Marketing Education Review

Teaching Across Generations: Technology Innovations for Experiential Learning in Marketing Education with a Diverse Learner Landscape (Teaching Innovations 2026)

Submissions Open: July 1, 2025

In today’s marketing classrooms, either the traditional in-person or an online or a hybrid modality, educators stand at a critical crossroads defined by both generational divergence and technological transformation. No longer homogenous, the learner population spans three distinct cohorts: Generation X digital immigrants, Millennial digital adopters, and Generation Z digital natives (Prensky, 2001). These groups differ not just by age but by how they engage with technology, process information, and define learning success. As educators, we are particularly interested in pedagogical work that uses technology and adaptive methods to guide and support the student’s skill acquisition and higher-level learning objectives. In a recent study, for instance, Gochenouer, Rickgarn, and Huang (2024) explore Generation Z students’ e-learning and technology adoption and their communication preferences with professors to facilitate their e-learning. They find that the primary technology that Generation Z students use to seek general information is related to the artificial intelligence (AI) based technology that they may adopt for e-learning, such as ChatGPT. The technology that students use for e-learning is related to their awareness and knowledge of those e-learning tools.

Kolb (1984) defines experiential learning as a process whereby knowledge is created through the transformation of experience, often ex-pressed as learning-by-doing. In marketing or other business context, experiential learning is a pedagogical framework for learning-by-doing that emphasizes longitudinal skill development and proficiency gained through focused, repetitive practice under real world-like conditions. As Prensky (2001) emphasizes, digital natives - those raised in technology-rich environments - possess cognitive and behavioral preferences that often conflict with the pedagogical models traditionally favored by digital immigrants. For example, Gen Z students tend to prefer visual information, multitask naturally, and expect real-time feedback - behaviors shaped by growing up in hyperconnected digital ecosystems. Thus, critical to experiential learning is instructional scaffolding across an ecosystem of experiential resources that promote skill development, including games and simulations delivered using virtual-, augmented-, and mixed-reality based applications. Consequently, marketing educators need to seek methods that can contribute to how technology-based educational methods can be applied to optimize experiential learning in modern classrooms for advertising, sales, promotion and communications, retailing, and so on.

Furthermore, instructional support, feedback, and coaching are critical for experiential learning. Determining when, how, and what forms of feedback and support to deliver to facilitate effective learning remain open areas of pedagogical research in marketing education. For instance, Gochenouer et al. (2024) find that the students’ preference of communication methods with the professors related to their knowledge about different types of e-learning tools. When considering experiential learning and longitudinal skill development, assisting students in defining what they should practice next, and how best to practice it is a useful question that AI can help through objective analysis and decision support functions.

Additionally, the intergenerational gaps are further amplified by the rapid integration of emerging technologies such as generative AI, adaptive learning systems, and immersive platforms into higher education. While Generation Z leads in both confidence and frequency of AI tool usage, older cohorts - including many instructors - struggle with uptake, leading to a noticeable “AI adoption gap” in the classroom (Singh & Huang, 2025; Strauss & Hill, 2007). This disparity raises critical questions about equity, engagement, and instructional design. From this perspective, a learning experience must not only be designed to support task execution; it must also be designed to allow for context-specific monitoring and assessment of foundational behaviors, processes, and procedures across a set of tasks, conditions, and standards. Furthermore, to support experiential learning, a student requires several experiential opportunities under variations in condition and complexity (Kolb, 1984), thus creating a content creation and curation challenge. As a result, the issue is not only technical but also pedagogical: how do we create inclusive learning environments that respect cognitive and digital diversity while leveraging the benefits of AI and other advanced tools?

The complexity of designing and assessing experiential learning and the technology-enabled, data-rich environments that make experiential learning an ideal candidate for using AI. It can assist in the design, delivery, and evaluation of experiential activities that contribute to longer-term skill and proficiency objectives and to enhance learning. To meet this challenge, marketing educators must evolve from content-centered pedagogy toward learner-centered andragogy - a shift that prioritizes autonomy, relevance, and problem-solving (Knowles, 1980). Moreover, recent research supports adopting constructivist approaches that frame students as co-creators of knowledge, especially in technology-rich environments (Smith & Johnson, 2024). These approaches can be particularly effective in navigating the intergenerational dynamics of today’s classrooms by offering flexible, adaptive strategies that align with students’ lived experiences.

Last but not least, predictive learner analytics is another growing area of interest in AI in education. By observing learner behaviors and outcomes over time and across multiple environments, and by using sources of data that provide evidence of learning, AI can be applied to develop predictive models of learner and team competency state, which will help inform next-step pedagogical decisions and provide in-sights for talent management (Namoun & Alshanqiti, 2020). Evaluating the quality of a learning event on overall competency acquisition is also a critical function to enable self-optimizing learning environments designed for experiential learning.

This special issue invites submissions that explore how marketing education can meaningfully adapt to intergenerational diversity and technological disruption. We seek contributions that go beyond tool-centric examinations of AI to consider broader questions of pedagogy, inclusion, faculty readiness, and long-term learning outcomes. In particular, we encourage research that examines how instructional strategies, classroom technologies, assessment models, and faculty development initiatives can be redesigned to engage all learners - from Boomers to Zoomers - in a shared, equitable learning experience.

We invite empirical research, conceptual papers, case studies, and pedagogical innovations related to, but not limited to, the following thematic areas and sample questions:

1. Generational Differences in Student Learning and Engagement

• What do Gen X, Y, and Z students expect from educational technology, and how do those expectations influence engagement and motivation?
• How do generational identities shape students' perceptions of instructor authority, feedback, or peer collaboration?
• What longitudinal or comparative evidence exists on how generational diversity impacts classroom dynamics, satisfaction, and performance?

2. Instructional Modality and Delivery Preferences

• What course formats (e.g., in-person, hybrid, asynchronous) are preferred by students at different life stages, and how do these preferences influence participation and learning outcomes?
• How can course design be made more flexible to accommodate diverse schedules, work commitments, and technological competencies?
• How do digital preferences and cognitive styles differ across generational cohorts?

3. Blending Pedagogy and Andragogy

• What teaching strategies help balance structured guidance with self-directed learning across generations?
• In what ways can constructivist methods (e.g., experiential learning, student co-creation) support both traditional and adult learners?
• What does an effective “hybrid model” of pedagogy and andragogy look like in multigenerational classrooms?

4. Inclusive Curriculum and Assessment Design

• How can marketing educators create assignments that are accessible and meaningful for both traditional undergraduates and mid-career learners?
• What kinds of assessment practices (e.g., oral defense, iterative drafts, peer evaluations) support inclusive learning across age groups?
• How do students’ career experiences influence their interpretation of academic rigor and relevance?

5. Technology Use and Digital Fluency

• How do generational cohorts differ in their use of classroom technologies, including LMS platforms, simulations, and generative AI?
• What low-barrier or alternative tech tools can support digital laggards without disadvantaging digital natives?
• How does AI-based personalization or automation affect perceived learning and engagement across student age groups?

6. Faculty Development and Institutional Readiness

• What support systems help faculty adapt their teaching to multigenerational audiences?
• How do faculty attitudes and training influence their ability to manage generational diversity in the classroom?
• What institutional incentives or professional development programs foster pedagogical innovation across generations?

7. Institutional Challenges and Opportunities

• What barriers - technological, policy-driven, or cultural - hinder institutions from fully addressing generational needs in marketing education?
• What models of course innovation or curriculum reform have proven scalable and inclusive for diverse learner populations?

(contact Guest Editors for list of references)