Development of Edusmart as an AI-Based Adaptive Learning Application for Needs Analysis Among Indonesian Primary School Students - sunaryati, Titin Sunaryati; Widodo, Edi; Firdaus, Ahmad; Sudharsono, Muhamad; Rayyan, Muhammad Faiz (Faculty of Educational Sciences, UIN (State Islamic University) Syarif Hidayatullah Jakarta, 2025)
Abstract Digital transformation in primary education requires learning tools that can support competency-based assessment and respond to students’ diverse learning needs. However, conventional evaluation practices often provide limited diagnostic information for teachers when planning differentiated instruction. This study aimed to develop and conduct an initial classroom trial of EduSmart, an AI-supported adaptive learning application designed to classify students’ competency levels and provide learning recommendations based on quiz performance. The study employed a Research and Development (R&D) approach using the ADDIE model. Participants consisted of 66 fifth-grade students from three public elementary schools in Bekasi Regency. Data were collected through observations, interviews, expert validation, user response questionnaires, and learning outcome tests using a one-group pretest–posttest design. Quantitative data were analyzed using descriptive statistics, paired-sample t-test, and N-Gain analysis, while qualitative data were analyzed thematically with NVivo support. The results showed that EduSmart was rated very feasible by experts, with an average score of 4.5 out of 5. The paired-sample t-test indicated a significant difference between pretest and posttest scores (p < .001), with a mean improvement of 15.68 points. The average N-Gain score was 0.42, indicating moderate improvement. Qualitative findings showed that students responded positively to EduSmart, particularly in terms of ease of use, learning relevance, and motivation. Since the study did not involve a control group, these findings should be interpreted as preliminary evidence of EduSmart’s potential to support adaptive and data-informed learning in primary education. Abstrak Transformasi digital dalam pendidikan dasar membutuhkan perangkat pembelajaran yang mampu mendukung asesmen berbasis kompetensi dan merespons kebutuhan belajar siswa yang beragam. Namun, praktik evaluasi konvensional sering kali belum memberikan informasi diagnostik yang cukup bagi guru dalam merancang pembelajaran terdiferensiasi. Penelitian ini bertujuan mengembangkan dan melakukan uji coba awal aplikasi EduSmart, yaitu aplikasi pembelajaran adaptif berbasis AI yang dirancang untuk mengklasifikasikan tingkat kompetensi siswa dan memberikan rekomendasi belajar berdasarkan hasil kuis. Penelitian ini menggunakan pendekatan Research and Development (R&D) dengan model ADDIE. Partisipan penelitian terdiri atas 66 siswa kelas V dari tiga sekolah dasar negeri di Kabupaten Bekasi. Data dikumpulkan melalui observasi, wawancara, validasi ahli, angket respons pengguna, serta tes hasil belajar dengan desain one-group pretest–posttest. Data kuantitatif dianalisis menggunakan statistik deskriptif, paired-sample t-test, dan N-Gain, sedangkan data kualitatif dianalisis secara tematik dengan bantuan NVivo. Hasil penelitian menunjukkan bahwa EduSmart dinilai sangat layak oleh ahli dengan skor rata-rata 4,5 dari 5. Hasil paired-sample t-test menunjukkan perbedaan yang signifikan antara skor pretest dan posttest (p < .001), dengan peningkatan rata-rata sebesar 15,68 poin. Nilai rata-rata N-Gain sebesar 0,42 menunjukkan peningkatan pada kategori sedang. Temuan kualitatif menunjukkan bahwa siswa merespons EduSmart secara positif, terutama pada aspek kemudahan penggunaan, relevansi pembelajaran, dan motivasi belajar. Karena penelitian ini tidak menggunakan kelompok kontrol, temuan tersebut perlu dipahami sebagai bukti awal mengenai potensi EduSmart dalam mendukung pembelajaran adaptif dan berbasis data di pendidikan dasar.