Conceptualizing AI dependency as the primary mechanism shaping students' academic impact - Melisa Aulina Sihite; Darwis Robinson Manalu (Eduvista: Journal of Education and Innovation, 2026)
The rapid adoption of Artificial Intelligence (AI) in higher education has intensified debate regarding its impact on students' academic abilities, while previous studies have not adequately explained the mechanism through which AI use evolves into dependency that shapes academic outcomes. This study aims to examine the effects of AI dependency and the drivers of AI utilization on students' academic impact while proposing a conceptual model explaining their relationships. A quantitative explanatory survey was conducted involving 81 undergraduate students selected through purposive sampling, and the data were analyzed using multiple linear regression. The findings reveal that AI dependency has a positive and significant effect on students' academic impact (β = 0.643, p < 0.001), whereas the drivers of AI utilization have no significant partial effect (p = 0.066). The proposed model explains 57.0% of the variance in academic impact (R² = 0.570). This study contributes by developing a conceptual model demonstrating that academic consequences emerge through the transformation of AI utilization into dependency, which subsequently alters students' learning behavior. Abstrak Perkembangan Artificial Intelligence (AI) di pendidikan tinggi memunculkan perdebatan mengenai dampaknya terhadap kemampuan akademik mahasiswa, sementara penelitian sebelumnya belum menjelaskan mekanisme bagaimana penggunaan AI berkembang menjadi ketergantungan yang memengaruhi aktivitas akademik. Penelitian ini bertujuan menganalisis pengaruh ketergantungan penggunaan AI dan faktor-faktor pendorong penggunaan AI terhadap dampak akademik mahasiswa serta menyusun model konseptual yang menjelaskan hubungan antarvariabel tersebut. Penelitian menggunakan pendekatan kuantitatif dengan desain explanatory survey terhadap 81 mahasiswa yang dipilih melalui purposive sampling, kemudian dianalisis menggunakan regresi linear berganda. Hasil penelitian menunjukkan bahwa ketergantungan penggunaan AI berpengaruh positif dan signifikan terhadap dampak akademik mahasiswa (β = 0,643; p < 0,001), sedangkan faktor pendorong penggunaan AI tidak berpengaruh signifikan secara parsial (p = 0,066). Model penelitian mampu menjelaskan 57,0% variasi dampak akademik mahasiswa (R² = 0,570). Penelitian ini berkontribusi dengan menawarkan model konseptual yang menjelaskan bahwa dampak akademik terbentuk melalui proses berkembangnya penggunaan AI menjadi ketergantungan yang kemudian mengubah perilaku belajar mahasiswa.