Abstract:

The growing integration of artificial intelligence (AI) into higher education has reshaped learning environments across a wide range of disciplines, including medical education, mathematics education, and digital learning. AI technologies—such as intelligent tutoring systems, adaptive learning platforms, automated assessment, and generative AI tools—are no longer supplementary resources but have become deeply embedded in students’ daily learning practices. As AI continues to expand in scope and accessibility, it plays a dual role: it enhances students’ opportunities for personalized learning and competency development, while simultaneously introducing new challenges related to technological adaptation, cognitive load, and evolving expectations within academic environments.

For university students, navigating AI-supported learning requires not only technical proficiency but also critical understanding, ethical judgment, and the ability to leverage AI as a meaningful learning partner rather than a passive tool. These demands contribute to the increasing complexity of the modern learning experience. Meanwhile, learning burnout has emerged as a significant concern in an increasingly digitalized and fast-paced society. Existing studies have shown that burnout among students can manifest in emotional exhaustion, reduced academic engagement, and a diminished sense of accomplishment, all of which can negatively affect psychological well-being, motivated learning, and long-term personal development.

Within this context, motivated learning becomes a pivotal component linking AI literacy and learning burnout. Motivated learning influences how students approach new technologies, how they persist in the face of challenges, and how they internalize their learning goals amid rapid technological change. Students with higher levels of motivated learning may view AI as a resource that supports autonomy and growth, whereas those with lower levels may experience AI-mediated learning as overwhelming or disengaging, thereby increasing susceptibility to burnout.

Recognizing the complex and interdependent nature of these constructs, this study aims to construct a comprehensive conceptual framework that explains how AI literacy, motivated learning, and learning burnout interact within the context of higher education. Rather than adopting a purely quantitative or correlational approach, the study synthesizes existing literature, theoretical foundations, and contemporary perspectives to build an integrative model that reflects current educational realities. Through this conceptual framework, the study seeks to provide researchers with a foundation for future investigations and offer educators deeper insights into how they may support students’ well-being, motivated learning, and technological competence in AI-enhanced learning environments.

Keywords:

Artificial Intelligence Literacy, academic burnout, motivated learning.