AI disruption: understanding performance and learning motivation of generation Z

Claude Chammaa () and Marie Haikel-Elsabeh ()
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Claude Chammaa: UniLaSalle
Marie Haikel-Elsabeh: IMT-BS - MMS - Département Management, Marketing et Stratégie - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]

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Abstract: The emergence of AI raises numerous concerns, including unemployment, lack of transparency, and environmental impact, among others. We are entering a transformative era marked by a significant shift from traditional tools to AI-driven technologies that revolutionize tasks. AI is playing a disruptive role in education and redefining its boundaries and evolution. Despite recent research on artificial intelligence in education—such as the systematic reviews by Zhang et al. (2024) and the empirical study by Atabekova et al. (2024)—there remains a notable gap in the literature regarding the impact of AI on young learners' motivation and their engagement in achieving personal learning goals. Our research aims to explore Generation Z's future perspectives, and challenges by conducting a quantitative study grounded in three theories: Facilitating conditions and technology attributes are grounded in the UTAUT model (Venkatesh et al., 2003), which explains technology acceptance. Engagement, autonomy, and learning motivation are based on Self-Determination Theory (Deci & Ryan, 1985), emphasizing intrinsic motivation. Self-efficacy, goal commitment, and performance outcomes are informed by Expectancy Theory (Vroom, 1964), focusing on the effort–performance–reward relationship. Each variable is theoretically rooted to provide a cohesive model. The combination of these theories offers a comprehensive view of how AI impacts learning behavior and outcomes. The study employs a structural equation modeling (SEM) approach to analyze the relationships between key factors such as Facilitating Conditions, Engagement, Self-Efficacy, Autonomy, Goal Commitment, Learning Motivation, and Technology Attributes. Results highlight how these constructs collectively drive Performance Outcomes, shedding light on the mechanisms through which AI impacts personal and professional trajectories for Generation Z. We can understand their future orientations by comprehending how AI transfigures and changes their ability to learn and reach their objectives.

Keywords: Engagement; Motivation; Artificial intelligence; Generation Z; Learning (search for similar items in EconPapers)
Date: 2025-05-21
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Published in AIM 2025 : 30ème Conférence annuelle de l'Association Information et Management. "Évolutions et perspectives des systèmes d'information dans les organisations et sociétés en transition", Association Information et Management (AIM), May 2025, Lyon, France

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05085739

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