningful learning tool.

This phase requires that the educator must make choices in guiding the AI. For instance, the educator might specify the search criteria for the suitable brands: industry or even a type of product, brand size, and architecture, specific details, such as budgets, costs, or goals that the educator would like to be included in case study description. We recommend that this process be approached incrementally. The educator can begin by prompting the AI to generate a list of brands that have faced challenges aligned with the criteria established in Phase 1, accompanied by brief descriptions of each scenario. This search should be progressively refined until several cases are identified that most closely match the pedagogical objectives.

Once a shortlist is established, the educator can request additional information on each case. At that point, one case can be selected and further developed by adding or omitting specific details as needed. This flexibility allows the educator to control the complexity, scope, and strategic ambiguity of the case study, tailoring it to the desired learning outcomes.

We recommend including all the relevant quantitative parameters that the students might have to base their decisions on, so that the solutions produced by students in Phase 3 are more easily compared. It is also recommended to check those numerical inputs against reliable sources, by directly asking AI the source of information and verifying its reliability.

Phase 2 is the end of the preparation phase, the next phase is introducing the case study to the students and providing instructions for them to work with.

Phase 3: Challenge—Student-Led Strategy Development With AI

The third phase of the 4C adaptive case study generation framework is the challenge phase, which marks the transition of agency from the educator to the student. This phase unfolds during in-class work, where students are asked to propose a strategy for the company and present it in class using the creative help that AI can offer. Their job is to identify and articulate a strategy, drawing on the theories learned in the course, market insight, ethical reasoning, and creative innovation and to design the visualization of the proposed strategy.

This phase, therefore, is meant not merely to replicate traditional case study analysis, but to elevate it. Because of the capabilities of generative AI, we now expect more from student solutions. Rather than abstract plans or bullet-point recommendations, students are asked to produce presentation-ready outputs—as they might in an agency pitch or consulting engagement. This might include mockups of social media ads, sample media plans, or even scripts for influencer content. AI allows these materials to be produced rapidly during class time, enabling a hands-on, production-oriented learning experience that was previously out of reach for many classroom settings. Such setup provides an opportunity to practice creative execution skills and learn from each other in the process.

The educator can decide which components to emphasize. Students can, for example, be instructed to use AI tools to explore multiple possible directions and evaluate trade-offs; ask AI to generate and refine campaign components; assess the feasibility of their proposed strategy; stress-test their ideas under realistic conditions; align solutions with values such as inclusivity, sustainability, or transparency.

This process demands critical judgment. AI will often generate plausible sounding but impractical suggestions. For example, a recent large-scale study by Si et al. (2024), with over 100 researchers found that while AI-generated research ideas were consistently rated as more novel than those from human experts, the human ideas were ultimately judged as more effective. The researchers concluded that this was because the human-generated ideas were more feasible and strategically aligned with real-world research practices, whereas the AI’s suggestions often lacked practical details or were based on incorrect assumptions. Students must assess the validity, coherence, and integrity of what AI produces—and iterate as needed. This aligns with our overarching pedagogical goal: to teach students how to use AI not to bypass thinking, but to expand what is thinkable and doable, taking into consideration also ethical concerns (Habib, 2025).

The challenge phase also reinforces the importance of iteration. Students are not expected to arrive at perfect answers immediately. Instead, they are encouraged to use AI in cycles of inquiry, reflection, and revision—much like professional teams navigating uncertain and high-stakes projects. The instructor’s role is to facilitate this process, providing guardrails, giving feedback, and challenging students to push their work further.

Phase 4: Contemplate—Reflecting on Decision-Making and the Limits of AI

The final phase of the 4C adaptive case study generation framework is the contemplate phase. Students should present their solutions to the class (for instance, in a form of a presentation pitch as if they were consultants). This stage is centered on critical reflection, both individual and collective, about the outputs generated during the challenge phase. The instructor can help students reflect on the similarities and differences in their solutions. This phase serves as the culmination of the learning process, where students and instructors examine not only the solutions that were proposed, but also the nature of the process that produced them—especially the role of generative AI within that process. It is critical that students understand their own contribution versus the contribution of AI and can clearly articulate the boundary (Habib, 2025).

This phase is essential because it explicitly addresses the core educational objective of the framework: cultivating in students a deep understanding of what AI can—and crucially, cannot—do, as it lacks the capacity to align with human judgment, ethics, and purpose, unless explicitly instructed to do so (Foysal et al., 2023).

A key learning point in this phase is to challenge the assumption—common among students—that AI can or should be able to deliver “the best solution” to a case study. We make it explicit: if the task were finite, with a single correct answer, AI would likely solve it faster and better. But most real-world problems do not have a single correct answer. Therefore, a case study presents an open-ended problem situated in a complex social, cultural, and commercial context—exactly the kind of situation where values, ethics, and human intent matter.

For instance, students may have proposed marketing strategies involving AI-generated influencers, crisis responses, or repositioning campaigns. At this point, we invite them to analyze the decision-making behind their own solutions and ask: What assumptions were embedded in this strategy? Who benefits? Who might be harmed? What trade-offs were made? Then, we ask them to reflect on the role AI played: Was it a source of insight or a source of overconfidence? What guided you in decision making in the face of a flood of possibilities?

This discussion marks a pedagogically transformative moment. Students begin to see that their professional value lies not in doing tasks AI can automate, but in performing the kind of judgment-driven, ethically anchored work that AI cannot. We encourage instructors to facilitate this reflection actively, whether through guided discussions, debrief presentations, or written analysis.

Beyond technical skill development, one of the important outcomes is to shift students’ attitudes toward AI—from viewing AI as a tool that provides solutions, to critical thinking, curiosity and agency in using AI. To better understand and measure this change, educators could consider integrating brief attitudinal assessments at the beginning and end of the case study exercise. For example, students could respond to statements such as: “I feel confident that AI cannot replace me as an advertising professional,” “I feel inspired about what I can contribute to the field of advertising, even in a world shaped by AI.”

Such attitudinal metrics can help educators gauge not only cognitive learning outcomes, but also affective development—particularly students’ evolving sense of professional identity and value in the age of AI. As more institutions integrate generative tools into advertising education, understanding these mindset shifts will be critical to designing learning environments that both prepare and empower the next generation of professionals.

Having outlined the conceptual structure of the 4C Framework, we next turn to the practical considerations that shape its implementation in real classroom environments.

Practical Considerations for Responsible AI Use

The successful implementation of the 4C Framework requires attention to several operational considerations, particularly when integrating generative AI into educational settings. These considerations are grouped into three areas: platform choice, privacy and data protection, and verification of AI-generated content.

Platform Choice

In our implementation, case generation was conducted using OpenAI’s Model 5 (OpenAI, 2025), although the framework is model agnostic; other large language models can be used equally effectively. The case-generation phase undertaken by the educator does not require specialized technical expertise; rather, it involves integrating publicly available, reliable data about selected brands into prompts that reflect the instructional goals established in the compile phase (Phase 1).

During the challenge phase (Phase 3), students were encouraged to select the platforms they preferred. This mirrors contemporary professional practice, in which teams frequently rely on a mix of tools. Morgan et al. (2025) document a range of challenges and opportunities students encounter when using tools such as ChatGPT, DALL·E 2, and Bing Image Creator, which aligns with our observations. In our course, students collectively used more than 20 different platforms to generate images, videos, and pitch materials.

Maintaining this flexibility is critical for two reasons. First, it supports accessibility and inclusivity: a multi-platform approach accommodates differences in students’ preferences, device access, prior experience, and institutional resource constraints. Second, because AI systems evolve rapidly, a rigidly platform-specific framework would quickly become outdated. Allowing students to choose their tools ensures that the framework remains adaptable and future-proof.

Privacy and Data Protection

A second consideration concerns privacy and responsible AI use. A university-hosted version of an AI model can offer an additional layer of privacy protection and may be considered a gold standard for data protection, though availability and safeguards will vary by institution. To ensure the safe and compliant use of AI tools, educators are encouraged to consult their local data stewards to identify any relevant limitations or regulations.

Students may use a university-hosted system or any alternative platform. However, they should be explicitly advised not to include any sensitive or personally identifiable information in their prompts, regardless of the platform selected. Any platform may be used to support students in solving the case study, as long as it can be used safely and responsibly.

To prevent circularity or inadvertent leakage of instructional content, educators and students should use separate accounts on their chosen AI platforms. This ensures that information used during case construction (e.g., specific brand details, strategic dilemmas, or numerical inputs) is not retained in the system’s memory in ways that could surface when students later query the model during strategy development. Separate accounts help maintain the integrity of the learning experience by preventing students from retrieving educator-generated content through AI, intentionally or not.

Verification of AI-Generated Content

Finally, educators must take steps to mitigate copyright risks and ensure factual precision in AI-generated content. Although many systems (e.g., ChatGPT) include general warnings about copyright, we found it necessary to explicitly instruct the model not to draw on copyrighted sources when generating case narratives or exhibits. We also requested that the system scan the final outputs for any potential copyright conflicts, recognizing that compliance must account for jurisdiction-specific regulations.

Equally critical is the verification of AI-supplied information. Generative models are known to produce hallucinated data, particularly when asked for numerical details such as campaign budgets, sales figures, or engagement metrics. To address this, we required the AI to provide sources for any numerical or critical factual claim. These sources were then manually checked for reliability and accuracy. This step ensures that the quantitative parameters integrated into the case study are valid, allowing students to base their analyses and strategic recommendations on verifiable information rather than fabricated inputs. These verification steps are part of the curate phase (Phase 2), during which the educator refines and validates the information generated by the model.

Demonstration—Applying the 4C Framework to Case Study Creation

To illustrate the 4C adaptive case study generation framework in practice, we document the step-by-step process through which we developed a graduate-level case study for a course on brand communication. This demonstration follows the same four phases described earlier—compile, curate, challenge, and contemplate—showing how generative AI was used at each stage, and how instructor guidance shaped the final outcome.

From Idea to Input: Phase 1 (Compile)

Our process began with a clear pedagogical objective: to create a case study for a graduate-level advertising course focused on strategic brand communication. The initial prompt we submitted to the AI was:

I would like your assistance with generating a case study for a graduate level course on brand communication. Before we engage in the generation process, I need you to help me compile the list of criteria that define the scope of the study. These criteria involve course and case study learning objectives, branding challenges that should be highlighted in the case study, and ethical dilemmas that the brand in the case study could be challenged with.

From there, we compiled a list of course and class learning objectives, the potential challenge areas that are discussed in our course (e.g., brand love, sensory branding, brand architecture) and asked the AI to use these as a framework for the case study generation.

Prompt Engineering and Refinement: Phase 2 (Curate)

In our implementation of the framework, the process began by prompting the AI to generate a list of brands that had faced challenges relevant to our intended learning themes. We were explicit in the instruction: the case should reflect real challenges that real brands have encountered in the past or are facing at the moment of study generation. We then asked the AI to provide short summaries of the crises or communication challenges each brand had faced.

This step yielded dozens of plausible scenarios, but not all were equally suitable for our course. For instance, some cases were too large in scope (involving multinational conglomerates far removed from the kinds of brands students would likely encounter early in their careers), while others lacked sufficient complexity for meaningful analysis.

Realizing the need to narrow the scope, we refined our prompt to include brand size and geographic relevance, asking the AI to identify mid-sized brands that operate across national borders. Some of the prompts we used to narrow down the search were:

More cases for mid-size brands that exist worldwide, but are not dominating their product category.

More examples of regional challenger brands.

We then selected one promising brand scenario: Estrella brand (name replaced for the publication, but real brand name presented to students), a socially driven European personal care brand. We then began shaping the case further. This included: asking the AI to elaborate on specific details relevant to the learning goals; guiding the AI to adjust the tone and narrative of the case to fit pedagogical objectives; instructing the AI to retrieve real numerical data (e.g., campaign budgets, engagement metrics, sales figures) that students could use for analysis and strategic decision-making and provide links to the sources, which we then verified.

This curation process required multiple rounds of refinement. Example prompts were:

Please turn this case study into business-style case study with extensive information on the brand’s marketing strategy, branding strategy and challenges in these domains.

Please retrieve information on the marketing expenditures of the brand, sales figures in different countries, and profit margins. For all numbers, please provide a link to the source of information.

We guided the AI through multiple rounds of feedback and correction—refining the depth of the background story, the strategic dilemma, and the brand’s positioning. The output gradually matured from a surface-level summary to a detailed, data-informed case document.

We further instructed the AI to generate narrative text structured like a four-page teaching case, supplemented with exhibits and numerical data:

Please create a narrative text for this case study that would be roughly 4 pages long and tell a story of the brand with numbers integrated in the text.

The text can refer to exhibits with numbers, but the main body of the text should be read like an essay that tells the story of the brand.

The result was a rich, realistic case study designed to challenge students to think strategically, ethically, and creatively (see the Appendix).

Preparing for Student Engagement: Phase 3 (Challenge)

In class, students were tasked with reading the scenario, diagnosing the brand’s strategic challenges, proposing actionable branding and communication strategies and presenting their strategy it in class.

The students were divided into five teams, and were asked to imagine that they are a consulting company who has been hired to provide strategic advice to the brand. The students were encouraged to use AI as a resource to develop campaign concepts, estimate costs, or stress-test their ideas. The expected output was a five-minute pitch-presentation “for the client” with the proposed strategic direction, that should be realistic, provide approximate budget estimates and expected reach, and must include clear visuals such as mockups, sample content, or campaign materials to illustrate the ideas.

Structured Reflection: Phase 4 (Contemplate)

The final in-class activity involved structured reflection, in which each group presented their proposed strategy. As expected, the solutions were diverse and even contradicting each other. Two of the groups generated entire video advertising examples within the 45-minute time that they had to produce a solution. All groups generated beautiful and realistic ad campaign mockups and the theoretical argumentation behind why such campaign would be successful.

The presentations were followed by a comparative discussion on the different solutions proposed. In this case, the discussion was particularly interesting, since two of the groups proposed orthogonal solutions (specifically, one group proposed eliminating the flagship product which was least profitable, while the other proposed centering the entire campaign around the flagship product). All groups were using the same AI platform (OpenAI – model 5, latest available at the moment when the case study was taught), and we discussed how AI was used in the decision-making process. Eventually all groups acknowledged that while AI was great for brainstorming, the key decisions still had to be made by them. We concluded that AI can assist with execution, but cannot replace the judgment, values, and contextual understanding required to make meaningful strategic decisions.

Conclusion

As generative AI reshapes society (e.g., Azrout et al., 2024; Dubèl et al., 2025) and the advertising industry (Campbell, 2023), advertising education must evolve alongside it—not by resisting these tools, but by integrating them thoughtfully and purposefully. The proposed 4C adaptive case study generation framework offers a structured approach for doing so, enabling educators to design AI-assisted case studies that are aligned with learning objectives, rooted in pedagogical best practices, and responsive to the demands of a changing profession.

By structuring the learning experience around compiling, curating, challenging, and contemplating, the framework supports the development of both strategic thinking and AI fluency. More importantly, it encourages students to engage AI not as a shortcut, but as a tool for value creation. In doing so, it helps them build an adaptive mindset—one that combines critical evaluation with creative problem-solving and builds the confidence required to thrive as advertising professionals in an AI-driven world.

As AI capabilities continue to advance, so too must our pedagogical strategies, ensuring that education remains a space for critical inquiry, creativity, and meaningful human contribution.

Supplemental Material

Supplemental Material - The 4C Framework: Integrating AI in Creating and Solving Case Studies

Supplemental Material for The 4C Framework: Integrating AI in Creating and Solving Case Studies by Dasha Kolesnyk, Zeph van Berlo in Journal of Advertising Education

Footnotes

ORCID iDs

Dasha Kolesnyk

Zeph M. C. van Berlo

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online

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