The rise of conversational agents (CAs) like chatbots in education has increased the demand for advisory services. However, student–college admission interactions remain manual and burdensome for staff. Leveraging CAs could streamline the admission process, providing efficient advisory support. Moreover, limited research has explored the role of Arabic chatbots in education. This study introduces Tayseer, an Arabic AI-powered web chatbot that enables instant access to college information and communication between students and colleges. This study aims to improve the abilities of chatbots by integrating features into one model, including responding with audiovisuals, various interaction modes (menu, text, or both), and collecting survey responses. Tayseer uses deep learning models within the RASA framework, incorporating a customized Arabic natural language processing pipeline for intent classification, entity extraction, and response retrieval. Tayseer was deployed at the Technical College for Girls in Najran (TCGN). Over 200 students used Tayseer during the first semester, demonstrating its efficiency in streamlining the advisory process. It identified over 50 question types from inputs with a 90% precision in intent and entity predictions. A comprehensive evaluation illuminated Tayseer’s proficiency as well as areas requiring improvement. This study developed an advanced CA to enhance student experiences and satisfaction while establishing best practices for education chatbot interfaces by outlining steps to build an AI-powered chatbot from scratch using techniques adaptable to any language.

Evaluating user experience (UX) is essential for optimizing educational chatbots to enhance learning outcomes and student productivity. This study introduces a novel weighted composite metric integrating interface usability assessment (via the Chatbot Usability Questionnaire, CUQ), engagement measurements (via the User Engagement Scale—Short Form, UES-SF), and objective performance indicators (through error rates and response times), addressing gaps in existing evaluation methods across interaction modes (text-based, menu-based, and hybrid) and question complexities. A 3 × 3 within-subject experimental design (n = 30) was conducted, measuring these distinct UX dimensions through standardized instruments and performance metrics, supplemented by qualitative feedback. Principal Component Analysis (PCA) was used to derive weights for the composite UX metric based on empirical patterns in user interactions. Repeated-measures ANOVA revealed that the hybrid interaction mode outperformed the others, achieving significantly higher usability (F(2,58) = 89.32, p < 0.001) and engagement (F(2,58) = 8.67, p < 0.001), with fewer errors and faster response times under complex query conditions. These findings demonstrate the hybrid mode’s adaptability across question complexities. The proposed framework establishes a standardized method for evaluating educational chatbots, providing actionable insights for interface optimization and sustainable learning tools.

Cancer research has increasingly utilized multi-omics analysis in recent decades to obtain biomolecular information from multiple layers, thereby gaining a better understanding of complex biological systems. However, the curse of dimensionality is one of the most significant challenges when handling omics or biological data. Additionally, integrating multi-omics by transforming different omics types into a new representation can reduce a model’s interpretability, as the extracted features may lose the biological context. This paper proposes Iterative Similarity Bagging (ISB), assisted by Bayesian Ridge Regression (BRR). BRR serves as a domain-oriented supervised feature selection method, choosing essential features by calculating the coefficients for each feature. Despite this, the BRR output datasets contain many features, leading to complexity and high dimensionality. To address this, ISB was introduced to dynamically reduce dimensionality and complexity without losing the biological integrity of the omics data, which often occurs with transformation-based integration approaches. The evaluation measures employed were Root Mean Square Error (RMSE), the Pearson Correlation Coefficient (PCC), and the coefficient of determination (R²). The results demonstrate that the proposed method outperforms some current models in terms of regression performance, achieving an RMSE of 0.12, a PCC of 0.879, and an R² of 0.77 for the CCLE. For the GDSC, it achieved an RMSE of 0.029, a PCC of 0.90, and an R² of 0.80.

A rule-based chatbot is a type of chatbot that responds by matching users’ queries with pre-defined rules. In e-learning, chatbots can enhance the learning experience by assisting teachers in delivering learning materials pleasantly. This research introduces Moalemy, an Arabic rule-based chatbot designed to provide a personalized learning experience by tailoring educational content to each learner’s prior knowledge. This empirical study evaluates learning outcomes, user engagement, and system usability using both subjective and objective metrics. It compares the effectiveness of a proposed Arabic rule-based chatbot with adaptive personalization to that of a static, non-personalized chatbot. The comparison was conducted across three levels of task difficulty (easy, medium, and hard) using a 2 × 3 within-subject experimental design with 34 participants. Descriptive statistics revealed higher mean values of usability and engagement in the adaptive method. Although the analysis revealed no significant variations in learning outcomes and SUS scores, it showed statistically significant differences in user satisfaction in favor of the adaptive method, p = 0.003. Analyses showed no significant differences between the two learning methods in terms of effectiveness, efficiency, and engagement. Across difficulty levels, the adaptive method outperforms the static method in terms of efficiency and effectiveness at the medium level, and in engagement at the easy level.

Personalized e-learning emphasizes a learner-centered instruction approach by adapting educational content to individual needs. This study empirically evaluates three personalization methods (adaptive, adaptable, and mixed) alongside a non-personalized static method, implemented within Moalemy, an Arabic rule-based educational chatbot. A 4 × 3 within-subjects experiment involving 52 students across three levels of task difficulty (easy, medium, and hard) was performed to examine usability, learner engagement, and learning outcomes as key indicators of user experience in educational chatbots. Results showed no statistically significant differences in learning gain or relative learning gain across the four methods, indicating comparable effectiveness in supporting learning success. Engagement differed significantly by method, with the adaptable approach obtaining the highest scores. Descriptive usability results further suggested that the adaptable method achieved numerically higher scores in SUS, efficiency, and effectiveness under lower task difficulty, while static and adaptive showed comparatively stronger tendencies at higher levels. However, these patterns represent exploratory trends rather than statistically confirmed performance advantages. This study provides a controlled comparison of system-driven, learner-driven, shared-control, and non-personalization strategies in an Arabic e-learning context, offering empirical insights into how different learning methods influence usability, learning success, and engagement within Arabic educational chatbots.