The rapid expansion of online learning platforms has necessitated advanced systems to address scalability, personalization, and assessment challenges. This paper presents a comprehensive review of artificial intelligence (AI)-based decision support systems (DSSs) designed for online learning and assessment, synthesizing advancements from 2020 to 2025. By integrating machine learning, natural language processing, knowledge-based systems, and deep learning, AI-DSSs enhance educational outcomes through predictive analytics, automated grading, and personalized learning paths. This study examines system architecture, data requirements, model selection, and user-centric design, emphasizing their roles in achieving scalability and inclusivity. Through case studies of a MOOC platform using NLP and an adaptive learning system employing reinforcement learning, this paper highlights significant improvements in grading efficiency (up to 70%) and student performance (12–20% grade increases). Performance metrics, including accuracy, response time, and user satisfaction, are analyzed alongside evaluation frameworks combining quantitative and qualitative approaches. Technical challenges, such as model interpretability and bias, ethical concerns like data privacy, and implementation barriers, including cost and adoption resistance, are critically assessed, with proposed mitigation strategies. Future directions explore generative AI, multimodal integration, and cross-cultural studies to enhance global accessibility. This review offers a robust framework for researchers and practitioners, providing actionable insights for designing equitable, efficient, and scalable AI-DSSs to transform online education.

Ocular toxoplasmosis (OT) often presents a diagnostic dilemma in clinics, with retinal lesions that are not only varied in appearance but also frequently subtle and underrepresented in fundus images. Current automated segmentation tools, though promising, are often hampered by class imbalance and a lack of robust testing across real-world scenarios. To address these gaps, we developed ToxoSegFusion, a dual-backbone deep learning framework that capitalizes on the complementary strengths of DenseNet121 and ResNet101, enhanced with attention modules. Unlike typical single-backbone models, this hybrid approach was specifically tuned for the intricate challenges of OT lesion segmentation, using a combined Dice and binary cross-entropy loss to better balance rare lesion pixels. We trained and validated on 149 image-mask pairs from the OTFID-Version 3 dataset, achieving an intersection over union of 0.858 and a Dice coefficient of 0.795, both exceeding the current MobileNetV2/U-Net baseline. The model also demonstrated reliable performance on the DRIVE dataset for vessel segmentation, indicating practical flexibility. By facilitating accurate lesion localization, ToxoSegFusion enables more timely interventions in ophthalmology. Future directions include larger multi-center trials and streamlined models for routine deployment. ABSTRAK: Toksoplasmosis okular (OT) sering menimbulkan cabaran diagnostik di klinik, dengan lesi retina halus pelbagai rupa dan kurang terwakili pada imej fundus. Alat segmentasi automatik semasa, walaupun memberi harapan, sering terhad pada ketidakseimbangan kelas dan kekurangan ujian di peringkat perubatan. Bagi mengatasi kekurangan ini, kajian ini membangunkan ToxoSegFusion, sebuah rangka kerja pembelajaran mendalam berkomponen dua yang memanfaatkan kekuatan saling melengkapi DenseNet121 dan ResNet101, diperkaya dengan mekanisme perhatian. Tidak seperti model komponen tunggal biasa, pendekatan hibrid ini dirancang khusus bagi cabaran kompleks segmentasi lesi OT, menggunakan kehilangan Dice dan entropi silang binari gabungan bagi keseimbangan terbaik antara piksel lesi yang jarang. Kajian ini melatih dan mengesahkan 149 pasangan imej-topeng dari set data OTFID-Versi 3, mencapai persilangan atas kesatuan 0.858 dan pekali Dice 0.795, keduanya melebihi garis dasar MobileNetV2/U-Net semasa. Model juga menunjukkan prestasi terbaik pada DRIVE bagi segmentasi salur darah, mencadangkan fleksibiliti praktis. Melalui pengesanan lokasi lesi yang tepat, ToxoSegFusion membuka jalan bagi intervensi lebih tepat pada masa oftalmologi. Pada masa hadapan, cadangan bagi penyebaran rutin adalah melalui ujian berbilang pusat yang lebih besar dan perkemasan model.