Combining Generative AI and Scheduling Algorithms for Personalized Learning Powered by TELISIK
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Artamananda Artamananda
*
PT Vanz Inovatif Teknologi, Jakarta,
Indonesia
- Eogenie Lakilaki National Library of The Republic of Indonesia, Jakarta, Indonesia
- Syakillah Nachwa Universitas Sriwijaya, Ogan Ilir, Indonesia
- Muhammad Gilang Ramadhan 4PT Bukalapak.com Tbk, Jakarta, Indonesia
- Amaliah Sobli 5Regional Representative Council of the Republic of Indonesia, Palembang, Indonesia
- Annisa Fatihah Salsabila SIT Robbani, Ogan Ilir, Indonesia
- Bagus Ramadhan National Library of The Republic of Indonesia, Jakarta, Indonesia (*) Corresponding Author
Abstract
The increasing competitiveness of the Computer Based Written Examination for National Selection Based on Test (UTBK-SNBT) necessitates adaptive and sustainable learning support. This study proposes a web based intelligent learning platform that integrates Artificial Intelligence (AI) to facilitate examination preparation through Automatic Question Generation (AQG), an AI Tutor, automated solution generation, and a scheduler driven question generation mechanism. The platform adopts a client server architecture and employs a Large Language Model (LLM) to generate examination questions tailored to the characteristics of each UTBK-SNBT subtest. The system was evaluated from two perspectives: the learning performance of 30 students across seven UTBK-SNBT subtests and the quality of 1.663 AI generated questions using a Jaccard Similarity based deduplication approach. The results demonstrate that the proposed platform provides continuous and personalised practice beyond conventional static question banks. Acceptable question generation rates reached 96.3% for Quantitative Knowledge, 92.9% for Mathematical Reasoning, and 91.4% for General Knowledge and Comprehension, whereas reading-intensive subtests exhibited higher duplication rates due to the limitations of lexical similarity measurement. Overall, the findings confirm the feasibility of the proposed platform as an intelligent and scalable solution for UTBK-SNBT preparation, while highlighting semantic similarity techniques as a promising direction for improving the quality of text-based question generation.
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References
Muhammad Syauqi Firdaus, Wahyu Kholis Prihantoro, Latifaturrohmah Latifaturrohmah, Husna Rifaatul Mahmudah, and Afrida Aunil Illah, “Efektivitas Instrumen Tes Uraian Dibandingkan dengan Tes Pilihan Ganda dalam Mengukur Hasil Belajar Siswa di MAN 2 Bantul,” Jurnal Kajian Ilmu Pendidikan, Bahasa dan Komunikasi, vol. 1, no. 4, pp. 87–101, Nov. 2025, doi: 10.61132/jkaipbaku.v1i4.175.
S. Zhang, Z. Wang, J. Qi, J. Liu, and Z. Ying, “Accurate Assessment via Process Data,” Psychometrika, vol. 88, no. 1, pp. 76–97, Mar. 2023, doi: 10.1007/s11336-022-09880-8.
M. A. N. Awal, Hasniati, Husni Agriani, and Izmi Alwiah M, “Pelatihan Mengerjakan Tes Potensi Skolastik Bagi Siswa Kelas XII SMA Untuk Menghadapi Seleksi Nasional Berdasarkan Tes (SNBT),” Journal Inovasi Pengabdian Masyarakat, vol. 1, no. 3, pp. 126–132, Oct. 2024, doi: 10.65255/jipmas.v1i3.102.
Seleksi Nasional Penerimaan Mahasiswa Baru, “Informasi Umum | Seleksi Nasional Penerimaan Mahasiswa Baru.” Accessed: May 15, 2026. [Online]. Available: https://snpmb.id/utbk-snbt/informasi-umum
Seleksi Nasional Penerimaan Mahasiswa Baru, “Statistik Nilai Peserta UTBK 2024,” Jun. 2024. Accessed: Jun. 15, 2026. [Online]. Available: https://snpmb.id/blog/statistik-nilai-peserta-utbk-2024
Seleksi Nasional Penerimaan Mahasiswa Baru (SNPMB), “SIARAN PERS Nomor:07/sipers/snpmb/V/2025 TENTANG HASIL SNBT 2025 RESMI DIUMUMKAN: 29,43% PESERTA DINYATAKAN LULUS ,” May 2025. [Online]. Available: https://files.snpmb.id/web2025/07_Sipers%20Pengumuman%20SNBT%202025.pdf
J. Xu et al., “Active recall strategies associated with academic achievement in young adults: A systematic review,” J. Affect. Disord., vol. 354, pp. 191–198, Jun. 2024, doi: 10.1016/j.jad.2024.03.010.
J. Schwerter, F. Lauermann, T. Brahm, and K. Murayama, “Differential use and effectiveness of practice testing: Who benefits and who engages?,” Learn. Individ. Differ., vol. 123, p. 102761, Oct. 2025, doi: 10.1016/j.lindif.2025.102761.
J. Burstein, R. Cardwell, P.-L. Chuang, A. Michalowski, and S. Nydick, “Exploring AI-Enabled Test Practice, Affect, and Test Outcomes in Language Assessment,” ArXiv, Aug. 2025.
S. Y. Widjaja and A. Yohannis, “AI-Powered Automatic Question Generation for Teachers,” in 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML), IEEE, Jun. 2025, pp. 1–6. doi: 10.1109/SIML65326.2025.11081160.
B. Das, M. Majumder, S. Phadikar, and A. A. Sekh, “Automatic question generation and answer assessment: a survey,” Res. Pract. Technol. Enhanc. Learn., vol. 16, no. 1, p. 5, Dec. 2021, doi: 10.1186/s41039-021-00151-1.
P. Panchal, J. Thakkar, V. Pillai, and S. Patil, “Automatic Question Generation and Evaluation,” Journal of University of Shanghai for Science and Technology, vol. 23, no. 05, pp. 751–761, May 2021, doi: 10.51201/JUSST/21/05203.
M. M. Henry, G. N. Elwirehardja, and B. Pardamean, “Automatic question generation for bahasa indonesia examination using copynet,” Procedia Comput. Sci., vol. 245, pp. 953–962, 2024, doi: 10.1016/j.procs.2024.10.323.
G. Kurdi, J. Leo, B. Parsia, U. Sattler, and S. Al-Emari, “A Systematic Review of Automatic Question Generation for Educational Purposes,” Int. J. Artif. Intell. Educ., vol. 30, no. 1, pp. 121–204, Mar. 2020, doi: 10.1007/s40593-019-00186-y.
S. Al Faraby, A. Romadhony, and Adiwijaya, “Analysis of LLMs for educational question classification and generation,” Computers and Education: Artificial Intelligence, vol. 7, p. 100298, Dec. 2024, doi: 10.1016/j.caeai.2024.100298.
Seleksi Nasional Penerimaan Mahasiswa Baru (SNPMB), “Ujian Tertulis Berbasis Komputer (UTBK) dalam Seleksi Nasional Berdasarkan Tes (SNBT) Tahun 2026.”
L. Reynolds and K. McDonell, “Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm,” ArXiv, 2021.
PISA 2022 Assessment and Analytical Framework. OECD Publishing, 2023. doi: 10.1787/dfe0bf9c-en.
B. S. Bloom, Taxonomy of educational objectives. 2023.
W. M. Lim, A. Gunasekara, J. L. Pallant, J. I. Pallant, and E. Pechenkina, “Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators,” The International Journal of Management Education, vol. 21, no. 2, p. 100790, Jul. 2023, doi: 10.1016/j.ijme.2023.100790.
M. N. Salem and M. F. Abdullah, “Evaluating String-Based Similarity Algorithms for Duplicate Record Identification in Databases,” Journal of Science and Technology, vol. 31, no. 2, Feb. 2026, doi: 10.20428/jst.v31i2.3566.
W. M. Ahmed, A. A. Azhari, A. Alfaraj, A. Alhamadani, M. Zhang, and C.-T. Lu, “The Quality of AI-Generated Dental Caries Multiple Choice Questions: A Comparative Analysis of ChatGPT and Google Bard Language Models,” Heliyon, vol. 10, no. 7, p. e28198, Apr. 2024, doi: 10.1016/j.heliyon.2024.e28198.
S. Wang, F. Wang, Z. Zhu, J. Wang, T. Tran, and Z. Du, “Artificial intelligence in education: A systematic literature review,” Expert Syst. Appl., vol. 252, p. 124167, Oct. 2024, doi: 10.1016/j.eswa.2024.124167.
J. Kim and Y. Park, “A Survey of Text Deduplication: From Syntactic Matching to Semantic Understanding,” IEEE Access, vol. 14, pp. 16731–16750, 2026, doi: 10.1109/ACCESS.2026.3658439.
A. Huda, F. Firdaus, D. Irfan, Y. Hendriyani, A. Almasri, and M. Sukmawati, “Optimizing Educational Assessment: The Practicality of Computer Adaptive Testing (CAT) with an Item Response Theory (IRT) Approach,” JOIV : International Journal on Informatics Visualization, vol. 8, no. 1, p. 473, Mar. 2024, doi: 10.62527/joiv.8.1.2217.
J. Risch, T. Möller, J. Gutsch, and M. Pietsch, “Semantic Answer Similarity for Evaluating Question Answering Models,” Oct. 2021.
I. Crk and E. Gultepe, “Evaluating the instrumental quality of LLM-generated assessment items,” Front. Educ. (Lausanne)., vol. 11, Jun. 2026, doi: 10.3389/feduc.2026.1837523.
X. Wang et al., “Evaluating General-Purpose AI with Psychometrics,” Dec. 2023.
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Copyright (c) 2026 Artamananda Artamananda, Eogenie Lakilaki, Syakillah Nachwa, Muhammad Gilang Ramadhan, Amaliah Sobli, Annisa Fatihah Salsabila; Bagus Ramadhan
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