The results suggest that students’ intention to use AI tools is primarily driven by their perceptions of usefulness and ease of use, while metacognitive awareness plays an important role in shaping these perceptions.
Abstract
This study investigates the role of metacognitive awareness in shaping students’ adoption of artificial intelligence (AI) tools for learning within the framework of the Technology Acceptance Model (TAM). Data were collected from 236 students at the University of Ha’il, and structural equation modeling (SEM) was employed to examine the relationships among metacognitive awareness, perceived usefulness (PU), perceived ease of use (PEU), and behavioral intention (BI). The findings indicate that metacognitive awareness has significant positive effects on both PU and PEU. In turn, PU and PEU significantly influence students’ BI to use AI tools for learning, with PU emerging as the stronger predictor. However, metacognitive awareness does not have a significant direct effect on BI. These results suggest that students’ intention to use AI tools is primarily driven by their perceptions of usefulness and ease of use, while metacognitive awareness plays an important role in shaping these perceptions. The study extends the Technology Acceptance Model by positioning metacognitive awareness as an upstream cognitive factor that enhances students’ evaluation of AI technologies in educational contexts. The findings offer practical implications for higher education institutions, emphasizing the importance of developing students’ metacognitive skills to improve their engagement with AI tools. By fostering students’ ability to reflect on and regulate their learning processes, educators can enhance the perceived value and usability of AI technologies, thereby supporting their effective integration into learning environments.
The growing adoption of AI in higher learning institutions poses important questions regarding its effects on higher-order cognitive competencies of students. Although previous studies have investigated perceptions of learning influenced by AI, very little information exists regarding the cognitive processes involved i...
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Despite widespread artificial intelligence (AI) integration in mathematics education, the mechanisms through which student engagement translates into adoption behavior remain undertheorized. Existing technology acceptance frameworks overlook the pedagogical dimension of learner-AI interaction. Grounded in Vygotsky's Zo...
C. Ünal, Adem Duru· International Journal of Edu...· 0 citations
Metacognitive awareness, defined as the ability to plan, monitor, and evaluate one’s own learning, plays a crucial role in problem solving. It enables individuals to determine not only which strategies to use but also when and how to apply them effectively. This study aimed to examine the relationship between student t...
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The rapid integration of artificial intelligence (AI) into educational ecosystems necessitates a deeper understanding of how future teachers conceptualize AI-supported tools. This study examines pre-service teachers’ perceptions across four dimensions: perceived usefulness, digital self-efficacy, trust in AI, and perce...
Ciprian Baciu, Letiția Muntean-Trif· Revista Romaneasca pentru Ed...· 0 citations
Positive student acceptance alongside persistent technical and feedback-related challenges are indicated, highlighting the importance of AI literacy, responsible use and institutional support.
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