The inclusion of usability heuristics into the metaverse is aimed at solving the unique issues raised by virtual reality (VR), augmented reality (AR), and mixed reality (MR) environments. This research points out the usability challenges of metaverse user interfaces (UIs), such as information overloading, complex navigation, and the need for intuitive control mechanisms in these immersive spaces. By adapting the existing usability models to suit the metaverse context, this study presents a detailed list of heuristics and sub-heuristics that are designed to improve the overall usability of metaverse UIs. These heuristics are essential when it comes to creating user-friendly, inclusive, and captivating virtual environments (VEs) that take care of the needs of three-dimensional interactions, social dynamics demands, and integration with digital–physical worlds. It should be noted that these heuristics have to keep up with new technological advancements, as well as changing expectations from users, hence ensuring a positive user experience (UX) within the metaverse.

Artificial Intelligence (AI) technologies are promised to improve digital services and automate tasks. However, there are still significant barriers to ensuring that AI technologies are accessible and usable by a broad range of users. As AI solutions proliferate across mainstream systems and applications, design-based approaches that explicitly bring in inclusive and human-centric values have become critical. This paper provides a concerted look at user-centered design at the intersection of AI, accessibility, and usability, proposing a framework that cuts across technological, social, and regulatory challenges. Contributions include identifying existing work and current literature gaps, key research questions, and a methodology to explore how to optimize AI systems for the widest possible range of users. We anchor our recommendations with a use-inspired case of an AI-driven public transportation assistant for individuals with diverse physical and cognitive abilities to demonstrate how our framework could benefit real-world applications. On the basis of existing standards and theoretical insights, this paper argues that the design process should be proactive, iterative, and implemented with the participation of multiple stakeholders. In their design of AI systems, this is meant to make the systems adaptive to users, rather than users being adaptive to the AI systems, thus revealing that “AI for all” can indeed be a realistic and realizable paradigm.

Generative Artificial Intelligence (GenAI) has transformed various sectors with its power to create human-like texts, images, and multimedia content. However, with all this power and creativity in content production, real-time generation and creativity come significant risks, including adversarial attacks, data poisoning, misinformation campaigns, and theft of intellectual property. This chapter takes a different approach by discussing security concerns regarding GenAI through its core technologies—Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based Large Language Models (LLMs)—so they may be used maliciously.

Business Intelligence (BI) has been acknowledged as a major driver in the paradigm of digital transformation, which is befallen by multitudes of complexities. Though the potential value that Business Intelligence can add to an organization is well acknowledged and respected, properly implemented and effectively utilized systems fail to exist within organizations for them to leverage such qualities into a sustainable competitive advantage. This chapter, therefore, takes a shot at this challenge by proposing an integrated framework for aligning BI capabilities with initiatives and strategic objectives of digital transformation. The technological, organizational, and strategic aspects of BI implementation will reveal those critical success factors that foster competitive advantage within environments undergoing digital transformation. Results show that firms getting an edge through BI show st͏rɒng alignment between BI skills and digital change plans, putting in place better data rules, and building thinking cultures around analysis.

This chapter introduces the adaptive defense intelligence framework (ADIF), short for the Adaptive Defense Intelligence Framework, a radically new initiative in the field of autonomous cybersecurity systems. ADIF overcomes glaring limitations present in existing security solutions through innovation in four different major spheres: hierarchical reinforcement learning architecture, a contextual analysis engine that drastically reduces false positives, federated knowledge distribution that maintains privacy, and a self-verification feedback loop. Financial, healthcare, and manufacturing operational dashboards showed remarkable improvements in detection rates, response times, and operational continuity during active threats. Though faced with startup challenges in terms of initial training requirements and computational intensiveness, this marks the shift from reactive to proactive security postures, offering the capability of continuous self-learning and further evolving the system with ever-growing threats.

Despite the increased technological development, the Jordanian industrial sector has faced major challenges in utilizing data. This chapter presents an agile business intelligence framework that suits the industrial environment of Jordan and concretely addresses the pivotal inadequacies concerning the ecosystems of data, infrastructures, skills, and organizational culture. The framework merges new technologies with local restrictions using a stepwise approach and specific elements for zones like manufacturing, pharmaceuticals, and textiles. Implementation guidelines can lead to improvements in operational efficiency, market responsiveness, and competitive advantage. The methodology harmonizes global best practices with regional limitations and offers practical paths for organizations at different levels of digital maturity to implement these practices. This study helps to know localized BI strategies in developing economies and offers a sustainable digital transformation blueprint for the industrial landscape of Jordan.