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The rapid development of generative AI will have a significant impact on the design processes of emerging intelligent systems. Debates surrounding these topics of current interest must focus on collaborative human-AI decision-making, human- computer interaction, and the related improvements to design and process science. This collection of selected papers provides a focused overview of what role AI plays in the future of design and process of system development with perspectives on making it more relevant and beneficial for our society. It covers such topics as large language models, generative AI, Intelligenticism, communication dynamics repre- sentation, feature selection, smart design, and social goodness and demonstrates the general applicability of AI to various problems in transdisciplinary domains. Yu Chen, Heather Macias, Gabriel Granco, Yunfei Hou, and Frank A. Gomez discuss how AI will impact education and open opportunities to educate the next generation of students from various disciplines to be capable of practical AI skills while having the awareness and confidence to innovatively contribute to their communities. They present a special project called AI for Social Good (AI4SG) that aims to teach AI through the lens of social good to students of multiple disciplines. The impact of AI4SG is illustrated by deploying the special curriculum among 514 undergraduate students in management information systems, geography, and computer science at three universities and evidenced by the report showing increased level of interest and curiosity in learning AI among the students, appreci- ation for project-based learning, and development of learning space within and beyond the classroom. Lurong Pan, Frank M. Skidmore, Serkan Güldal, and Murat M. Tanik bring into light another aspect of communication dynamics by proposing a new theoretical framework and delving into the inherent role of energy within the proposed frame- work. This draws inspiration from Shannon’s communication theory and explores the centrality of energy in varying computational models, from classical mechanics to quantum mechanics. In it, the confluence of information and energy revealed that information transfer necessitates energy interactions. Morteza Zangeneh Soroush, Md. Sakib Ullah Sourav, and Yong Zeng present the concept of creativity supported by AI as an effective tool to complex cognitive processes. Creativity assessment, human-AI co-creation, and team creativity enhancement are presented as examples of AI applications to creativity with short- comings and open questions. Sang Suh and Swetha Reddy Pathuri introduce an interactive chatbot called CSACbot (Computer Science as Career bot) developed using neural networks, sentence transformers, and PyTorch. This chatbot serves as an innovative solution to provide comprehensive information about the field of computer science, guiding students and parents in making informed decisions about courses, majors, and potential careers. The CSACbot aims to bridge the information gap, empowering students to embark on successful academic journeys in computer science. Cristiane Chaves Gattaz, Fuad Gattaz Sobrinho, Joaquim Roberto Neves Cam- pos, Oscar Ivan Palma Pacheco, and Quymã da S. Viana introduce an AI framework for a Business Value-Centric Living Laboratory (BVLL), leveraging Semantic Modeling for Big Data Analytics, emphasizing the contextual nature of data analy- sis, and recognizing the importance of AI technologies such as machine learning and natural language processing in unlocking business potential sustainability. The PRIMETHOD methodology is presented with P3Tech Architecture Model show- casing how AI integration revolutionizes process management, value realization, and operational excellence in line with ESG and SDG principles. Michael P. Papazoglou, Bernd J. Krämer, and Amal Elgammal present a proven digital twin-driven manufacturing approach—called manufacturing blueprints. This includes a novel manufacturing knowledge model and a programming approach to rethink and redesign the whole product customization and development process. Manufacturing blueprints promises to realize the vision of smart manufacturing by fully digitizing all physical assets and processes, connecting factory sites and plants to customize outputs, and allocating resources to provide a logical shift between the digital and physical realms of product customization. Ray A. Gonzalez and Julia Rayz investigate the ability of transformer encoder models to discern and interpret the distinct meanings of compositional compound nouns, crucial for tasks such as semantic analysis and information retrieval. They uncover insights into the models’ capabilities to comprehend the semantic depth of these expressions by conducting a systematic comparison of similarity scores among compound nouns and exploring the effects of modifier and head-noun manipulation. They highlight the potential of these models to advance our understanding and processing of natural language, promising significant improvements in search tech- nologies and the broader domain of NLP. Raghav Vadhera and Manfred Huber propose an approach to a network optimi- zation policy on an abstract problem and architecture embedding those transfers across problems and thus permitting the optimization of networks for new problems without excessive additional training in a reinforcement learning framework. Geetanjali Bihani and Julia Rayz provide a concise survey of relevant research on large language models (LLMs) resorting to shortcuts when performing tasks, creat- ing an illusion of enhanced performance while lacking generalizability in their decision rules, and presenting challenges in accurately assessing natural language understanding in LLMs. They urge more research efforts to be put toward deepening the comprehension of shortcut learning, contributing to the development of more robust language models, and raising the standards of NLU evaluation in real-world scenarios. Preface ix Francis Joseph Costello and Kung Chang Lee propose the novel philosophical concept of “Intelligenticism” as a response to the recent impressive developments in AI and the proliferation of large language models. They propose and define that Intelligenticism consist of four philosophical components such as Anthroposia, Telopolarity, Virtuodymica, and Temporal Bifusion, delving into the effects of AI’s widespread adoption, specifically as they pertain to IS/IT study. A conceptual framework that accounts for the unique challenges and issues of deploying and supervising intelligent devices that communicate with humans is also addressed in the study. Clement Ting, Patrick Then, and Man Lau introduce an automated Feature Selection before Propositionalization (FSbP) and compare its characteristics with traditional feature selection techniques. They provide a use case for FSbP in KDD for oil drilling data, where FSbP is vital for timely project completion, discussing the implementation of FSbP algorithms for the oil drilling use case, areas for improve- ment, and limitations of FSbP. Overall, this book is a source of inspiration for everyone in the academia and research sectors working on the vision of creating a strong, fruitful, and creative community of design and process science for social good through evolving and generative AI.