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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

Author:

Group

First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

The Motivational Power of Personalized Learning Pathways
The Missing Human Element and Institutional Challenges
Motivational Dynamics in Language and EMI Contexts
Student Perceptions and the Risk of Over-Reliance
Conclusion
Bibliography

Johdanto

The lecture halls of American universities are no longer defined solely by the exchange between professor and student. Instead, a silent intermediary—Artificial Intelligence—has begun to dictate the pace, content, and emotional resonance of the learning experience. As institutions integrate generative models and adaptive platforms into their core curricula, a fundamental question arises regarding the psychological drive of the students themselves. Motivation is not a static resource; it is a sensitive state influenced by the perceived relevance of the material and the level of support provided by the environment. While early proponents suggested that AI would revolutionize engagement through efficiency, the reality is far more nuanced. The introduction of these technologies creates a paradox where the removal of academic friction can both empower a learner and strip away the struggle necessary for genuine intellectual satisfaction. Understanding this impact requires looking beyond technical capabilities to the core of how students find meaning in their studies. By examining the shift toward personalized pathways, the absence of human-centric design, and the specific pressures of language-intensive courses, one can see that AI is not merely a tool but a transformative force that alters the very nature of student persistence.

References

  1. IMPACT OF ARTIFICIAL INTELLIGENCE PERSONALIZED LEARNING ON STUDENT MOTIVATION AND ACADEMIC PERFORMANCE (2025)
    Hassan Imran
    DOI-linkki
  2. Impact of Artificial Intelligence Tools on Learning Motivation in University EMI Courses: A Network Meta-Analysis (2026)
    Liwei Hsu, Yu-Chun Wang
    DOI-linkki
  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-linkki

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SFS 5989 (Finnish Citation)