EVALUASI EFEKTIVITAS SISTEM UMPAN BALIK BERBASIS AI DALAM MENINGKATKAN HASIL BELAJAR MAHASISWA
DOI:
https://doi.org/10.51878/edutech.v4i3.3142Keywords:
Umpan balik berbasis AI, hasil belajar, teknologi pendidikanAbstract
This study aims to evaluate the effectiveness of an AI-based feedback system in enhancing student learning outcomes at the Faculty of Education and Psychology, Mandalika University of Education. A quantitative survey method with a descriptive approach was employed on 238 students to measure various aspects of learning, including material comprehension, learning motivation, and system accessibility. Data were analyzed using descriptive statistics, Shapiro-Wilk normality test, and Pearson correlation analysis. The results indicate that the AI-based feedback system significantly facilitates material comprehension (mean = 3.134) and enhances students' learning motivation (mean = 3.067), with data distribution approximating normality (p < 0.001). However, the instrument reliability measured by Cronbach's alpha was very low (0.001), indicating a need for improvements in instrument design. This study concludes that the AI-based feedback system is effective in improving various aspects of learning, but further evaluation is needed to enhance the instrument's internal consistency and optimize its implementation in higher education contexts. These findings provide significant contributions to the development of educational technology and the formulation of policies supporting AI integration in the learning process.
ABSTRAK
Penelitian ini bertujuan untuk mengevaluasi efektivitas sistem umpan balik berbasis kecerdasan buatan (AI) dalam meningkatkan hasil belajar mahasiswa di Fakultas Ilmu Pendidikan dan Psikologi Universitas Pendidikan Mandalika. Metode survei kuantitatif dengan pendekatan deskriptif digunakan pada 238 mahasiswa untuk mengukur berbagai aspek pembelajaran, termasuk pemahaman materi, motivasi belajar, dan aksesibilitas sistem. Data dianalisis menggunakan statistik deskriptif, uji normalitas Shapiro-Wilk, dan analisis korelasi Pearson. Hasil penelitian menunjukkan bahwa sistem umpan balik berbasis AI secara signifikan memudahkan pemahaman materi (mean = 3,134) dan meningkatkan motivasi belajar mahasiswa (mean = 3,067), dengan distribusi data yang mendekati normal (p < 0,001). Meskipun demikian, reliabilitas instrumen yang diukur dengan Cronbach's alpha menunjukkan nilai yang sangat rendah (0,001), mengindikasikan perlunya perbaikan dalam desain instrumen. Penelitian ini menyimpulkan bahwa sistem umpan balik berbasis AI efektif dalam meningkatkan berbagai aspek pembelajaran, namun memerlukan evaluasi lebih lanjut untuk meningkatkan konsistensi internal instrumen dan mengoptimalkan implementasinya dalam konteks pendidikan tinggi di Indonesia. Temuan ini memberikan kontribusi penting bagi pengembangan teknologi pendidikan dan penyusunan kebijakan yang mendukung integrasi AI dalam proses pembelajaran.
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