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The Impact of Artificial Intelligence on Student Learning Motivation in American Colleges

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The Impact of Artificial Intelligence on Student Learning Motivation in American Colleges

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Stadt, 2026

Inhaltsverzeichnis

Algorithmic Personalization and the Self-Efficacy Loop
Linguistic Support and Confidence in EMI Courses
The Educator's Absence and the Risk of Disconnection
Cultural Perceptions and the Drive for Efficiency
Cognitive Friction and the Future of Academic Resilience
Conclusion
Bibliography

Einleitung

The lecture halls of American colleges are currently witnessing a silent transformation as students increasingly turn to automated systems to navigate their curricula. This shift represents more than a change in study habits; it reflects a foundational change in how motivation is sustained and directed. When a student interacts with a personalized learning platform, the immediate feedback loop can either catalyze a sense of mastery or foster a debilitating dependency. The stakes are high because motivation serves as the engine of academic persistence. If technology simplifies the learning process to the point of removing all intellectual struggle, the very resilience that higher education seeks to build might be compromised. This essay examines how these tools influence the inner drive of undergraduates, looking at the benefits of tailored support and the risks associated with the diminishing presence of human educators. By analyzing current research on personalization, linguistic aid, and student perceptions, a clearer picture emerges of a landscape where efficiency and engagement often pull in opposite directions. The core question remains whether these digital assistants serve as a bridge to deeper understanding or a shortcut that bypasses the necessary friction of intellectual growth.

Literaturverzeichnis

  1. IMPACT OF ARTIFICIAL INTELLIGENCE PERSONALIZED LEARNING ON STUDENT MOTIVATION AND ACADEMIC PERFORMANCE (2025)
    Hassan Imran
    DOI-Link
  2. Impact of Artificial Intelligence Tools on Learning Motivation in University EMI Courses: A Network Meta-Analysis (2026)
    Liwei Hsu, Yu-Chun Wang
    DOI-Link
  3. Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019)
    Olaf Zawacki‐Richter, Victoria I. Marín, Melissa Bond et al.
    DOI-Link

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