ABSTRACTS

As technology continues to permeate and disrupt traditional work roles, it becomes increasingly important to support the development of lifelong learning skills. As educators, we must build programs that support learners as they adapt and evolve to this new norm. Constantly growing skill sets, cultivating awareness about changing contexts, maintaining flexibility, and refining project and people management skills will be key to the survival of companies and workers in the 21st Century. Recent breakthroughs in Generative AI have created opportunities to explore and develop new types of AI-driven educational technologies that have the potential to reshape the work of educators with tools to augment teaching practice and tools to support student learning. The best of these tools will enable well-trained educators to do what they do best: ensure each learner reaches their fullest potential and achieves the best possible education outcomes. Generative AI can help us achieve this goal by personalizing the learning experience to:
• Match learning activities to the student’s current learning level, aptitude, and interests;
• Support diverse learners who struggle with traditional classroom environments;
• Assist underprepared learners to get back on track academically; and
• Allow learners to progress relatively independently to meet their needs.

Personalized Learning (PL) is a distinct student-centered approach to learning that is increasingly being used to ensure that students are able to meet their goals and their potential. It has been a key element of recent and planned educational reform in the United Kingdom, United States, Australia, and New Zealand. The philosophy underpinning this approach believes in every learner’s ability to succeed, communicating this to each individual learner to unlock their potential, and providing them with resources for succeed. Support for PL is based on research that indicates that personalization of learning and assessment results in improved scholastic attainment, thinking skills, personal development, and self-confidence for learners. Simply put, PL is a teaching and learning approach centered on the needs, aptitudes, and interests of individual learners.

Overall, personalized learning has been identified as one of the top educational trends for five years in a row (2016-2020) in K-12 and higher education as well as in the highly funded sphere of Educational Technology startups. Additionally, the 2020 EDUCAUSE Horizon Report identified Adaptive Learning Technologies and AI/ML Educational Applications as the top two emerging technologies that will have a significant impact on the future of postsecondary teaching and learning and on the broader educational practice of PL.

Research on PL using AI adaptive technology has shown statistically significant improvements in degree program completion and satisfaction. For example, recent research has shown that AI-driven personalized learning scaffolds, informed by real-time data are promising interventions that dynamically respond to students’ needs enhancing self-regulated learning and leading to academic achievement, retention, and success. However further research is necessary to refine the AI-driven supports and optimize their impact on learning outcomes. Another recent study, addressed the challenges ChatGPT and generative AI technologies pose in educational contexts such as concerns about potential student misuse, misrepresentation of work, and passive learning without active participation. The researchers advocate embracing AI as a contemporary educational trend and collaborating among educators, instructional designers, user experience designers, AI researchers, and software developers to establish pedagogical principles for integrating AI technologies into instructional contexts.

This chapter will review the research on using Generative AI to personalize teaching and learning interactions and experiences in higher education contexts and provide guidelines for integrating Generative AI into teaching and learning practices starting with the following:
• Goal Setting (Prompting): Teaching prompting is crucial for developing students’ self-regulated learning (SRL). AI chatbots can prompt students to set clear goals, fostering their autonomy and metacognitive awareness.
• Feedback and Self-Assessment: Configuring reverse prompting within AI chatbots guides students’ SRL and helps monitor their understanding. Students receive timely feedback, enabling them to assess their progress and adjust strategies.
• Personalization: Developing data-driven mechanisms allows AI chatbots to provide personalized learning analytics. Learners can reflect on their learning experiences and develop effective SRL strategies.

Integrating SRL frameworks allows us to focus on using AI-assisted pedagogy to personalize the teaching and learning experience to promote students’ self-regulation which is critical for attaining lifelong learning skills. Additionally, focusing on personalizing the learning experience contributes valuable insights into sustainable educational practices, emphasizing the responsible use of AI tools to enhance personalized self-regulated learning or PSRL.

According to John Hack, a long-time performance management software innovator who started his career in the Natural Language Processing (NLP) R&D group at Wang Labs in the 1980s and is now CTO and co-founder of Interflexion, leveraging AI to help aspiring professionals develop their conversational skills, personalized instruction is the Gold Standard in workforce education contexts. With the critical need for modern-day workers to reskill and retool, requiring workers to become continuous learners, personalized instruction promises to be an effective strategy for increasing the value of training resources, reducing the time spent locating relevant resources, and supporting continuous learning and development.

The world of Industry 4.0 has fundamentally reshaped our societal landscape, especially education. Industry 4.0 principles are based on decentralized decision-making, globalization, interpretability, information transparency, and technical assistance. Through these principles, Industry 4.0 has offered continuous trends (e.g., continuous manufacturing) that have been facilitated by technologies such as artificial intelligence/machine learning, automation, big data, blockchain, cloud computing, digitization, Internet of Things, robotics, and sensors. These technologies have improved performance, stimulated growth, and increased efficiency in different sectors while maintaining sustainability.
Specifically in education, Industry 4.0 has enabled the delivery of personalized education online, cost-effectively crossing geographical boundaries. Through digitization, which creates a digital repository of all educational material, educators are empowered to make decentralized decisions while benefiting from technical assistance. Moreover, the use of cloud/edge computing enabled learning to interact with vast datasets and analyze authentic case studies using machine learning software without necessitating high-performance computing. Robotics and sensors also offered many advantages in Education 4.0 where they allowed interaction with more real-world scenarios and authentic case studies. Thereby bringing the classroom to the student and allowing a more authentic, interactive, and personalized learning experience.
Despite these advancements, Education 4.0 encounters many challenges for its full implementation. These challenges are not solely confined to hardware and software requirements but are predominantly rooted in the domain of Skills 4.0. Hence, Education 4.0 demanded new sets of skills from both learners and educators, diverging from previous educational models. Characterized by a teacher-centered approach solely focused on knowledge transfer from the teacher to the student. On the other hand, Education 4.0 focuses on both students and learning, ensuring mutual benefits. Education 4.0 has intensified soft skills, hard skills, intellectual skills, and knowledge that are essential to Industry 4.0. Yet it did not undermine knowledge, especially technology literacy where both are essential skills. Moreover, knowledge and technology literacy are key in most future jobs. Nonetheless, there are many barriers to implementing Education 4.0 related to shortages in technical competencies, management, and senior researchers due to the lack of expertise. This chapter discusses the pros and cons of Skills 4.0 in education considering efficiency, sustainability, ethics, and safety.

Over the past two decades, a plethora of events revolving around the notion of Artificial Intelligence has emerged. The euphoria addressing subject matter has resulted in much discourse addressing the expanse and diversity of topics covering areas going from the fields of personalized medicine to customized pharmaceuticals on to intelligent engineering designs going into venues for smart agriculture and arriving at creating human-like robots and instantiating weaponry and armament. Meanwhile, AI has had its pronounced toll on the diverse fields of research, education, and the advancement and creation of knowledge into what is nowadays dubbed or recognized as AIEd.
The cumulative data warehouses of industrial establishments as well as academic institutions, over many years of operation, the world over, have rightfully earned these establishments what was being referred to as Big Data establishments. Three decades ago, the various establishments direly fell in need of some capabilities that would offer forms of data analytics to help CEOs and managers with their decision-making processes arriving at ways of optimizing industrial outcomes to reduce costs of production while maximizing profit. The earlier forms of data analysis some three to four decades ago leveraged what was referred to as Expert Systems, but the underpinnings for these analytics handles were nothing more than some advanced forms of optimization algorithms that would inherently consume vast amounts of time before an administrator would properly arrive at some likely answer/s.
Due to their ongoing growth in population together with the accompanying growth in their data repositories, academic institutions, while fostering varying degrees in the areas of AI in their course undertakings, have sensed a dire need for means and ways of better exploitations and the understanding of warehoused data sets pertinent to the very functionalities of operations of these institutions. Due to the emerging needs in recent years to accommodate student learning paces of learning under outcome-based educational paradigms, academic institutions have started exploring means and ways of attending to the diverse needs of students with varying levels of grasping the subject matter on hand. This has led many academic institutions to, amongst others, adopt learning paradigms that could adapt to the varying student paces of learning. Such learning paradigms have commonly fallen under the nomenclature (or classification) of iterative e-learning paradigms.
Several academic institutions in Jordan, in their efforts to fulfill accreditation requirements, have started to transform their methods of higher education into what has become better recognized as the Digital Transformation of Higher Education. This has gone hand in hand with online and hybrid modes of rendering academic undertakings to the intended constituents. In doing so, many academic institutions have readily realized the presence of academic handicaps in delivering academic undertakings and run against various obstacles along the way. This has necessitated the adoption of smarter means of sensing the effectiveness of the delivery of the course offerings and readily came to realize the benefits levied from the adoption of AI-leveraging technologies to overcome the shortcomings of existing methodologies.
As the University of Jordan has begun a process of digital transformation of the academic processes it fosters, it has witnessed a dire need to move into full automation of various academic as well as administrative processes. For one, the University of Jordan is transforming into a smart campus that can attend to the academic needs of students as well as the teaching support needed by the academic cadre involved in administering the academic process. Various other facets of automation at the University of Jordan are well underway. This inherently includes automation of the implementation of the strategic and executive plans of the university, which also includes automation of the tracking of employee activities to help optimize functionalities of the various academic and administrative units around campus. The process also involved the tracking of student academic lifestyles to help students become high achievers with their campus lives.
In a recent chapter accepted for publishing through AAS on the Digital Transformation of Higher Education in the Arab World, the authors have delineated the various steps to advance the digital transformation process forward. In this chapter, we will review the various details and existing alternatives pertinent to AI exploitation which the University of Jordan has considered throughout its automation lifecycle and will inherently address the various AI alternatives that the university is, or will be, adopting in due process.

The use of artificial intelligence (AI) in higher education (HE) offers immense promise in transforming the educational landscape, particularly in the Arab states. In addition, AI has the potential to instigate a massive paradigm shift in higher education within the Arab states, offering new opportunities for personalized learning, improved student outcomes, and enhanced teaching practices, as well as enhancing students’ research nexus. This chapter examines the transformative potential of AI in higher education across the Arab states, exploring how AI-powered tools and platforms can revolutionize teaching and learning processes and elevate student experiences.
By leveraging AI technologies, educational institutions in the Arab states can create adaptive learning environments, provide personalized feedback to students, and support educators in developing innovative teaching methods. This chapter will further examine the current landscape of AI in higher education in the Arab states, shedding light on the opportunities and challenges associated with the integration of AI in educational contexts. AI has the potential to revolutionize higher education by personalizing learning experiences, automating administrative tasks, improving student engagement, and enabling data-driven decision-making. AI-powered tools such as chatbots, virtual assistants, predictive analytics, and adaptive learning platforms can benefit institutions streamline operations, and support student success. By harnessing the power of AI, universities can foster a more efficient, effective, and inclusive learning environment that caters to the diverse needs of both students and faculty. Sohar University has embraced AI technologies to augment its educational offerings and administrative processes.
The university offers AI programs at the bachelor’s level and has implemented AI-powered chatbots to deliver personalized support to students; virtual classrooms for remote learning, and predictive analytics tools to identify at-risk students and allow for early intervention. Additionally, the university aims to delve into cutting-edge technologies linked to AI. These initiatives have improved student engagement, retention rates, and overall academic performance at the university. By integrating AI into its educational practices, the university has demonstrated a commitment to innovation and excellence in higher education. AI has the potential to transform higher education in the Arab states by enhancing teaching and learning experiences, improving administrative efficiency, and fostering innovation. Institutions within certain Arab states serve as models for how AI can be successfully integrated into educational practices to benefit students, faculty, and the broader community. By embracing AI technologies and adhering to best practices, Arab universities can position themselves as pioneers in the digital era and provide a world-class education that equips students with the challenges and opportunities of the future.

Artificial intelligence (AI) has encountered several aspects of our lives, and the education sector is not an exception. The recent advancement in information technology in general, and more specifically in the field of AI, has contributed to major and multiple leaps in the pedagogy of teaching and learning in the higher education sector. The University of Petra (UOP), like any other higher education institute worldwide, has been faced with these major and imperative leaps that AI mainly influences. Thus, academic staff from different programs and disciplines are utilizing AI applications and tools within the teaching and learning process, whether for research purposes or academic teaching purposes. Therefore, based on the widespread use of AI in education, multiple questions arise within the strategy of education at the UOP.
This study addresses several questions in order to understand the academic staff’s views and opinions about the uses of AI, which is performed by an extensive and exhaustive study that takes into consideration many aspects such as i) the objectivity of the subject, for instance, the technical aspects (i.e., the readiness (in campus), and ii) secondly and equally important, through statistical analysis of a dataset aggregated by a questionnaire that is engineered for the study in hand to conclude whether the majority of the academic staff of the university are capable, prefer, and are using some AI tools, or the majority of the sample prefers traditional methods. This conclusion is a cornerstone that would influence the improvement of the current processes to reach optimal utilization of AI on the one hand, and on the other hand, it will be the foundation of any solution to overcome all the challenges and obstacles as it leads the solution by pinpointing the weaknesses and strengths within the UOP academic staff.

As the technological landscape evolves, the demand for engineering graduates equipped with interdisciplinary skills, critical thinking, and the ability to solve complex problems is becoming paramount. Artificial Intelligence (AI) offers unprecedented opportunities to enhance engineering education. This chapter explores the methods universities can undertake to integrate AI into their educational framework and pedagogy. By incorporating AI into engineering education, various aspects of academic life will be revolutionized; this includes curriculum development, teaching, learning, practical applications, simulations, and research.
In addition, this chapter also integrates AI into each stage of the Conceive, Design, Implement and Operate (CDIO) pedagogy which in turn will empower students and faculty to innovate in unprecedented ways. AI-driven tools, simulations, adaptive learning, and platforms allow students the opportunity to experience real-world engineering challenges and become adept at navigating complex problems. This chapter offers step-by-step details of strategically integrating AI into engineering education with a special focus on CDIO. It also discusses the infrastructure required for such integration including curriculum design, faculty training, student engagement, facilities, and ethical considerations.

Artificial intelligence (AI) is rapidly transforming higher education, promising personalized learning, administrative efficiency, automated evaluations, and revamping research methodology. However, this transformative potential is intertwined with regulatory and ethical concerns about bias, algorithmic discrimination, data privacy, the potential for human displacement, and the erosion of human interaction. Thus, responsible governance is crucial to ensure that AI serves as a positive force, empowering students, faculty, and institutions to thrive in the digital age. Challenges abound at various levels: students face potential bias in algorithms used for grading, admissions, and resource allocation; classrooms grapple with impersonalized learning environments and the potential loss of human interaction; institutions struggle with issues of transparency and accountability in AI implementation; and the state and sector require clear guidelines to address data privacy, algorithmic bias, and workforce impact. Whereas a silver bullet resolution may not be possible, collaborative efforts among the stakeholders are crucial to prevent a fragmented approach and devise creative solutions. These efforts would ensure:
1. A human-centered AI design, where AI is a tool augmenting human intelligence;
2. Inclusive data practices, including data anonymization, bias audits, diverse data collection methods, and transparency in data collection and usage;
3. Clear ethical frameworks that address issues of bias, fairness, privacy, and accountability; and
4. Collaborative governance between institutions, governments, and technology companies to develop and implement effective regulatory frameworks.
This requires investment in education and training that equips stakeholders to navigate the AI landscape responsibly. By charting a responsible course with robust governance, ethical implementation, and a commitment to continual learning and adaptation, we can build a future where AI enhances the learning experience, promotes equitable access, and empowers individuals.

Artificial General Intelligence (AGI) and large language models (LLMs) represent a transformative force in education, offering both promise and complexity. This paper investigates innovative approaches to harness AI’s potential for engaging teaching, interactive learning, and adaptive assessment in higher education. The paper introduces a comprehensive decision-making framework tailored for universities, enabling them to navigate the evolving landscape of AGI technologies while enhancing student and faculty experiences. By emphasizing adaptive strategies for AI-powered assessment and engaging pedagogy, this chapter addresses the practical integration of AI within higher education settings. Through a lens of adoption practices, the framework explores opportunities and challenges associated with the flourishing role of LLMs, and more broadly, Multimodal AI, to pave the way for a forward-looking educational era.

This chapter investigates the essential enhancement of AI innovation and technology transfer within the higher education sector in the Arab region, highlighting the indispensable role of artificial intelligence in fostering an ecosystem of technological advancement and economic development. It underscores the essential nature of intellectual property (IP) management, particularly in patent mining, filing, exploitation, and identifying R&D areas, as a core asset for AI-driven startups and a cornerstone for a thriving innovation ecosystem. Boosting the integration of Arabic training content in AI is essential for developing Generative AI for Arabic competitive with the English counterparts in LLMs, NLP, and training data. The discourse further emphasizes the importance of establishing dedicated technology transfer units or offices (TTUs) in universities, research institutes, and government entities to facilitate the fluid exchange between inventors, researchers, and the commercial sector. The presented mechanism would enhance the path from innovation to the marketplace, especially in AI, which will dominate innovation and research during the coming decade. The TTUs are essential for protecting researchers’ rights, reducing administrative hurdles in industry collaboration, and ensuring the continuous flow of innovation. These will safeguard intellectual property and ignite an entrepreneurial spirit among faculty, students, and industry partners. The chapter describes gluing the different components mentioned: IP management, TTUs, AI innovation, and promoting Arabic content to advocate for an ecosystem that prioritizes and accelerates the commercialization of AI research. The Arab world is positioned to leapfrog into a future where its indigenous innovation drives economic growth and societal advancement. Through policy recommendations, strategic frameworks, and AI-specific case studies, this chapter outlines a roadmap for embedding IP management, entrepreneurship, and technology transfer into the higher education landscape, aiming to transform academic excellence into tangible societal and economic benefits.

The functionality of academic institutions has, in general, evolved around a vision, a mission together with some strategic planning commensurate with the vision and mission statements set therefrom. Most academic institutions in the Western sphere have evolved around goals fulfilling the goals of their strategic plans. Meanwhile, academic institutions in the developing world, with Arab countries constituting a subset, have evolved either partly or entirely on vision and mission statements pertinent to each institution short of having strategic plans in conjunction with the underlying vision and mission statements. And, where such institutions have grown around well-couched strategic planning, these institutions have generally fallen short of their ability to fulfill their plans and gauge the degrees of such fulfilments.
Just about the past decade to a decade and a half, academic institutions in the Arab world have started to put together more robust strategic plans accompanied by executive plans towards fulfilling the targeted goals. During the last few years, academic institutions in the Arab world, particularly in the interim of the euphoria of endeavors to fulfill regional and international accreditation requirements, have come to realize the necessity to move forward on endeavors leading to the digital transformation of the academic models they fostered. Such moves have enticed most of these institutions to automate not just the academic model delivery systems but also to automate, to varying degrees, the execution of the strategic goals with ways to measure the levels and rates of achievement of such goals. In so doing, this has helped create a niche where the performances of faculty members, administrative staff, and students could be assessed and gauged against the requirements embedded into the strategic and executive plans of the participating institutions. With them, such practices have created enormous amounts of data that could be leveraged to properly measure the intended quantities.
The accumulation of data over time has brought with it a dire need for huge data storage capabilities which would ultimately evolve into what is currently dubbed “data warehouses” with the participating institutions becoming known as big data establishments. In the past, this had required the presence of what the technology community then dubbed “expert systems” that would offer appropriate data analytics to help administrators with their decision-making chores. However, as technology evolved further, the prohibitively huge data sizes have inherently required leverage of the more upscale capabilities of what people have started to recognize as AI systems and machine learning paradigms. During the past decade, the University of Jordan has committed to developing a state-of-the-art strategic plan that reflects the aspirations and ambitions of a highly developing academic institution in the Middle East. Over the past couple of years, the institution has put together its strategic plans into molds that can be executed and assessed at the individual, departmental, college and administrative levels. To put its strategic plans into a readily executable form, the administration took up the strategic planning into sets of goals with targeted outcomes according to some preset key performance indicators with grade allocations associated with each targeted objective.
More recently, the university has embarked onto a process of implementing its strategy into a more engaging approach wherein each employed individual, student and/or administrator would pitch in their shares harnessing the latest AI technologies and machine learning regimes. The process starts with the faculty portfolio wherein the faculty member would fill in their resume data into well-designed database tables that would be followed with the intended professional development goals of the participating faculty members. By year’s end, these goals will be assessed against what the individual would have committed to at the beginning of the academic year. Students would also take part in this by filling in a student portfolio as their graduation dates draw near. This (student portfolio) will be leveraged to gauge the intended learning outcomes by students as designed by the course and program offerings. Finally, course development efforts would be embedded in the execution of the institutional strategic objects using what is being dubbed “Course Folio”.
In this chapter, we will review the various processes involved in the execution of the strategic plans of the university and for each process will offer certain levels of insight into the machine learning and AI deployments that have been harnessed in the overall paradigm. This will show pronounced evidence of how the University of Jordan has carried out the execution paradigms that will have put the institutional strategic planning into good running order.

A discerning observer of teaching methods in education and higher education levels in the Arab world (levels commonly known as key stages 1 to 5 in the UK system), can quickly realize that the methods have not changed much over the decades, i.e., since the start of the 20th century. Teacher-centered education is the norm with the classical use of the blackboard (or whiteboard) and, during the last quarter of the century or so, the data show. The presence of a teacher is important, in fact crucial, in providing proper guidance to the students. Nevertheless, in more crowded classrooms, especially in Higher Education amphitheaters, personalized attention to the student’s learning becomes very difficult, not to say impossible.
A lot has been researched and written about learner-centered education where the learning is meant to improve on the learner’s active request and participation. This learning includes the material content as well as an automated evaluation of the learner’s acquired knowledge and skills. This takes one straight into the realm of Adaptive Learning Systems where each learner gets his/her relevant learning material and exercises based on the concepts of Scaffolding and Chunking (of the material). It has been shown that this new paradigm can help many types of students improve on their own. If learner-centered education is not to replace the presence of a teacher, it can definitely be a very enriching complement that can make up for some of the shortcomings of teacher-centered learning.
Our “Natural Language Processing, Machine Learning, and Applications” research group of the Laboratory for Research on Artificial Intelligence, hosted by the University of Science and Technology Houari Boumediene (Algiers) started a few years ago R&D on the development of a learner-centered digital environment to allow learners to improve their skills in the Arabic language in terms of vocabulary learning, reading comprehension, essay writing, and question-answering. The adopted approaches and techniques are from Artificial Intelligence, Natural Language Processing, and Machine Learning. The aim of these prototypes is to eventually build comprehensive, customized tools for learner-centered education for levels going from Key stage 1 to Key stage 5 (i.e., till higher education) and the domains can cover, for a start, the Arabic language and literature, Islamics, History and Geography.
Recent developments of Artificial Intelligence have produced a fairly revolutionary technology, the Large Language Models (LLMs), best exemplified with the launching on the 30th of November 2022 of ChatGPT by OpenAi. This was the first time an LLM was introduced to the public at large, not just to be used by AI practitioners. Since then, it has been a very big hype for the use of the LLM, so much so that, since then, numerous are the LLMs that have been trained and made available to the public, either freely and sometimes integrated into search engines, or through subscription models. In this chapter we present some of the work that we have already done on the use of LLMs in developing Adaptive Learning Systems for reading comprehension of Arabic texts, development of a learner’s general culture (in Arabic literature, Islamics, history, geography, and science), learning of Arabic grammar, and learning of English by Arab natives. We will also draw the roadmap of the future work that we envisage to do within our vision of productively introducing AI in the Education and Higher Education contexts in our universities. The work we are doing is still prototypical in the sense that the ultimate aim is to see the same techniques generalized to a national context and, why not, to a Pan-Arab context. We believe this is an ambitious vision that merits all our efforts.

This chapter explores the transformative impact of the ISO 42001 AI Management System (AIMS) Standard on enhancing the integrity and innovation of AI research within the academic domain. ISO 42001, introduced as the world’s first AIMS in December 2023, serves as a pioneering management system standard that is central in reinforcing the foundational integrity of AI research. Our contribution to this area involves validating the standard’s clauses across different aspects and stages of research and assessing their effectiveness in promoting reliable and innovative AI research practices.
This includes exploring the guidelines that outline best practices in data management, encompassing the collection, storage, analysis, and dissemination of research data. These guidelines are pivotal in ensuring the accuracy, security, and transparency of data handling, thereby fostering the reliability and reproducibility of research findings. Moreover, we examine the strength of the standard in fostering a culture of innovation within AI research by providing a structured yet flexible framework that encourages researchers to venture into new territories of AI technology with creativity and ethical foresight.
This dual focus on rigorous data management and the promotion of innovative research creates a conducive environment for researchers to explore AI’s potential responsibly and inventively. The chapter will also demonstrate how embedding ethical guidelines within its framework, ISO 42001 ensures that AI innovations are developed with a keen awareness of their broader social, economic, and ethical impacts. This approach not only promotes the responsible development of AI technologies but also aligns research endeavors with societal values and norms. In conclusion, through its comprehensive guidelines and ethical framework, ISO 42001 could potentially be a key player in shaping the future of AI research in academia, ensuring that it is both robust in its methodological rigor and dynamic in its innovative capacity.

The onset of the Artificial Intelligence (AI) revolution offers a unique opportunity for the Arab World to transform its educational paradigms, aligning them with technological advancements and future workforce needs. This chapter outlines a targeted strategy for revamping higher education curricula across Arab universities to better equip students for the era of AI. Initially, the chapter assesses the current status of AI education within the region, identifying significant deficiencies and areas ripe for development.
It then presents a comprehensive proposal for integrating AI fundamentals, ethical frameworks, and hands-on experience into existing courses, while also advocating for the establishment of specialized AI programs. This initiative promotes an interdisciplinary approach, merging computer science with various fields such as healthcare, finance, and environmental studies, aiming to nurture a multifaceted and innovative professional cohort.
Additionally, the chapter highlights the importance of minor yet impactful collaborations with the industry to ensure the practical relevance of the educational content. While emphasizing less extensive industry engagement, it suggests incorporating real-world case studies and guest lectures from industry professionals to bridge the gap between academic theory and practical application. This strategy enriches the learning experience and keeps the curricula up to date with the latest AI advancements.
Moreover, the imperative need for supportive measures such as policy reforms, investment in infrastructure, and faculty development to enable these curriculum enhancements is discussed. Through examining successful global models and adapting them to the Arab context, the chapter offers actionable insights for universities, policymakers, and educational leaders. These recommendations aim to position the Arab World as a leader in AI education, fostering a well-prepared generation to contribute to the global technological landscape. The strategic update of higher education curricula, with a balanced focus on AI integration, is envisioned to drive the region toward technological innovation and heightened economic growth.

The rise of artificial intelligence (AI) is ushering in a transformative technological revolution with profound impacts across industries and societies. For nations to remain competitive in this new era, developing world-class AI capabilities within the higher education sector is a strategic imperative. However, most Arab countries currently lag far behind international leaders in measures of AI research output, talent development, and skills cultivation. This comprehensive chapter provides an objective assessment of the gaps facing Arab universities in AI, benchmarked against global metrics and best practices. It then outlines a multipronged strategy to catalyze AI excellence by addressing the foundational pillars of cutting-edge research and innovation ecosystems, preeminent AI education and curricula, robust talent pipelines spanning K-12 to professionals, and amplification through global collaboration and knowledge exchange. Specific focus areas include strengthening AI R&D funding and industry partnerships, embedding AI across disciplines, creating AI fundamentals pathways from early education, attracting elite AI faculty, and facilitating brain circulation.
The chapter culminates with a holistic implementation roadmap for crafting national AI strategies, sustainable funding models, capability development frameworks, and an integrated AI ecosystem. By pursuing the comprehensive recommendations outlined, Arab institutions of higher learning can work to close the existing gaps and emerge as powerhouses of AI innovation and human capital development. However, achieving this lofty vision will require long-term commitment, coordination, and investment commensurate with the boundless opportunities that lie ahead in the age of artificial intelligence.

A large number of industries, including education, are being revolutionized by artificial intelligence (AI) and this trend is only going to become more popular and widespread with time. The use of AI applications in English Language Teaching (ELT) in higher education in the Arab World is examined in this chapter and its potential for enhancing the teaching and learning experience. This will be done by presenting an overview starting by defining AI, its components, and its potential importance as a teaching tool.
It will also highlight the current role of AI in English Language Teaching and future teaching approaches. Therefore, the application of AI tools, as an instructive guide, will be discussed by giving examples of AI-based language learning platforms that adopt intelligent tutoring systems, such as chatbots, virtual reality and augmented reality and their role in ELT. Thus, Natural Language Processing (NLP), an interdisciplinary subfield of computer science and linguistics, will be discussed to shed light on machine learning applications like Grammarly, Duolingo, and Google Translate which are AI-driven technologies that can assist learners in language learning and teaching.