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How Marketing Professors Are Rethinking AI in the Classroom
Artificial intelligence is already changing how marketing students research, brainstorm, analyze information, create content, and complete assignments. The bigger question for higher education is no longer whether students will use AI, but how professors can teach them to use it without replacing the critical thinking, creativity, and professional judgment they are in college to develop. That question brought marketing educators together for a recent QuantHub roundtable focused on AI, marketing education, and preparing students for an increasingly AI-enabled workforce.
The discussion was led by members of the QuantHub team, including Jordan Hernandez, who works with higher education faculty and academic leaders to integrate AI and data literacy into courses; Kellie Weed, Director of Go-to-Market at QuantHub; and John Mark Norton of QuantHub’s marketing team. They were joined by marketing professors including Dr. Kimberly McNeil of North Carolina A&T State University, Dr. Hamid Abbasi of Fayetteville State University, and faculty from Lehigh University and other institutions. Participants brought perspectives from consumer behavior, sales, digital marketing, MBA courses, and other areas of marketing education.
What were the key takeaways from the marketing and AI roundtable? Faculty largely agreed that students need to learn how to work with AI rather than simply use it to produce answers. That means making students’ reasoning visible, teaching the limitations of AI before asking students to rely on it, evaluating the process behind an assignment rather than only the polished final product, and helping students identify the uniquely human value they bring to AI-assisted work. As AI capabilities continue to evolve, the goal is not simply AI literacy—it is developing marketers who can question, evaluate, explain, and improve what AI produces.
How Should Marketing Students Be Using AI?
One of the clearest themes of the discussion was that giving students access to AI does not automatically produce better thinking. Faculty described using generative AI for brainstorming, developing questions, exploring pricing strategies, generating product names, and improving assignments. But they have also seen students accept initial outputs without asking follow-up questions or investigating whether an answer actually makes sense. That challenge reinforces the need to teach AI critical thinking alongside the tools themselves.
One professor described an assignment that required students to submit screenshots of their conversations with AI. The exercise revealed an important distinction: some students continued questioning the model, refining their prompts, and exploring ideas, while others stopped after receiving an initial response. As the professor explained, many students lacked the “motivation or imagination to ask follow-up questions and go deeper and deeper.” The lesson was not that students should avoid AI, but that curiosity, questioning, and iteration have to be deliberately developed alongside AI proficiency.
Dr. Kimberly McNeil described a similar approach in sales education. Before allowing students to use AI to develop needs-discovery questions for a prospect, she first has them generate questions independently and discuss them with classmates. Only then do students bring AI into the process to develop “better questions” and create additional follow-up and elaboration questions. The sequence keeps students involved in the thinking while demonstrating how AI can strengthen work they have already begun.
What Skills Do Marketing Students Need in an AI-Enabled Workforce?
Knowing how to prompt an AI model is only one part of AI readiness. Students also need enough conceptual understanding to know why an AI system produces certain results, when its output should be trusted, and where human expertise needs to take over. One professor described teaching concepts such as retrieval-augmented generation (RAG) without turning a marketing course into a computer science course. Rather than focusing on coding, he connects the underlying concept to marketing decisions—for example, considering when internal company data may provide more reliable context or when greater variability could actually benefit creative work.
That distinction is increasingly important because different marketing responsibilities call for different relationships with AI. Marketing analytics, consumer research, sales, communications, and creative work do not require identical capabilities. Faculty discussed asking students to separate their professional responsibilities into tasks that could potentially be automated and tasks that depend on their individual experience, judgment, creativity, or taste. The exercise forces students to think beyond the question of whether AI will “take jobs” and instead identify where they personally create value.
One professor summarized that expectation particularly clearly: “If you cannot tell me what it is that you have added, then there is no use.” He applies the same standard to student assignments, asking students to explain what AI produced, what they contributed, and how they improved the result. This emphasis on human contribution reflects a broader shift explored in QuantHub’s What Is AI Literacy Training in Higher Education?: preparing students for AI-enabled work requires more than familiarity with technology. It requires the judgment to use that technology purposefully.
How Should Professors Assess Student Learning When AI Can Produce the Final Product?
Generative AI has also complicated a familiar question for educators: What does a finished assignment actually prove a student knows? AI can help produce polished presentations, campaign recommendations, reports, and written assignments in seconds. As Jordan Hernandez noted during the discussion, the quality of the final product still matters, but it should not be the only evidence of learning. Instead, instructors can ask students to show parts of their process: an original brief, a meaningful AI output, their critique of that output, and an explanation of the final decision.
Faculty shared several ways they are already moving in that direction. One professor requires students to meet before selecting projects, return for another checkpoint as they develop their plans, present their work, review their classmates’ presentations, seek feedback from an AI tool, and then revise the final submission. Students are specifically asked to explain what feedback they received and what they changed because of it. The result shifts assessment away from a single finished artifact toward evidence of iteration, decision-making, and improvement.
As the discussion continued, one professor predicted that education may place increasingly greater weight on the process behind student work because AI can make final outputs look increasingly similar. Students may need to defend why they selected an audience, explain an analysis, critique what AI missed, or justify why they rejected an AI recommendation. Dr. McNeil connected this directly to workforce preparation: employees will increasingly use AI and automated analytical tools, but they will still need to “explain what it spits out.”
What Changes When AI Can Act on Behalf of Marketers?
The conversation also moved beyond generative AI to agentic AI, where systems can do more than generate content and begin taking actions or interacting with customers. For marketing educators, that creates a new set of questions around autonomy, brand responsibility, customer trust, ethics, and human oversight. If an AI system can respond to a customer, recommend a product, or take another action on behalf of an organization, students need to understand not only how the technology works but when a person should remain in the decision loop.
Faculty repeatedly returned to the importance of human checkpoints. Routine organizational or administrative tasks may be increasingly appropriate for automation, but unusual, sensitive, creative, or customer-facing decisions may require escalation to a person. One participant drew an especially useful distinction: AI can be effective for “organizing or summarizing and integrating,” but when something carries a person’s name, intellectual property, judgment, or point of view, the human should be a co-creator rather than simply an approver. “Ultimately you are responsible,” he said.
For educators, this makes responsible AI use a moving target. Tools available today may look very different within months, making it difficult to design courses around individual platforms or features. The more durable approach is teaching students how to continuously learn, evaluate new technologies, understand risk, and decide when human intervention is necessary. That same focus on durable judgment and workforce readiness has emerged across disciplines, including in QuantHub’s conversations with information systems faculty about what AI means for the future of MIS education.
How Can Universities Prepare Marketing Students for an AI-First Workplace?
AI education in marketing ultimately cannot be reduced to a universal policy or a single tool. As one professor pointed out, using Grammarly to polish language may be completely appropriate in a marketing plan while being inappropriate for an assignment specifically designed to assess grammar. The same principle applies across marketing disciplines: what works in advertising may not work in sales or marketing research. “One size may not fit all,” he explained, meaning faculty will need discipline- and assignment-specific approaches to responsible AI use.
What can remain consistent is the underlying goal. Students should leave college able to use AI, evaluate its output, recognize its limitations, communicate their reasoning, and demonstrate the value they add beyond the technology itself. Faculty even discussed encouraging students to demonstrate AI competency on their résumés through relevant certifications and real projects where they can explain how they used AI and how they added value to its output. Those experiences can turn AI proficiency from an abstract classroom concept into evidence of workforce readiness.
QuantHub helps higher education faculty bring those skills into courses students are already taking through structured AI and data learning that supports prompting, evaluating AI outputs, understanding AI’s role within disciplines such as marketing, and developing practical judgment. As Hernandez explained at the close of the roundtable, the goal is to give students a foundation they can apply to faculty assignments without requiring professors to build every AI lesson from scratch. For colleges and universities exploring how to prepare students for AI-enabled careers, explore QuantHub’s higher education AI and data literacy resources and discover ways to integrate practical AI fluency into existing coursework.