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The integration of Artificial Intelligence (AI) in education offers unprecedented opportunities for personalization, efficiency, and innovation, yet resistance to adoption persists among Generation Z learners. This study investigates the structural interplay of ten key barriers to AI adoption in higher education, integrating the Technology Acceptance Model, Resistance to Innovation Theory, and Cognitive Load Theory. Using Interpretive Structural modelling (ISM) and MICMAC analysis on survey data from 118 Gen Z respondents, the research identifies lack of trust in AI, data privacy concerns, and algorithmic bias as high-driving factors, influencing dependent barriers such as low perceived usefulness and poor user experience. Digital fatigue and cognitive overload emerge as central linkage variables, mediating relationships between upstream drivers and downstream disengagement. Findings highlight the pivotal role of faculty support and pedagogical integration in shaping adoption attitudes. The study reframes resistance not as reluctance, but as a signal of systemic misalignment between learner expectations, institutional practices, and AI design. Practical implications call for transparent, inclusive, and cognitively considerate AI tools, robust governance frameworks, and AI literacy initiatives to foster equitable and sustainable adoption.

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Mapping Resistance to AI in Education: A Structural Analysis of Gen Z Adoption Barriers

  • 会议论文

  • 发表日期：

    2026年

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  • V. R. Aishwini,
  • B. Aravindan,
  • Santanu Mandal

摘要

The integration of Artificial Intelligence (AI) in education offers unprecedented opportunities for personalization, efficiency, and innovation, yet resistance to adoption persists among Generation Z learners. This study investigates the structural interplay of ten key barriers to AI adoption in higher education, integrating the Technology Acceptance Model, Resistance to Innovation Theory, and Cognitive Load Theory. Using Interpretive Structural modelling (ISM) and MICMAC analysis on survey data from 118 Gen Z respondents, the research identifies lack of trust in AI, data privacy concerns, and algorithmic bias as high-driving factors, influencing dependent barriers such as low perceived usefulness and poor user experience. Digital fatigue and cognitive overload emerge as central linkage variables, mediating relationships between upstream drivers and downstream disengagement. Findings highlight the pivotal role of faculty support and pedagogical integration in shaping adoption attitudes. The study reframes resistance not as reluctance, but as a signal of systemic misalignment between learner expectations, institutional practices, and AI design. Practical implications call for transparent, inclusive, and cognitively considerate AI tools, robust governance frameworks, and AI literacy initiatives to foster equitable and sustainable adoption.