AI Anxiety and the General Extended Technology Acceptance Model of e-Learning: A Conceptual Framework

Artificial intelligence (AI) is rapidly reshaping marketing education through adaptive learning systems, automated feedback, and generative tools such as ChatGPT. As students increasingly use generative AI to support tasks such as brainstorming, drafting, and data analysis, instructors are simultaneously experimenting with AI-assisted feedback, grading support, and course design. Despite these benefits, adoption remains uneven due to psychological responses that influence how students and faculty engage with AI-enabled systems. This conceptual study extends the General Extended Technology Acceptance Model of e-Learning (GETAMEL) by introducing AI Anxiety as a multidimensional construct capturing cognitive, affective, and behavioral reactions to autonomous and opaque technologies. The framework positions AI Anxiety as a conditioning mechanism that shapes how perceived usefulness and perceived ease of use translate into behavioral intention. Individual factors (e.g., self-efficacy, privacy concerns, generational cohort) and contextual influences (e.g., institutional support, faculty adoption, classroom norms surrounding generative AI use) further shape these relationships. By refining technology acceptance theory for intelligent systems, this research offers a foundation for future empirical testing and provides practical guidance for fostering trust, transparency, and psychologically safe AI integration in marketing education.