Optimizing Arabic Language Learning for Students with Disabilities through Artificial Intelligence in Distance Education

Education in the 21st century is undergoing a paradigmatic transformation driven by the integration of digital technologies and artificial intelligence, especially in distance education. In this context, the present research proposal had the general objective of reflecting on how these innovations transform pedagogical relationships, educational processes and distance learning courses. And, specifically: - to analyze how the student assumes a more autonomous and protagonist posture in the interaction with technological tools; and - to discuss how the role of the teacher/tutor and course planning should be adapted to enhance the use of these technologies. In carrying out this study, bibliographic research was used as a procedure to build a theoretical understanding of the topic addressed. It was concluded that the integration of Artificial Intelligence and emerging technologies in Distance Education not only transforms the roles of students and educators, but also redefines the very essence of teaching. By strengthening student autonomy and demanding new skills from teachers, distance learning becomes a dynamic platform that combines personalization and scalability with the essentiality of human interaction. Thus, at the end of this study, we hope to contribute to a deeper understanding of how emerging technologies, especially AI, are revolutionizing the distance education scenario.

The integration of Artificial Intelligence (AI) and Big Data has fundamentally transformed distance learning into a highly personalized and data-driven experience. However, this transition poses major obstacles to maintaining academic integrity and rigorous standards in Open and Distance Learning (ODL) environments. The objective of this study is to formulate a strategic roadmap that aligns AI-driven learning optimization with academic rigor. This research employs a qualitative descriptive method through a systematic literature review (SLR) and thematic analysis. Data were synthesized from high-impact academic publications and case studies published between 2023 and 2025, concentrating on strategic implementations in global ODL institutions. The findings identify four critical thematic pillars: faculty training, ethical governance, individualized learning, and assessment redesign. The study reveals that a "human-centric AI" model is vital, where AI serves as an augmentative tool rather than a replacement for human judgment. Institutions must transition toward authentic, process-oriented assessments and robust ethical frameworks to ensure that technological efficiency does not compromise higher-order thinking skills. In practice, this research provides policymakers with a blueprint for creating inclusive and transparent educational ecosystems.

The integration of Artificial Intelligence (AI) and Big Data has fundamentally transformed distance learning into a highly personalized and data-driven experience. However, this transition poses major obstacles to maintaining academic integrity and rigorous standards in Open and Distance Learning (ODL) environments. The objective of this study is to formulate a strategic roadmap that aligns AI-driven learning optimization with academic rigor. This research employs a qualitative descriptive method through a systematic literature review (SLR) and thematic analysis. Data were synthesized from high-impact academic publications and case studies published between 2023 and 2025, concentrating on strategic implementations in global ODL institutions. The findings identify four critical thematic pillars: faculty training, ethical governance, individualized learning, and assessment redesign. The study reveals that a "human-centric AI" model is vital, where AI serves as an augmentative tool rather than a replacement for human judgment. Institutions must transition toward authentic, process-oriented assessments and robust ethical frameworks to ensure that technological efficiency does not compromise higher-order thinking skills. In practice, this research provides policymakers with a blueprint for creating inclusive and transparent educational ecosystems.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

The integration of artificial intelligence into the logistics industry is a rapidly evolving field with the potential to revolutionize the way goods are transported and managed. Artificial intelligence can be used to optimize a wide range of logistics processes, from demand forecasting and route planning to warehouse management and customer service. However, the integration of artificial intelligence also raises a number of technical and ethical issues that need to be addressed to ensure its successful implementation. Choosing the right artificial intelligence algorithms for specific logistics tasks is crucial to ensure their efficiency and accuracy. This requires careful consideration of factors such as data type, task complexity, and desired performance metrics. The growing amount of data collected and processed by artificial intelligence systems raises concerns about data security and privacy. Companies need to implement robust security measures to protect sensitive data from unauthorized access, breaches, and misuse. The use of artificial intelligence in logistics raises ethical issues related to bias, transparency, and accountability. Artificial intelligence algorithms should be developed and used fairly, transparently, and with respect for the right to privacy and in compliance with all relevant laws and regulations. In order to eliminate or prevent these problems, recommendations for the effective implementation of artificial intelligence in the logistics sector have been developed and formulated. They include aspects that need to be addressed in the first place when developing mechanisms for automating logistics processes. The integration of artificial intelligence into logistics offers significant opportunities to increase efficiency, reduce costs and improve customer service. However, it is crucial to address the technical and ethical challenges associated with artificial intelligence integration to ensure that it is used responsibly and beneficially. By following the recommendations, logistics companies can successfully use artificial intelligence to transform their operations and achieve their strategic goals.

This study addresses a critical gap in educational technology research by simultaneously examining the internal and external determinants of Artificial Intelligence (AI) integration in primary mathematics instruction. Using a second-order Structural Equation Modeling (SEM) framework, the study investigates how teachers’ attitudes and TPACK competencies (internal factors), alongside policy support, infrastructure, and community engagement (external factors), influence AI utilization among 516 primary school mathematics teachers in Jakarta, Indonesia. The results reveal that internal factors have a strong direct effect on AI utilization (β = 0.791; p < 0.001), while external factors exert a significant indirect influence via internal mediators (β = 0.217; p < 0.001), despite an insignificant direct effect (β = 0.008; p = 0.908). The model explains 78.1% of the variance in AI utilization (R² = 0.781) and shows high predictive relevance (Q² > 0.70). These findings underscore the pivotal role of teacher readiness in AI integration, with systemic support enhancing its effectiveness through internal capacity-building. The study contributes an empirically validated instrument and a comprehensive ecological model, offering actionable insights for policymakers and educators in developing nations pursuing ethical, equitable, and sustainable AI integration in primary education.

In the realm of Artificial Intelligence (AI) integration and project management efficiency (PME), a comprehensive research study has been conducted, primarily focusing on various industries in Pakistan. The intricate interplay between AI integration, team proficiency in AI, organizational support for AI technologies, and PME forms the crux of this investigation. The theoretical underpinning of this research has been rooted in the Resource-Based View (RBV) theory. Data for this study have been collected through a structured questionnaire survey, targeting a diverse group comprising project managers, IT managers, senior executives, and other key personnel engaged in AI-driven decision support systems. The research has revealed significant positive correlations between the integration of AI, team proficiency in AI, organizational support for these technologies, and PME. These findings highlight the crucial role these elements play in enhancing project outcomes. This study, by uncovering these relationships, offers valuable insights for organizations aiming to optimize their project management practices, especially in emerging economies like Pakistan. It contributes to the existing body of knowledge by providing a nuanced understanding of how AI integration can be leveraged to enhance project management efficiency. Furthermore, the study discusses broader implications for policy and suggests directions for future research, emphasizing the strategic importance of nurturing AI competencies and fostering organizational support for AI technologies to realize enhanced project management outcomes.

Purpose This study aims to explore the interconnectedness between artificial intelligence (AI) integration, customer satisfaction, process task efficiency and organizational readiness within the hospitality and tourism sector, elucidating their combined influence on firm performance. Design/methodology/approach The research sample comprises 790 owners, supervisors, managers, customers and employees from 158 firms from hospitality and tourism firms in Guangzhou. This study uses a multimodel approach to analyze the relationships between AI integration, customer satisfaction, process task efficiency, organizational readiness and firm performance. Findings Model 1 indicates a positive correlation between AI integration and firm performance. Model 2 introduces customer satisfaction as a mediator, revealing its partial mediation effect on the relationship between AI integration and firm performance. Model 3 expands to demonstrate the moderating effect of process task efficiency on the AI integration–firm performance relationship. Finally, Model 4 incorporates organizational readiness as a predictor, enhancing the model’s fit and emphasizing its significance in driving firm performance alongside other factors. Research limitations/implications This study’s scope is limited to the hospitality and tourism sector in Guangzhou, potentially restricting the generalizability of findings to other industries or regions. Future research could explore diverse contexts to ascertain broader implications. Practical implications The findings underscore the multifaceted impact of AI integration on organizational outcomes, highlighting strategic opportunities for firms to enhance performance through investments in AI integration and organizational preparedness. Originality/value This study contributes to the understanding of how AI integration, along with factors like customer satisfaction, process task efficiency and organizational readiness, collectively shape firm performance within the hospitality and tourism sector, offering valuable insights for strategic decision-making and resource allocation.

This article examines the theoretical and practical implications of artificial intelligence (AI) integration in supply chain management (SCM). AI has developed dramatically in recent years, embodied by the newest generation of large language models (LLMs) that exhibit human‐like capabilities in various domains. However, SCM as a discipline seems unprepared for this potential revolution, as existing perspectives do not capture the potential for disruption offered by AI tools. Moreover, AI integration in SCM is not only a technical but also a social process, influenced by human sensemaking and interpretation of AI systems. This article offers a novel theoretical lens called the AI Integration (AII) framework, which considers two key dimensions: the level of AI integration across the supply chain and the role of AI in decision‐making. It also incorporates human meaning‐making as an overlaying factor that shapes AI integration and disruption dynamics. The article demonstrates that different ways of integrating AI will lead to different kinds of disruptions, both in theory and in practice. It also discusses the implications of AI integration for SCM theorizing and practice, highlighting the need for cross‐disciplinary collaboration and sociotechnical perspectives.

This article investigated the integration of Artificial Intelligence (AI) in the context of Distance Education (DE), with the aim of exploring its advantages, disadvantages, and the challenges faced by teachers and students. The research focused on how AI can be employed to promote meaningful learning, using a bibliographic research methodology, as proposed by Severino (2007). This approach involved the critical analysis of existing literature, including relevant case studies and theories pertinent to the use of AI in education. The main authors cited were Castro (2002) and Tavares, Meira, and Amaral (2020), whose works provided insights on the application of Intelligent Tutoring Systems (ITS) and other AI technologies in education. The literature review highlighted the potential of AI to personalize the learning experience and the associated challenges, such as the need for adequate infrastructure, digital skills, and ethical considerations. A case study from Georgia State University illustrated a successful practical application of AI to prevent student dropout, offering a model for future implementations in DE. The analysis showed that, despite obstacles, the integration of AI in DE has the potential to positively transform education, offering opportunities for more adaptive and personalized learning. In conclusion, the article emphasized the importance of addressing technical, ethical, and pedagogical challenges in adopting AI in education, highlighting the need for careful strategies that ensure the effectiveness and inclusivity of technological interventions in DE.

In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

The study is devoted to the issue of digital transformation of business structures and focuses on examining the conceptual foundations of the digital transformation of these entities. Digital transformation is one of the key trends in modern business. A vision of a paradigm shift in development regulation, based on the consideration of digital methods of delivering value to the market has been outlined in the research. The essence of the digital transformation process lies in the creation of a new mode of production, characterized by the integration of human and artificial intelligence. It is substantiated that digital transformation is a strategic goal of the company, which requires a holistic approach and leads to a change in the very economic paradigm. It is noted that digital transformation is accompanied by the application of advanced technologies that serve as its foundation, and the study identifies a list of such technologies. It is revealed that technologies do not operate in isolation but create a powerful synergistic effect. Digital transformation fundamentally alters the way companies create value and generate profit. Instead of traditional models, digital technologies enable the development of new, innovative approaches that redefine market entry strategies. It is identified the key components of corporate digital transformation in the study, namely: the human factor, technological foundations, organizational change, and strategic development. The need for digital transformation is shaped by both external and internal factors that compel organizations to reconsider their strategy and operations. It is emphasized that the most challenging aspect of transformation is not technological but human and organizational. Cultural adaptation, employee engagement, and the enhancement of digital skills are critically important for overcoming resistance and minimizing risks. Transformational changes in the digital economy make businesses more flexible, innovative, and data-driven, yet they require a balance between technologies and the human factor. It is further noted that the next stages of digital transformation – including hyper-automation, cognitive robotics, and further integration of artificial intelligence – will demand continuous adaptation and innovative thinking from organizations to remain leaders in the era of the digital economy.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.