ABSTRACTS

Developed nations operate within well-established industrial models that are closely aligned with long-term national development strategies, including defense, security, and economic priorities. These models have traditionally acted as guiding frameworks that steer research agendas within universities and industrial institutions toward clearly defined and productive outcomes. As a result, strong alignment between industrial needs and academic research has contributed significantly to sustained innovation and the economic success of industrialized countries. In contrast, many developing regions, including Arab nations, often lack similarly integrated industrial models. This absence has contributed to fragmented research directions, where much of the academic output is driven by individual faculty efforts linked primarily to promotion requirements, rather than coordinated national priorities. Consequently, research activities are frequently disconnected from real industrial needs, leading to limited impact on economic development and a tendency toward sporadic, uncoordinated scholarly output. In this context, artificial intelligence presents a significant opportunity for developing countries to better align research systems with national development objectives in the era of the Fourth Industrial Revolution. AI can help identify institutional strengths and gaps, map them against strategic economic priorities, and guide more coherent research directions aligned with national agendas. It can also support the matching of research expertise with development needs, enabling more structured and impactful innovation ecosystems. Ultimately, this approach can contribute to the gradual emergence of viable industrial models that are better suited to the economic realities and aspirations of these nations.

Since 2022, artificial intelligence (AI) has shown extensive applications across most aspects of our lives, and scientific research is no exception. Some AI applications, such as ChatGPT, can now conduct broad literature reviews, provide scientific interpretations, write scientific papers, and respond to reviewer comments, which changes the researcher’s role. With such machine intelligence, it is currently unclear what the remaining contribution of human intelligence is compared with AI. Scientific promotion, PhD and master’s theses, graduation projects, seminars, presentations, quizzes, and homework can all be generated by AI, and the three categories of education outcomes, knowledge, skills, and competencies that the researcher should gain are nowadays at risk if AI is not used properly. Higher education institutions (HEIs), along with many international organizations, have begun drafting regulations and policies to address the challenges of AI revolution, maintain human intellectual efficiency, leverage AI’s benefits, and avoid its potential dangers. AI declaration forms and AI usage percentages were proposed to regulate the use of AI while maintaining the role of human intellectual effort. In general, the use of AI to enhance the language of scientific research and to collect updated references has been accepted; however, generating ideas with AI has been prohibited. It may be that the new researcher will be the one who is able to use AI prompts rather than possessing many other classical skills, and that the rules of scientific promotion will no longer be valid. In the current investigation, we conduct a literature review of best practices for the use of AI in scientific research. Additionally, the questionnaire results, which reflect the opinions of the University of Petra (UOP) faculty members on the use of AI in scientific research, are presented and discussed.

This paper explores how higher education institutions in the Arab world can leverage artificial intelligence to advance economic development through creative, student-centered research, using Sohar University in Oman as a case study. In alignment with Oman’s Vision 2040 transition toward a diversified, knowledge-based economy, universities are increasingly expected to graduate students who are not only AI-literate, but also capable of understanding core AI theories and applying them to address local industrial and societal challenges. At Sohar University, recent developments provide a timely context for examining this transformation. These include the strengthening of student-centered learning and research through the Teaching Research Nexus (TRN) approach, the introduction of AI and Cyber Security programs, the development of AI policies and guidelines, and the establishment of an Innovation Centre, alongside strategic investment in AI infrastructure and institutional reaccreditation processes. Within this setting, the study focuses on three student-centered initiatives: the integration of AI modules across non-STEM programs to enhance cross-disciplinary problem-solving skills; the “AI for Al Batinah” undergraduate research incubator, where students collaborate with SMEs, ports, and logistics companies in Sohar to develop AI solutions for supply chains, energy efficiency, and water management; and a capstone reform that replaces traditional theses with industry-linked AI projects evaluated based on creativity, impact, and commercialization potential. Drawing on preliminary findings from student surveys, graduate employment data, and project outcomes, the paper suggests that embedding AI within creative, research-driven learning environments enhances student employability, entrepreneurial mindsets, and contributions to regional economic diversification. It also examines the extent to which this approach supports the emergence of tech-based ventures and improves access to roles within Oman’s growing digital economy. Overall, the study argues that Arab universities can replicate this model by aligning reaccreditation standards with AI pedagogy and student research ecosystems, thereby positioning students as active contributors to economic transformation rather than passive beneficiaries. The case of Sohar University demonstrates that when institutional strategy, AI infrastructure, and student engagement in research are effectively aligned, higher education can serve as a direct catalyst for sustainable development in the Arab world.

Artificial Intelligence (AI) is reshaping labor markets, and there is growing recognition that early exposure to AI research is essential for future prosperity and long-term economic competitiveness. While higher education systems have traditionally emphasized postgraduate research and commercialization, the process of translating ideas into market-ready solutions, far less attention has been given to undergraduate AI research and its potential role in building national competitiveness. This chapter highlights undergraduate AI research as a foundational element in strengthening economic capacity over time. By engaging undergraduate students in authentic research experiences, universities can equip them with practical AI skills, enhance their ability to contribute to innovation, and enable them to address real-world challenges across multiple sectors. In this way, undergraduate research serves as an early pathway for developing a more innovation-ready and capable talent base. The chapter presents a conceptual framework that connects undergraduate AI research to economic competitiveness through four dimensions: advanced skill development, creative problem-solving, entrepreneurial growth, and productivity improvement across sectors. It also examines the roles of students, faculty, university leadership, and government in embedding undergraduate AI research within higher education systems, with contextual examples from Kuwait and Jordan. Finally, it proposes measurable indicators and policy recommendations to support the structured integration of AI research across disciplines, positioning undergraduate research as a strategic lever for strengthening competitiveness in an AI-driven global economy.

The rapid proliferation of artificial intelligence (AI) tools has fundamentally transformed the learning landscape for Generation Z (born 1997–2012), the first cohort to experience AI as a native feature of their academic lives. While global discourse has largely focused on the disruptive potential of AI in education, empirical evidence regarding how Arab Gen Z learners specifically engage with these tools remains limited. This study investigates the AI-related learning behaviors and habits of undergraduate students at Abu Dhabi University (ADU), serving as a representative sample of higher education learners in the Arab world. Adopting a mixed-methods approach with a sequential explanatory design, the study combines quantitative survey data with qualitative insights from interviews to provide a comprehensive understanding of student-AI interaction. The research evaluates four interconnected dimensions: the types and frequency of AI tools used (such as ChatGPT and Microsoft Copilot); student attitudes toward academic integrity; the extent to which AI supports or undermines self-regulated learning and critical thinking; and students' perceived readiness for an AI-driven job market. The findings reveal how Gen Z learners in the UAE integrate AI into their academic routines, highlighting a notable tension between convenience-driven usage and the development of deep cognitive competencies. This paper argues that these behavioral patterns carry significant implications for curriculum design, assessment policy, and institutional governance across Arab universities. Building on this empirical evidence, the study proposes a set of policy recommendations aimed at enabling regional higher education institutions to harness the economic and creative potential of AI while cultivating academically resilient, critically thinking, and workforce-ready graduates.

Higher education institutions are increasingly expected to play a strategic role in supporting innovation, digital transformation, and knowledge-based economic development. Recent years witnessed increasing attention given by most universities to international accreditations. However, international accreditation is not only perceived as quality assurance of the educational system, but also as a catalyst for change in university’s overall environment. The culture of continuous improvement and Impact is a corner stone for any higher education institution seeking and maintaining international accreditation. Although the Association to Advance Collegiate Schools of Business (AACSB) plays a significant role in shaping how business schools integrate AI into business education by promoting innovation, technological relevance, and continuous curriculum improvement can also indirectly impact on the overall culture of the university by the conferences take aways brought back by faculty members attended these conferences. This chapter will highlight how AACSB accreditation affected not only the business school, but also how the business school had a leading role in changing and shaping the overall technological culture of the university giving Al Ain University experience in this regard. Bringing insights from five annual conferences, the college of business has each year proposed AI and innovation-related ideas that were adopted and implemented in the university. The chapter argues that international accreditation can function as a governance and policy mechanism that strengthens universities’ capacity to contribute to innovation ecosystems, enhance university–industry knowledge exchange, and generate economically relevant outcomes. By linking accreditation, AI integration, and institutional transformation, the study offers insights into how Arab universities can leverage global frameworks to support sustainable economic development and competitiveness in the AI-driven global economy.

Higher education institutions in the Arab world are under increasing pressure to balance academic quality and research productivity with operational efficiency and national development goals. In this landscape, artificial intelligence is emerging as a critical enabler of institutional transformation. Rather than being limited to teaching and research, AI can drive "operational excellence" by aligning people, processes, and data-driven decision-making. This enables universities to redesign core functions, ranging from enrollment management and student support to resource allocation and risk management, replacing fragmented, manual systems with proactive, intelligent automation. Internal operational efficiency directly translates into broader institutional and economic impact. By adopting AI-driven workflows, universities become more agile and better equipped to respond to labor market demands and national diversification agendas. In the Arab regional context, where digital maturity varies, successful transformation requires moving beyond isolated technical experiments toward an integrated strategy. This means modeling the intelligent use of technology within the university’s own structures to serve as an engine for human capital development and knowledge-based growth. Unlocking the full potential of AI requires a strategic framework focused on leadership commitment, robust data governance, and the redesign of legacy processes. Implementation must be purposeful and responsible, addressing ethical oversight, workforce readiness, and cybersecurity. Ultimately, by positioning AI-driven operational excellence as a central strategic capability, Arab higher education institutions can improve their resilience, enhance student success, and solidify their role as primary drivers of regional socioeconomic advancement.

Disease diagnosis is a critical determinant of clinical outcomes, particularly in the context of non-communicable diseases and multimorbidity, where delayed detection is associated with increased morbidity and healthcare burden. In many settings, especially under-resourced and fragmented healthcare systems, late diagnosis is often driven by limited access to affordable diagnostic tools and specialist expertise. Recent advances in artificial intelligence (AI), including deep learning and generative models, have demonstrated significant potential in extracting clinically relevant patterns from complex medical datasets, such as imaging and multimodal patient data. However, the translation of these technologies into sustainable healthcare practice remains constrained by gaps in data availability, validation, and workforce readiness. The proposed approach entails a human-in-the-loop, education-integrated framework that embeds AI-driven diagnostic research within higher education systems. The framework encompasses (i) structured real-world data collection and validation, (ii) guided development of synthetic datasets to address data scarcity, (iii) the design and evaluation of AI diagnostic models, and (iv) the integration of AI outputs into clinical decision-making processes and medical guidelines. By positioning universities as hubs for AI-enabled healthcare innovation, this approach supports the co-creation of diagnostic tools that combine algorithmic scalability with human expertise. The proposed model contributes to sustainable and cost-effective healthcare systems while fostering research capacity, workforce development, and innovation ecosystems in the Arab world. It further contributes to AI-driven economic development by enabling cost-efficient diagnostic innovation, reducing healthcare expenditure, and supporting knowledge-based economies in the region.

As artificial intelligence transforms intelligence into an abundant, on-demand resource, the traditional link between education and cognitive capability is rapidly dissolving. This shift lends credence to the idea that access to capital can substitute for years of formal learning, as individuals increasingly rely on AI systems to perform tasks that once required advanced expertise. However, the commodification of intelligence does not eliminate the need for education; it redefines it. While knowledge acquisition and routine cognitive skills decline in value, the ability to interpret, direct, and responsibly apply machine-generated intelligence becomes paramount. This transition introduces new forms of inequality, separating those who can effectively leverage AI from those who cannot. Access to AI is not as simple as “having money.” While cost matters, so do infrastructure, context, and the skill required to use these systems effectively. The emerging divide is not only between those who can pay and those who cannot, but between those who can leverage intelligence and those who merely consume it. In this emerging future, higher education is not rendered obsolete, but it is unbundled: its enduring value shifts from the transmission of knowledge to the cultivation of judgment, creativity, metacognition, and social capital. The central question, therefore, is not whether education remains necessary, but what forms of it retain meaning in an age where intelligence itself is no longer scarce.

Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping organizational processes, knowledge creation, and innovation capabilities across various sectors. In the Arab world, the growing adoption of AI technologies presents new opportunities for enhancing innovation performance and strengthening the transition toward knowledge-based economies. AI applications enable organizations to analyze large volumes of data, automate complex tasks, and support intelligent decision-making, thereby improving their ability to generate creative solutions and develop innovative products and services. This study examines the role of Artificial Intelligence as a catalyst for innovation performance in the Arab world. The research explores how AI adoption contributes to improving innovation outcomes by enhancing knowledge management, supporting research and development activities, and fostering organizational creativity. The study adopts an empirical approach based on survey data collected from professionals and academics across selected institutions in the Arab region. Statistical analysis is used to examine the relationship between AI adoption and innovation performance. The findings are expected to demonstrate that AI adoption significantly enhances innovation performance by improving efficiency, facilitating knowledge discovery, and enabling organizations to respond more effectively to dynamic market and technological changes. The study provides valuable insights for policymakers, academic institutions, and organizational leaders seeking to leverage AI technologies to promote innovation and sustainable economic development in the Arab world.

Universities in the Arab world can harness AI-driven research in higher education to promote economic diversification, create jobs, and foster social progress by aligning their academic initiatives with national development goals. University AI research primarily focuses on key areas such as energy transition, automation, healthcare accessibility, agricultural technology, and smart city development. Additionally, universities can leverage AI to transform higher education research into a powerful driver of industrial, commercial, and socioeconomic value by aligning projects with national priorities and building strong partnerships with industry. This paper presents a comprehensive strategic framework for universities in the Arab region to harness artificial intelligence (AI) in higher education research, positioning AI as a key driver of industrial innovation and socioeconomic development. In an era of rapid digital transformation, where AI is reshaping industries and economies, universities must go beyond traditional, discipline-specific research and focus on mission-oriented research that directly addresses national priorities such as economic diversification, sustainable development, sector modernization (e.g., energy, healthcare, agriculture), and inclusive growth. By integrating AI into research ecosystems, institutions can develop scalable, deployable solutions that not only support GDP growth through job creation and exportable technologies but also address pressing societal challenges such as inequality, climate resilience, and inefficiencies in public services. This approach accelerates innovation in sectors such as energy, healthcare, and finance, while also addressing broader social challenges related to sustainability and equity.