AI in Marketing Education: A Multistakeholder perspective
(A2026-134046)

AUTHORS

Anett Erdmann, Universidad Pontificia Comillas/Universidad Pontificia Comillas; Isabel Carrero, Universidad Pontificia Comillas; DIANA GAVILAN, Universidad Complutense de Madrid; Orzan Mihai, BUCHAREST UNIVERSITY OF ECONOMIC STUDIES; Carlos Alberto Pérez Rivero, ESIC University

ABSTRACT

Generative AI (GenAI) is fundamentally reshaping the marketing profession by altering how value is created and exchanged in the marketplace (Grewal et al., 2025). This technological transformation enhances analytical, technological, and strategic efficiency capabilities in marketing processes (Kumar et al., 2024), influencing core domains like consumer research ideation (De Freitas et al., 2025), relationship marketing via AI companions (Chaturvedi et al., 2024), and is omnipresent in all Marketing areas (advertising, pricing and sales,…). Initially met with fear and defensiveness among educators, the focus is now shifting toward integrating GenAI to enrich the learning environment (Acar, 2024), exemplified by advances such as automated analytical feedback for scalable marketing analytics assignments (Jürgensmeier & Skiera, 2024). As professional practices evolve, an urgent need arises to strategically reform marketing education and close the widening gap between curricula and industry demands, through a human-centric and multistakeholder approach considering the behavioural shift and impact of AI in the Marketing education ecosystem. This special session, being transversal to all EMAC track sessions, addresses three main perspectives in four papers 1. Student engagement as an outcome shaped by task design and structured interaction with AI tools. This focus must address critical risks such as over-reliance, and the emergence of impostor feelings (Article 1), and the context and impact of metacognitive laziness (Article 2) 2. Implications for faculty, requiring enhanced AI literacy and digital competencies to sustain learning quality and meet the new competence framework (Article 3) 3. Ultimately, the success of institutional AI integration—or lack thereof (institutional readiness)—shapes the perception of Alumni and influences the university’s long-term brand equity (Article 4). The session brings together perspectives from students, faculty, and alumni, and draws on evidence from meta-analyses, experiments, structural modelling, and interview-based topic modelling to offer a well-rounded view of GenAI’s role in marketing education. Alongside presenting these findings, the session aims to open a constructive discussion, led by the chairs and an invited expert working at the intersection of AI and education.