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.

The knowledge gained from data mining is highly dependent on the experience of an expert for further analysis to increase effectiveness and wise decision-making. This mined knowledge requires actionability enhancement before it can be applied to real-world problems. The literature highlights the reasons that emerged the need to incorporate human wisdom in decision-making for complex problems. To solve this problem, a domain called ‘Wisdom Mining’ is recommended, proposing a set of algorithms parallel to the algorithms proposed by the data mining. In wisdom mining, a process to extract wisdom needs to be defined with less influence from an expert. This review proposed improvements to data mining techniques and their applications in the real world and emphasised the need to seek ways to harness wisdom from data. This study covers the diverse definitions and different perspectives of wisdom within philosophy, psychology, management and computer science. This comprehensive literature review served as a foundation for constructing a wise decision framework that aided in identifying the wisdom factors like context, utility, location and time. The inclusion of these wisdom factors in existing data mining algorithms makes the transition from data mining to wisdom mining possible. This research includes the relationship between these two mining process that facilitated further elucidation of the wisdom mining process. Potential research trends in the domain are also seen as a potential endeavour to improve the analysis and use of data.

Selection of an attribute for placement of the decision tree at an appropriate position (e.g. root of the tree) is an important decision. Many attribute selection measures such as Information Gain, Gini Index and Entropy have been developed for this purpose. The suitability of an attribute generally depends on the diversity of its values, relevance and dependency. Different attribute selection measures have different criteria for measuring the suitability of an attribute. Diversity Index is a classical statistical measure for determining the diversity of values, and according to our knowledge, it has never been used as an attribute selection method. In this article, we propose a novel attribute selection method for decision tree classification. In the proposed scheme, the average of Information Gain, Gini Index and Diversity Index are taken into account for assigning a weight to the attributes. The attribute with the highest average value is selected for the classification. We have empirically tested our proposed algorithm for classification of different data sets of scientific journals and conferences. We have developed a web-based application named JC-Rank that makes use of our proposed algorithm. We have also compared the results of our proposed technique with some existing decision tree classification algorithms.

The presence of hydrocarbons beneath earth’s surface produces some microbiological anomalies in soils and sediments. The detection of such microbial populations involves pure bio chemical processes which are specialized, expensive and time consuming. This paper proposes a new algorithm of context based association rule mining on non spatial data. The algorithm is a modified form of already developed algorithm which was for spatial database only. The algorithm is applied to mine context based association rules on microbial database to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve. The surface and soil manifestations caused by the presence of hydrocarbon oxidizing microbes are selected from existing literature and stored in a shared database. The algorithm is applied on the said database to generate direct and indirect associations among the stored microbial indicators. These associations are then correlated with the probability of hydrocarbon’s existence. The numerical evaluation shows better accuracy for non-spatial data as compared to conventional algorithms at generating reliable and robust rules.