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All Journal SAGA: Journal of Technology and Information Systems Mandailing Journal of Education and Sciences
Dwitia Pratiwi
RSAU Esnawan Antariksa Jakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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AI-DRIVEN VOCABULARY PERSONALIZATION: FILLING THE GAP IN TRANSPARENCY AND LEARNER TRUST IN ADAPTIVE RECOMMENDER SYSTEMS FOR LANGUAGE LEARNING Muhammad Sobri Maulana; Arditya Prayogi; Dwitia Pratiwi
Mandailing Journal of Education and Sciences Vol. 1 No. 2 (2026): Vol. 1 No. 2, Juli 2026
Publisher : PT Mandailing Publisher Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66698/mjoes.v1i2.9

Abstract

AI-driven vocabulary recommender systems can personalize vocabulary practice by using learner data such as prior performance, error patterns, goals, and review schedules. However, many systems remain opaque because they recommend words without explaining why the items are selected or how learners can adjust the recommendation process. This conceptual article examines transparency as a key design issue in technology-enhanced language learning and synthesizes literature on explainable AI, trust in automation, technology acceptance, and self-regulated learning. The results of the synthesis show that transparent vocabulary personalization is most likely to support learning when it strengthens three mechanisms: perceived control, calibrated trust, and sustained adoption. The analysis also indicates that the most appropriate design is not excessive technical disclosure, but concise explanations, on-demand details, and learner controls that allow users to correct or adjust recommendations. These findings suggest that trustworthy vocabulary recommenders should combine pedagogically meaningful explanations with learner agency so that adaptive systems support, rather than replace, self-regulated learning.
RadOnco-Priority: Machine Learning Decision Support for Radiotherapy Queue Prioritization Using Real-World Retrospective Radiotherapy Referral Data Muhammad Sobri Maulana; Dwitia Pratiwi; Arditya Prayogi
SAGA: Journal of Technology and Information System Vol. 4 No. 2 (2026): May 2026
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/saga.v4i2.685

Abstract

Radiotherapy queues are often managed by referral date and manual clinician judgment, although limited linear accelerator capacity requires prioritization that is clinically transparent, operationally auditable, and fair. This study evaluates RadOnco-Priority, a machine learning-enabled decision support framework for radiotherapy queue prioritization, using a de-identified real-world retrospective dataset of 240 radiotherapy referral records rather than simulated or synthetic patient records. The system combines a literature-informed rule-based urgency score with supervised machine learning models to identify patients requiring accelerated booking. Accelerated booking need was defined a priori as an operational triage label reflecting clinician-documented priority, urgent symptoms, time-sensitive tumor-site and treatment-intent combinations, accumulated referral delay, and planning complexity. Logistic regression, random forest, and gradient boosting were trained to predict accelerated booking need, while a capacity-aware scheduling simulation evaluated waiting-time redistribution. To address potential circularity, additional ablation analyses were performed with the aggregate urgency score removed from the predictors. In the held-out test set, logistic regression achieved the highest discrimination in the full-feature model (AUC 0.91), with sensitivity-oriented classification favoring reduced false negatives. Performance remained acceptable after removing the aggregate urgency score, indicating that the model did not rely solely on the pre-specified scoring logic. The scheduling simulation reduced median waiting time in the high-priority group and decreased the proportion of high-priority patients waiting more than 28 days. These findings support RadOnco-Priority as an interpretable, human-governed information-system framework for radiotherapy queue management. Prospective multicenter validation, fairness monitoring, and local ethics approval remain required before routine implementation.
Co-Authors Arditya Prayogi Muhammad Sobri Maulana