PENGARUH ACADEMIC SELF-EFFICACY TERHADAP ARTIFICIAL INTELLIGENCE DEPENDENCY DENGAN ACADEMIC STRESS SEBAGAI VARIABEL INTERVENING - Dina Yuliana; Triesninda Pahlevi (Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I), 2026)
This study focuses on examining the impact of academic self-efficacy on artificial intelligence dependency, while also investigating the role of academic stress as a mediating factor among students in the Office Administration Education Program at Surabaya State University. The rapid advancement of AI technology is now fueling a growing pattern of dependency among students, necessitating a deeper exploration from an academic perspective. We employed a quantitative method with an explanatory design. Data were collected via a questionnaire distributed to 274 respondents and analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) through the SmartPLS 4 software. The results indicate that: (1) academic self-confidence negatively and significantly influences AI dependency; (2) academic self-confidence also negatively and significantly reduces academic stress; (3) academic stress, conversely, positively and significantly increases AI dependency; and (4) academic stress significantly mediates the relationship between academic self-confidence and AI dependency. In conclusion, the higher the academic self-confidence, the lower the stress experienced by students, which ultimately helps reduce excessive dependence on AI technology. ABSTRAK Penelitian ini difokuskan pada pengkajian dampak rasa percaya diri akademik (academic self-efficacy) terhadap ketergantungan pada kecerdasan buatan (artificial intelligence dependency), sambil meneliti peran stres akademik sebagai faktor mediasi di kalangan mahasiswa Program Studi Pendidikan Administrasi Perkantoran di Universitas Negeri Surabaya. Kemajuan cepat teknologi AI kini memicu pola ketergantungan mahasiswa yang semakin kuat, sehingga perlu dieksplorasi lebih dalam dari sisi akademiknya. Kami menerapkan metode kuantitatif dengan rancangan eksplanatori. Data diperoleh lewat kuesioner yang disebarkan ke 274 responden, lalu diolah pakai Partial Least Squares–Structural Equation Modeling (PLS-SEM) melalui software SmartPLS 4. Dari hasilnya, terlihat bahwa: (1) rasa percaya diri akademik secara negatif dan signifikan memengaruhi ketergantungan AI; (2) rasa percaya diri akademik juga menekan stres akademik secara negatif dan signifikan; (3) stres akademik justru meningkatkan ketergantungan AI secara positif dan signifikan; serta (4) stres akademik terbukti memediasi hubungan antara rasa percaya diri akademik dan ketergantungan AI dengan cara yang signifikan. Kesimpulannya, semakin tinggi keyakinan diri dalam urusan akademik, semakin rendah stres yang dialami mahasiswa, yang akhirnya membantu mengurangi ketergantungan berlebih pada teknologi AI.