Dr. Chahna Gonsalves, Senior Lecturer in Marketing at Kings College London, shares her experience of using GenAI in Marketing education and explores how it may be utilised in engineering education.
Generative AI (GenAI) is more than a technical breakthrough. It represents a transformation in how knowledge may be produced, applied, and valued. For educators in engineering and related disciplines, this shift prompts a rethinking of what students need to learn—and, crucially, how they need to learn it.
In my work in marketing education, I’ve been grappling with these questions for some time. While the contexts differ, the underlying challenges are shared. The implications of GenAI span disciplines and demand a thoughtful, future-facing response in engineering education. This is a moment to examine long-held assumptions and to explore new models of learning that are imaginative, ethical, and pedagogically grounded.
Reframing Foundational Learning
Traditionally, engineering education has prioritised mastery—of logic, of systems, of structured problem-solving. Yet when AI can generate code, simulate design solutions, or identify system errors in seconds, the rationale for these methods shifts.
Foundational knowledge remains essential. What changes is its role in the learning process. Students need fluency in core concepts like thermodynamics or structural analysis, not merely to replicate textbook problems, but to critically assess AI-generated outputs. Understanding how to interpret, question, and refine what AI proposes becomes a central skill.
The same holds true in marketing. Students might prompt AI to generate compelling campaigns or customer personas, but the ability to interrogate these outputs—for tone, bias, relevance, or ethical impact—defines whether the final result has real-world value. In both fields, learning now hinges on discernment and judgment, not just execution.
Thinking With AI
Integrating GenAI into education requires more than technical training. It involves cultivating a new kind of cognitive partnership.
In engineering design, for example, students may use GenAI to propose initial CAD models or generate algorithmic solutions. The educational depth comes from examining these results: What design logic is being followed? What constraints were considered—or ignored? What trade-offs are hidden beneath the surface?
AI, used this way, becomes a conceptual sparring partner—provoking, extending, and challenging the student’s thinking. I describe this in my research as ‘iterative engagement’: a process that includes generation, critique, adaptation, and reflection. It’s an approach that centres process over product, curiosity over compliance.
Elevating Ethical Reasoning
Ethical understanding needs to be as integral to engineering education as technical proficiency.
GenAI models carry the imprint of the data they are trained on—complete with biases, assumptions, and blind spots. When used to generate code for critical systems, develop visual designs for healthcare devices, or automate aspects of the built environment, the ethical implications are significant.
Students must be prepared to ask difficult questions: Is this model reinforcing inequity? Could this design decision compromise safety? How transparent and accountable are these systems in practice?
In marketing, we’ve confronted similar questions—especially with GenAI now used to fabricate influencer personas, auto-generate hyper-personalised ad campaigns, or simulate emotionally resonant content. These outputs may be effective in terms of engagement, but they complicate issues of authenticity, manipulation, and consent.
Engineering students, too, must be equipped to navigate the social and ethical dimensions of GenAI—whether they are building physical systems, writing code, or modelling environments. Developing ethical reflexes isn’t a supplement to their education; it’s a cornerstone.
Avoiding the Creep of Cognitive Offloading
One emerging challenge is more subtle: the gradual erosion of students' intellectual confidence.
As GenAI tools become more capable, students may begin to second-guess their own reasoning. Why struggle through a design problem when a prompt yields a seemingly viable solution? The risk is a form of cognitive offloading—where reliance on AI reduces active engagement and weakens critical autonomy.
To counter this, learning activities must foreground metacognition. Students should be encouraged to document their reasoning, compare AI-generated solutions with traditional methods, and reflect on how their thought processes evolve. Shifting assessment towards justification, explanation, and evaluation reinforces this deeper level of engagement.
Principles for Pedagogical Transformation
Integrating GenAI meaningfully into engineering education requires intentional design. These four strategies help create a richer, more resilient learning environment:
- Position AI as a Launch Point
Use AI to spark ideas and generate drafts—but require students to interrogate, revise, and contextualise what it produces. The focus is on interpretation, not automation. - Weave Ethics Throughout
Ethical reasoning should surface across multiple contexts—design challenges, data analysis, code reviews—not just in standalone modules. This fosters habitual, cross-disciplinary ethical awareness. - Focus on Conceptual Understanding
Assignments should ask students to explain why a solution works, what assumptions it relies on, and where it might fail. This maintains intellectual rigour and guards against superficial engagement. - Cultivate AI Fluency as Core Literacy
Students need to understand how generative models work, what limitations they carry, and how to evaluate their trustworthiness. This is a new form of engineering fluency, one that will define future readiness.
Choosing the Educational Future We Want
The goal of GenAI integration isn’t simply to update syllabi or incorporate new tools. It’s to shape a learning environment that prepares students not only to cope with change, but to lead it.
Engineering graduates will help design the systems and structures through which AI interacts with society. Their work will shape cities, industries, and interfaces—not just technologically, but socially and ethically. Our role as educators is to prepare them for that task with integrity, curiosity, and critical depth.
While GenAI can generate answers at astonishing speed, the enduring work of education is to nurture insight—and insight still belongs to the human.