Designing AI-enhanced yet AI-resilient marketing education

Artificial intelligence is rapidly reshaping marketing practice - and, inevitably, marketing education. From content generation to audience analytics, generative AI tools are now embedded in the workflows students will encounter in their careers. Yet this transformation raises a critical pedagogical question: how can educators harness AI’s benefits without encouraging over-reliance or undermining critical thinking?

This blog reflects on a recent undergraduate module at Queen Mary, University of London, Influencer Marketing and AI Trends, which was designed to address exactly this challenge. The aim was not to resist AI, but to integrate it thoughtfully - creating a learning environment that is both AI-enhanced and AI-resilient.

The pedagogical challenge

Business schools are under increasing pressure to ensure graduates are “AI-ready.” However, simply allowing unrestricted use of generative tools risks shallow engagement, passive learning, and diminished originality, and there is a growing concern around academic integrity, superficial learning, and the erosion of critical thinking skills.

Rather than treating AI as either a threat or a shortcut, this module positioned it as a learning augment - a tool that supports, but does not replace, human judgment.

A project-based learning design

At the heart of the module was a semester-long influencer marketing project. Students worked in groups to design and execute a campaign, applying theoretical concepts in real time.

Each week introduced key ideas, such as influencer typologies, authenticity, content strategy, and platform algorithms, which were immediately integrated into the project. This tight coupling of theory and practice ensured that learning was not abstract but continuously reinforced through application.

This approach draws on experiential learning principles, where knowledge is constructed through doing, reflecting, and iterating. It also mirrors the realities of the marketing industry, where strategy and execution evolve simultaneously.

Integrating AI - but with boundaries

AI tools (such as Haygen, Mango AI, ElevenLabs, ChatGPT, and Gemini etc.) were embedded across multiple stages of the project:

Ideation support: generating initial campaign ideas

Content refinement: improving captions, scripts, and messaging

Video development: supporting short-form content creation

Students were also introduced to industry-relevant tools commonly used in influencer marketing workflows. This ensured that they developed practical, employable skills.

However, a key design principle was scaffolded AI use. Rather than allowing unrestricted reliance, AI was introduced with clear guidance on when it adds value; where it falls short; and how to critically evaluate its outputs.

This structured approach encouraged students to see AI as a collaborator, not an authority.

Designing for AI resilience

The most distinctive feature of the module was its deliberate effort to limit AI use in certain contexts.

To counter over-dependence, the course incorporated design thinking pedagogy, with activities that required fully human engagement. These included: offline brainstorming sessions using chart paper; empathy mapping exercises; collaborative ideation workshops.

In these settings, students had to negotiate ideas, challenge assumptions, and build consensus - processes that AI cannot replicate.

This intentional alternation between AI-supported and AI-free tasks proved critical. It helped students develop what might be termed AI literacy with discernment: an understanding not just of how to use AI, but when not to.

Assessment for deep learning

Assessment was designed to reinforce higher-order thinking. Instead of focusing solely on outputs, students were required to present and justify: their strategic decisions; content choices; and ethical considerations

This reflective component ensured that students could articulate why they made certain decisions, not just what they produced.

Such an approach aligns with constructivist learning theory, where knowledge is actively constructed rather than passively received. It also mirrors professional practice, where marketers must defend their strategies to stakeholders.

What did students gain?

Student feedback highlighted three key outcomes:

Higher engagement: The project-based structure made learning more interactive and meaningful.

Stronger conceptual understanding: Applying theory in real time deepened comprehension.

Critical AI usage: Students reported greater confidence in evaluating AI outputs rather than accepting them uncritically.

Perhaps most importantly, students developed a more nuanced view of AI, not as a replacement for thinking, but as a tool that requires thoughtful use.

Implications for business schools

This case offers a practical framework for educators navigating the AI transition:

Integrate AI intentionally, rather than allowing unrestricted use

Design for balance, combining AI-enabled and AI-free activities

Prioritise reflection, ensuring students justify their decisions

Embed real-world projects, aligning learning with industry practice

The goal is not to “AI-proof” education by excluding technology, but to future-proof graduates by developing their critical, creative, and ethical capabilities.

Final thoughts

AI is here to stay, and its role in marketing will only expand. The challenge for educators is not whether to adopt AI, but how to do so responsibly.

This module demonstrates that it is possible to strike that balance. By combining experiential learning, structured AI integration, and human-centred pedagogy, we can create classrooms that are both innovative and intellectually rigorous.

In doing so, we prepare students not just to use AI but to think beyond it.