Abdelalim Sadiq

The rapid digital transformation of education has accelerated the adoption of artificial intelligence (AI), cloud computing, and smart learning platforms, enabling personalized and adaptive learning experiences. However, this evolution has also increased exposure to cybersecurity threats, making educational institutions prime targets due to the sensitivity and volume of data they handle. This chapter examines key cybersecurity challenges in AI-driven educational environments, including threats such as phishing, ransomware, and data breaches, as well as vulnerabilities related to human behavior and system weaknesses. It proposes a comprehensive approach combining governance, technical safeguards, and user awareness. The chapter also highlights the role of machine learning in enhancing threat detection, predictive security, and automated responses, emphasizing the need for proactive and adaptive cybersecurity strategies.

The growing reliance on digital technologies, artificial intelligence (AI), and interconnected systems has expanded the cybersecurity threat landscape. While AI enhances automation and threat detection, it also introduces new vulnerabilities requiring advanced expertise. Simultaneously, organizations face a shortage of cybersecurity professionals, widening the gap between security needs and available skills. This paper examines the link between cybersecurity skills demand and the development of AI-driven secure information systems. It identifies key technical and transversal competencies, including AI security, data protection, and automated threat detection, along with essential security practices.To address these challenges, a holistic framework is proposed, integrating human expertise, AI capabilities, governance policies, and technical safeguards. This approach aligns workforce competencies with AI-driven security mechanisms to strengthen resilience, improve risk management, and support secure digital transformation.

The effectiveness of online tutoring systems largely depends on their ability to adapt to the individual needs of learners, to personalize learning activities, and to provide immediate and effective assessment and remediation. This effectiveness can only be ensured if accurate information is available regarding learners’ progress and learning profiles. In this article, we aim to propose a learner model tailored to the specificities of our academic support system, incorporating learning functions that enable personalized tutoring based on students’ needs. For this purpose, this study began with a literature review of existing learner models. We focused on five representative samples of the most widely used learner models in current learning systems: instructional management system-learner information package (IMS-LIP), public and private information for learners (PAPI), CARCHIOLO, knowledge on demand (KOD), and learner model for personalized adaptation (LMPA). We examined their characteristics and then compared them based on the following criteria: adaptability, user preferences, personalized learning, pedagogical requirements, assessment, and remediation, to evaluate their potential for integration into our system. The study revealed that these models present several limitations, which led us to propose a new learner model based on the PAPI and IMS-LIP standards. This proposal incorporates a semantic ontological structure that categorizes learner characteristics into six domains: preferences, pedagogy, administration, identification, learning, and assessment. The proposed model represents a promising solution for adapting learning processes to individual learner profiles, thereby fostering more effective and engaging educational experiences.

Personalized learning is challenging because students have diverse backgrounds, abilities, needs, and traditional teaching often lacks timely, tailored support. This study introduces an AI tutoring system powered by ChatGPT, designed to provide adaptive, context-aware educational assistance. The system uses conversational interfaces, domain-specific prompts, and learner modeling to adjust dynamically to each student's progress and misconceptions. An experimental study with high school and early university students showed that the system significantly improved learner engagement, comprehension, and efficiency. Users appreciated the personalized explanations and adaptive responses, which boosted their motivation and deeper understanding of the material. These findings demonstrate the potential of AI-powered tutoring systems to enhance personalized education by offering scalable, effective, and engaging learning experiences suitable for diverse learners and contexts. This approach could help overcome limitations of traditional education and support individualized learning .

The rapid advancement of artificial intelligence has catalyzed the emergence of intelligent chatbots as pivotal tools in higher education. Initially deployed for administrative tasks, these conversational agents have evolved into sophisticated virtual advisors that support personalized learning by adapting to individual student needs. This chapter examines the role of chatbots as intelligent advisors, highlighting their capabilities to provide context-aware guidance, real-time feedback, and motivational support. It explores the pedagogical benefits of integrating chatbots into academic environments, including enhanced learner autonomy, inclusivity, and scalability. The discussion also addresses key challenges such as data privacy, ethical considerations, and technological limitations. Finally, the chapter outlines future directions for chatbot development, emphasizing human-AI collaboration and the importance of ethical, learner-centered design in realizing the full potential of chatbots to transform personalized learning in higher education.

In Morocco, the escalating challenges in the education sector underscore the necessity for precise predictions and informed decision-making. Effective management of the education system depends on robust statistical data, which is crucial for guiding decisions, refining policies, and improving both the quality and accessibility of education. Reliable indicators are vital for ensuring efficiency, equity, and accuracy in educational planning and decision- making. Without dependable data, implementing effective policies, addressing the needs appropriately, and achieving positive outcomes becomes difficult. This paper aims to identify the optimal machine learning model for analyzing educational indicators by comparing a range of advanced models across a comprehensive set of metrics. The objective is to determine the most effective model for profiling relevant information and addressing predictive challenges with high accuracy.

In Morocco, the dropout rate in primary and secondary education remains high due to environmental, social, familial, and educational factors. To address this issue, students rely on private tutoring or online platforms. However, socio-economic disparities make private tutoring inaccessible to many, while technical and pedagogical challenges limit the effectiveness of online platforms, deepening educational inequalities. This article proposes a nationwide participatory tutoring approach involving educational administration and teachers to ensure equitable and quality learning. We analyze existing models to identify their limitations and propose a structured tutoring system tailored to different student profiles. This system is based on a specific algorithm that defines skill assessment, remediation, and progress tracking. Unified modeling language UML is used to structure and present our approach in detail. Then, we compare current Moroccan platforms, particularly Massar, with our system, evaluating student engagement, pedagogical monitoring, curriculum alignment, and remediation effectiveness. Finally, we discuss our results, highlighting our system’s potential to reduce learning gaps, improve education, and significantly decrease the dropout rate in Morocco.

Data governance is crucial for enhancing the effectiveness of performance monitoring and evaluation (M&E) in education. This article emphasizes the role of robust data governance frameworks in ensuring data quality, consistency, and accessibility, which are vital for informed decision-making. Well-governed data enables improved predictions, real-time assessments, and targeted interventions that boost student outcomes and institutional performance. By setting clear policies for data collection, management, and analysis, data governance supports accurate tracking of educational progress, identifies areas for improvement, and optimizes resource allocation. It also ensures accountability, transparency, and compliance with privacy regulations, fostering trust among stakeholders. The article highlights challenges faced by educational institutions, such as limited resources, data fragmentation, and resistance to change. Ultimately, effective data governance is essential for improving predictions, decision-making, and continuous improvement in educational outcomes.

This article discusses the importance of data governance in managing data availability, integrity, usability, and security within organizations. Without a governance framework, businesses risk fragmented and vulnerable data systems. Effective data governance improves data quality, security, regulatory compliance (e.g., GDPR, HIPAA, CCPA), decision-making, and operational efficiency. Key principles include data ownership, stewardship, quality maintenance, security, privacy safeguards, compliance, and lifecycle management. It also highlights challenges such as cultural resistance, data silos, platform complexity, and technology barriers. The article suggests best practices for implementation, such as clear role definitions, leveraging technology, starting pilot projects, and ongoing monitoring. Prioritizing data governance allows organizations to transform data into a strategic asset.

Writer identification is a critical task in the realm of pattern classification, aimed at determining the authorship of a manuscript based on labeled handwriting sam- ples. This area has garnered considerable attention from researchers and has seen significant advancements in the last two decades, propelled by the inte- gration of novel computer vision and machine learning algorithms. Commonly, approaches within this field rely on calculating local texture descriptors of im- ages. In this work, we propose a novel local texture descriptor method, termed multi-points local binary patterns (MP-LBP), which is an enhancement of the traditional local binary patterns (LBP) descriptor. Our approach involves apply- ing the MP-LBP descriptor to patches surrounding Harris key points and aggre- gating the image descriptors into encoded vectors using the vector of locally ag- gregated descriptors (VLAD) encoding method. These vectors are subsequently classified by a ball tree classifier to associate the document with the most plau- sible writer. To assess the efficacy of our descriptor, we conducted evaluations on five publicly accessible handwritten databases: CVL, CERUG-EN, CERUG- CH, BFL, and IAM. The results of these tests provide insights into the perfor- mance of the MP-LBP descriptor in the context of writer identification.