ABSTRACTS

Ongoing technological advancements have brought with them the birth of the 4th industrial revolution in a manner that has necessitated significant reforms to the academic models that have prevailed for centuries. Classical academic models, particularly lecture-based strategies, can no longer cope with the rising aspirations of younger generations that have grown up adapted to current-day trends that rely heavily on the ubiquity of communication technologies that have become within reach of almost every human on the planet. Such adaptations, coupled with heavy reliance on gadgets that have embedded within them extreme capabilities, must be leveraged to customize the diverse spectrum of learning experiences of the constituents. Better yet, since the world has moved towards educational regimes that have placed the learners to the front and center by moving into student-centric learning paradigms, more room has been opened up for students to provide their input into the academic process, leading to a betterment in the yield of these educational systems.
During the last quarter of the 20th century, technology has progressed by leaps and bounds in a manner that has witnessed the creation of technology companies that came about, in many cases, because of aspiring student innovations evolving at several world-renowned academic institutions. In the last 3 decades alone, the evolution of what has become known as Artificial Intelligence (AI) has brought paradigm shifts that have revolutionized the lifestyles of human beings that just a half century ago would have seemed most unlikely. The dawn of the AI age has brought with it accelerated rates of progress and achievements that have already started to revolutionize the ways we conceive things. This, inherently, has not only touched upon the speed with which students can learn, but it also has brought with it greater potential ways of supporting our abilities to undertake productive research and to produce better research results faster.
The advent of the digital age, fortified by the leverage of AI, offers an ideal environment for students to fulfill their learning aspirations and enhance their appetite for knowledge acquisition. It provides students with tools that can bolster their learning experiences and offer them various enablers that can stretch their imagination as they learn. For educators, the digital age has helped teachers with educational tools and classroom support in ways that would have not been possible just 25 years ago.
In this chapter, we address the value added through student engagement in both a conventional classroom setting and by helping the academic system adapt better to student learning needs. We emphasize the importance of utilization of AI tools in engaging students to improve existing academic offerings and pinpoint in depth the effectiveness of AI tools in encouraging more student involvement in various technology-oriented innovations and more-productive research endeavors.

There is no doubt that digital transformation has been expanding in the higher education sector. Since the COVID-19 pandemic, electronic and blended learning have been continuously increasing. Many Arab universities have made this form of education a fundamental part of the teaching and learning process. Recently, artificial intelligence (AI) has been rapidly spreading in different life applications, and the education sector is not an exception. But have students been able to engage with this type of learning, and has this form of education become a competitor to traditional face-to-face learning? Both digital and traditional education have their advantages and disadvantages that we must consider.
At the University of Petra, there are three electronic learning platforms available: Blackboard, Moodle, and Microsoft Teams — all of which are used in the educational process. Digital tools like Moodle and Blackboard have become important parts of university education. This study aims to explore how students at the University of Petra (UOP) view these platforms, especially their functionalities such as interactive videos, discussion boards, assignment submission, and tools that support student engagement in learning. A survey will be distributed to students from all departments to gather their opinions about the ease of use, usefulness for learning, and impact on communication and academic experiences.
The study also connects to the UOP’s vision of promoting modern technology and artificial intelligence (AI) tools in education to create smarter, more personalized learning experiences. Understanding student perceptions will help in aligning digital learning platforms with UOP’s efforts to integrate AI features that support innovation, improve student engagement, and enhance overall academic performance.
The findings will provide recommendations to improve the use of Moodle and Blackboard in teaching and learning. They will also highlight the importance of using digital and AI tools to create more interactive, engaging, and supportive learning environments and offer proper training and support for students and teachers.

Artificial Intelligence (AI) is revolutionizing student engagement, transforming the way students interact with learning materials, instructors, and their academic environment. Traditionally, student engagement was limited to classroom participation and structured assignments, but AI has introduced personalized learning pathways, real-time feedback, adaptive assessments, immersive experiences, and predictive analytics that optimize academic support. AI-driven tools, such as virtual tutors, chatbots, and intelligent learning platforms, provide students with instant access to tailored resources, ensuring continuous learning beyond classroom walls.
This chapter explores the multifaceted impact of AI on student engagement, beginning with an examination of AI’s role in adaptive learning and personalization, where machine learning algorithms analyze students’ learning behaviors to provide customized content and pacing. The discussion then moves to AI-driven predictive analytics, which enable universities to identify at-risk students early, allowing for timely interventions and personalized academic support strategies.
The chapter also highlights the opportunities AI presents in fostering collaborative learning, including AI-powered peer matching, gamification, and virtual reality (VR)-driven interactive education. AI’s ability to break down language and accessibility barriers is examined through its role in speech-to-text technologies, real-time translations, and assistive tools for students with disabilities. The integration of AI in career readiness is also explored, showing how AI-powered resume analysis, job recommendations, and interview coaching are preparing students for a rapidly evolving workforce.
However, while AI brings significant benefits to student engagement, it also introduces critical ethical and practical challenges. The chapter critically examines concerns related to data privacy, algorithmic bias, the risk of over-reliance on AI, and the potential reduction of human interaction in learning environments. Universities must ensure that AI is implemented responsibly, balancing technological efficiency with human mentorship, emotional intelligence, and ethical oversight.
Finally, the chapter outlines future directions in AI-driven student engagement, including the development of emotionally intelligent AI tutors, AI-driven metaverse education, and interdisciplinary AI literacy programs. It concludes with strategic recommendations for universities, emphasizing the importance of ethical AI governance, faculty training, and a student-centered approach to AI integration.
By the end of this chapter, it is evident that AI is not merely a tool for automation but a transformative force in higher education. The challenge ahead is not whether AI should be part of student engagement, but rather how institutions can integrate AI ethically and effectively to create a more inclusive, engaging, and personalized learning experience.

Scenario planning is a strategic method employed to visualize and prepare for potential future developments, especially in the context of education and learning. As the landscape of learning continues to evolve due to technological advancements, societal changes, and global challenges, understanding the potential trajectories of future learners becomes paramount. This paper explores various scenarios for future learners, considering factors such as demographic shifts, technological integration, educational accessibility, and the impact of artificial intelligence. By analyzing these scenarios, educators, policymakers, and institutions can better anticipate the needs and characteristics of upcoming generations of learners. The study advocates for adaptive educational frameworks that embrace flexibility, inclusivity, and innovation. Ultimately, scenario planning serves as a vital tool in shaping proactive strategies that empower future learners to thrive in a rapidly changing world.

In today’s rapidly evolving digital landscape, experiential learning has become a crucial strategy for fostering student engagement and bridging the gap between theoretical knowledge and real-world application. As traditional teaching methods struggle to maintain student motivation and participation, universities are embracing experiential learning to create immersive, student-centered educational experiences. By leveraging digital tools and emerging technologies, educators can provide dynamic, hands-on learning opportunities that enhance critical thinking, problem-solving, and collaboration. This paper explores the role of experiential learning in the digital era, grounded in key educational theories such as Kolb’s Experiential Learning Cycle and Vygotsky’s Social Constructivism. It examines how technology-enhanced pedagogies, such as project-based learning, virtual internships, AI-driven adaptive learning, and digital simulations, are transforming student engagement. Through case studies from disciplines like engineering, business, health sciences, and technology, the chapter demonstrates how institutions worldwide are successfully integrating experiential learning to improve both academic performance and student retention. A central theme of this paper is the role of digital tools in supporting experiential learning. Innovations like AI, virtual and augmented reality, gamification, and learning analytics are reshaping the learning experience. These technologies provide immersive opportunities for students, personalize feedback, and allow them to take ownership of their learning journeys. By integrating these tools, educators can create more engaging environments that foster critical thinking and deeper learning. However, implementing experiential learning in the digital age presents several challenges. Issues such as digital accessibility, varying levels of digital literacy, faculty adaptation, and complex assessment models must be addressed to ensure equitable and impactful learning experiences. This paper examines these barriers while offering best practices for integrating experiential learning into hybrid and online models, emphasizing the need for faculty development and institutional support. Finally, the paper explores methods for assessing and measuring the impact of experiential learning on student engagement. By utilizing qualitative and quantitative data, such as surveys, learning analytics, and direct feedback, institutions can refine their strategies and improve the effectiveness of experiential learning initiatives. Looking ahead, the paper discusses the future of experiential learning in the context of digital transformation, highlighting its role in preparing students for a rapidly changing, technology-driven world and ensuring lifelong learning and career readiness.

The integration of generative artificial intelligence (Gen-AI) into higher education marks a transformative shift, offering both promising opportunities and complex challenges for student engagement and academic performance. This chapter presents a comprehensive analysis of how Gen-AI influences these critical educational dimensions, with a particular focus on the moderating roles of ethical considerations—specifically, data privacy and algorithmic bias.
Gen-AI demonstrates efficiency in generating personalized learning pathways and delivering immediate feedback, thereby enhancing student motivation, participation, and autonomy. However, concerns persist regarding its potential to promote superficial learning strategies, diminish intrinsic motivation, and compromise critical thinking skills. Moreover, over-reliance on AI tools may undermine authentic academic engagement and assessment validity.
Existing research reiterates that meaningful academic outcomes hinge on the thoughtful pedagogical integration of Gen-AI and the redesign of assessment practices. Ethical considerations play a pivotal moderating role: concerns over data privacy directly affect student trust and engagement, while algorithmic bias risks exacerbating existing inequities among diverse learner groups. Clear institutional guidelines and ethical frameworks are therefore essential to mitigate these risks.
This chapter concludes by offering evidence-based pedagogical strategies and policy recommendations to responsibly leverage the benefits of Gen-AI while minimizing associated ethical concerns. Key recommendations include implementing comprehensive AI literacy programs, promoting equitable and inclusive AI practices, and developing transparent institutional policies. This synthesis provides actionable insights for educators, institutions, and policymakers, advocating for a strategic and ethically grounded approach to Gen-AI integration to enrich educational practice and safeguard equitable learning outcomes.

The transformation of higher education in the digital age requires more than superficial integration of technology—it demands a strategic and systemic shift in how institutions engage students, make decisions, and operate efficiently. Artificial Intelligence (AI) stands at the center of this transformation, offering unprecedented opportunities to reimagine education systems to be more responsive, student-centered, and data-driven. This chapter explores how AI can be effectively harnessed to foster student engagement and drive institutional excellence, offering practical insights from initiatives implemented at Abu Dhabi University. Today’s learners are digital natives who expect personalized, flexible, and technology-enhanced educational experiences. Traditional models of instruction, assessment, and engagement often fall short in meeting these expectations. At the same time, universities are under increasing pressure to improve operational efficiency, ensure academic quality, and demonstrate their value to both students and society. AI offers powerful tools to meet these dual imperatives—improving student outcomes while enhancing institutional effectiveness.
This chapter introduces a strategic framework that places AI at the core of institutional innovation. It outlines the role of AI in: • Personalizing learning experiences through adaptive content delivery and intelligent tutoring systems. • Using predictive analytics to identify at-risk students and recommend timely interventions. • Automating repetitive academic and administrative tasks to increase institutional agility. • Enabling data-driven decision-making through real-time dashboards and AI-generated insights. • Enhancing co-curricular engagement through intelligent platforms that track student development across graduate attributes.
The chapter describes Abu Dhabi University’s use of AI through ADUConnect, a no-code platform that streamlines student engagement, links activities to graduate attributes, and automates event, survey, and peer support management—empowering staff and students to scale initiatives independently.
Another case highlights the digitization of faculty evaluation and curriculum development, where AI-enabled tools simplify the mapping of course learning outcomes, streamline program approvals, and enhance transparency in performance reviews. These innovations reduce administrative burden, improve consistency, and make institutional processes more resilient and scalable.
The use of predictive analytics to identify and support at-risk students, improving retention and satisfaction is emphasized, as is the role of no-code and low-code AI tools in enabling non-technical staff to create solutions, accelerating innovation and reducing reliance on external developers.
The discussion is grounded in the broader context of institutional change management, highlighting the importance of leadership support, capacity building, ethical AI practices, and inclusive stakeholder engagement. Special attention is given to ensuring data privacy, promoting fairness, and avoiding algorithmic bias in student-facing applications.
In the concluding section, the chapter offers a scalable AI roadmap for universities in the Arab world and beyond. The roadmap includes stages such as digital readiness assessment, pilot implementation, staff upskilling, cross-functional collaboration, and ongoing impact measurement. The objective is to support institutions in aligning their AI strategies with their mission, values, and long-term goals.

Robotics popularity surged over the last few years in many sectors including higher education. Advancement in robotics was supported by artificial intelligence. Intelligent chatbots became attractive to mentors and students due to their adaptability in different contexts and ability to provide vast amount of information in a short time. With advancements in natural language processing, chatbots reinforce knowledge, monitor students’ performance and ensure optimal learning experience. Nonetheless, despite the advantages chatbots offer, they are associated with many challenges relating to human-chatbot interaction, language, ethical and legal challenges. Chatbots based on narrow intelligence or agentic artificial intelligence can feed back within specific contexts depending on the data they have been trained with as well their algorithmic performance. Yet if based on artificial general intelligence, chatbots can predict new knowledge based on historically different knowledge. In other words, artificial general intelligence-based chatbots can self-teach and synthesize new knowledge in a way a human will do without needing the intervention of a human. Artificial general intelligence is driven by many artificial intelligent algorithms in addition to natural language processing, including deep learning, generative artificial intelligence, computer vision and robots. The proposed chapter will explore different types of chatbots of various intelligence levels and highlight the role of these chatbots in providing educational and skills training in the higher educational context. The chapter will consider the feasibility of use of such chatbots as well as the quality of knowledge from different subjects. Quality of knowledge will consider accuracy, authenticity and fairness of information provided by these chatbots. Moreover, it will assess both the consistency of information provided from different chatbots and the impact of chatbots on students’ skills and competencies. The chapter concludes by providing recommendations for institutions, educators and students on using intelligent chatbots in the higher education context.