Combining Generative AI and Scheduling Algorithms for Personalized Learning Powered by TELISIK - Artamananda Artamananda; Eogenie Lakilaki; Syakillah Nachwa; Muhammad Gilang Ramadhan; Amaliah Sobli; Annisa Fatihah Salsabila; Bagus Ramadhan (Journal of Information System Research (JOSH), 2026)
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.