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In this chapter, we discuss the merits and challenges of organic communication within an inclusive, iterative research design through our research project ‘Improving the quality of life of families with parents with intellectual disabilities (IDs) and their children (<12 years) by means of assistive robotics’. We will discuss the research process, its key steps and preliminary findings, as well as how inclusivity of participants was taken along throughout this process. The links between research design, designers and project participants are explored and reflected on. Our positions as researchers within the research process are also reflected upon. We will additionally address the implications of our research for the broader field of inclusive design for assistive robotics and the creative methodologies employed and tailored to the needs of families headed by parents with an ID and their children. In this, we keep a close eye on the difficulties such families face within the context of our research project. Lastly, we reflect on several key markers of collaboration within marginalised communities we encountered in our research.

This chapter discusses what special education means for students with visual impairments (that is, those who are blind or have low vision) including what is being done and how traditions are maintained. More specifically, this chapter explores the importance of advancing values for the diverse population of students with visual impairments, focusing on cultivation of supportive, inclusive, and collaborative educational environments that continue to stand the test of time. This chapter highlights the increasing heterogeneity of this population of students and specific instructional strategies to support the cultural and linguistic diversity of learners with visual impairments in today's classrooms. This chapter also discusses the significance of promoting core concepts that are rooted in a traditional and specialized instructional framework for students who are visually impaired.

In this chapter, we investigate the role of the Internet of Things (IoT) for a more sustainable future. The IoT is an umbrella term that refers to an interrelated network of devices connected to the internet. It also encompasses the technology that enables communication between these devices as well as between the devices and the cloud. The emergence of low-cost microprocessors, sensors and actuators, as well as access to high bandwidth internet connectivity, has led to the massive adoption of IoT systems in everyday life. IoT systems include connected vehicles, connected homes, smart cities, smart buildings, precision agriculture, among others. During the last decade, they have been impacting human activities in an unprecedented way. In essence, IoT technology contributes to the improvement of citizens' quality of life and companies' competitiveness. In doing so, IoT is also contributing to achieve the Sustainable Development Goals (SDGs) that were adopted by the United Nations in 2015 as an urgent call to action by all countries to eradicate poverty, tackle climate change and ensure that no one is left behind by 2030. The World Economic Forum (WEF) recognises that IoT is undeniably one of the major facilitators for responsible digital transformation, and one of its reports revealed that 84% of IoT deployments are presently addressing, or can potentially address the SDGs. IoT is closely interlinked with other emerging technologies such as Artificial Intelligence (AI) and Cloud Computing, for the delivery of enhanced and value-added services. In recent years, there has been a push from the IoT research and industry community together with international stakeholders, for supporting the deployment and adoption of IoT and AI technologies to overcome some of the major challenges facing mankind in terms of protecting the environment, fostering sustainable development, improving safety and enhancing the agriculture supply chain, among others.

The education of deaf and hard of hearing (d/DHH) students is largely dependent on the preferred mode of communication. Historically, the mode of communication for d/DHH students was determined by society rather than by students and families. This resulted in divisiveness between the Deaf culture and proponents of oral communication. The adoption of IDEA allowed family participation in the decision-making process. Advances in technology increased student access to sound, resulting in more educational placement options. Despite the positive changes, the complex nature of hearing loss and the wide variety in cultural considerations have made it difficult to determine the best approach to deaf education. Thus, educators and providers are left in a conundrum of which version of “traditional” deaf education is best for students.

Technology-enhanced learning (TEL), particularly in science, technology, engineering, arts, and math (STEAM), revolutionizes educational approaches by fostering active, transformative learning and expediting the learning process. TEL employs various tools like online courses, artificial intelligence (AI) technologies, virtual reality (VR), simulations, makerspaces, visual learning, and project-based learning, all contributing to accelerated learning in STEAM. A notable TEL innovation is the emergence of Large Language Models (LLMs) and AI chatbots, exemplified by the release of GPT-3 in December 2022. These tools utilize extensive parameters to generate natural language and perform tasks such as classification and prediction, thereby offering personalized and collaborative learning experiences essential for STEAM education. The generative pre-training transformer (GPT), a leading model in natural language processing (NLP), excels in generating human-like text and handling complex tasks like translation, summarization, and question answering. This chapter explores TEL environments that support transformative learning in STEAM, focusing on AI models. It reviews research on TEL’s impact on STEAM education, discussing the constructionism theory and emphasizing TEL’s role in creating engaging, student-centered learning experiences. However, challenges like technology access, instructor training, infrastructure, internet connectivity, and hardware resources are crucial. Additionally, the rise of AI brings ethical concerns regarding privacy, security, and potential biases in AI algorithms. Despite these hurdles, TEL’s potential to enhance STEAM learning experiences and accelerate the educational process is significant. By effectively implementing TEL strategies and leveraging LLMs and AI tools, educators can substantially improve learning outcomes in STEAM education.

The age of artificial intelligence (AI) is already upon us. The rapid development of AI tools is facilitating sustainable development and its corollary social good. For AI dedicated to social good to be impactful, it has to be human-centred, striving to achieve inclusiveness, sustainable livelihoods and community well-being. In short, it offers major opportunities to holistically enhance peoples' lives in diverse areas: education, health care, food security, disaster reduction, smart cities, etc. However, ethical, unbiased and ‘secure-by-design’ algorithms that power AI are crucial to building trust in this technology. Civil society's engagement can hopefully drive the features and values that should be embedded in AI.

This chapter focuses on the societal benefits that AI can deliver. Our initiatives and decisions of today will fashion the ‘Social Good’ AI applications of tomorrow. Sustainable Development Goals (SDGs) being addressed are 2–4 and 10–11.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.