When AI Wears Many Hats: The Role of Generative Artificial Intelligence in Marketing Education

Synthesis: When AI Wears Many Hats: The Role of Generative Artificial Intelligence in Marketing Education — Uses multipronged analysis (syllabi review, educator survey, qualitative interviews) and Role Theory + Community of Inquiry model to propose three GAI roles in marketing education: tutor (grasping theoretical concepts), teammate (brainstorming and Problem Solving), and tool. Each role influences Teaching, social, and cognitive presence differently. Identifies ethical considerations: data Privacy, plagiarism, AI dependency, and assessment fairness. Provides concrete examples for GAI integration in courses.

Uses multipronged analysis (syllabi review, educator survey, qualitative interviews) and Role Theory + Community of Inquiry model to propose three GAI roles in marketing education: tutor (grasping theoretical concepts), teammate (brainstorming and problem-solving), and tool. Each role influences teaching, social, and cognitive presence differently. Identifies ethical considerations: data Privacy, plagiarism, AI dependency, and assessment fairness. Provides concrete examples for GAI integration in courses.

Abstract

Generative Artificial Intelligence (GAI) is increasingly being integrated into marketing education and is reshaping the skillsets required in marketing careers. Building on Role Theory and the Community of Inquiry (CoI) model, we propose that GAI can assume three roles in marketing education: tutor, teammate, and tool. Each role influences teaching, social, and cognitive presence differently, shaping the learning experience and preparing workplace-ready marketing graduates.

What this means for practice

• Instructors. Name which role GAI plays in each activity — tutor, teammate, or tool — in the syllabus and assignment briefs, because each role shifts teaching, social, and cognitive presence differently.
• Instructors. Write an explicit GAI use statement that sets permitted and prohibited uses and names the tool's limitations, such as the "Research Assistant, Proofreader, Peer reviewer, Tutor and Cultural Coach" menu reported from practice, instead of leaving disclosure implicit.
• Instructors. Protect peer interaction when GAI takes the teammate role: over-reliance risks diminishing peer-to-peer interaction and weakening teaching presence, so pair the brainstorming with a collaborative follow-up.
• Faculty developers. Deliver role-specific GAI training rather than one-off tool demos, since the surveyed faculty were early adopters and their reported effectiveness is unlikely to represent the wider teaching population.
• Administrators. Rewrite program assessment criteria and policy together so they balance GAI-enabled innovation with core marketing competencies, and back them with ethical implementation guidelines.

Limitations

• The syllabi review covered 11 distinct marketing courses and the faculty survey drew n=14 full-time faculty, all at a single large midwestern U.S. public university.
• Courses and instructors were a convenience sample, which the authors note limits generalizability to institutions with different resources, student populations, or cultural settings.
• Data collection was cross-sectional during a period of rapid GAI change, so adoption dynamics over time are not captured.
• Survey respondents were primarily early adopters, which may overestimate GAI's perceived effectiveness across the broader marketing education community.

Citation

Unnati Narang, Vishal Sachdev, & Ruichun Liu (2026). When AI Wears Many Hats: The Role of Generative Artificial Intelligence in Marketing Education. Journal of Public Policy & Marketing, 44(3), 473-489 (2025).