http://hdl.handle.net/10453/35199 2026-10-15T03:48:02Z http://hdl.handle.net/10453/197209 Title: Explaining Hallucination of Artificial Intelligence in Data-Driven Decision-Making Authors: Kabir, ZS Abstract: Artificial intelligence transforms a paradigm shift in delivering data-driven innovative knowledge, where hallucinated information can influence users' decision-making. It demands exploring the effects of hallucinations towards the trustworthiness and ethics of AI in decision-making. Thus, this chapter aims to explain the implications of hallucinations across data-driven decision-making. The chapter conceptualizes a framework for explaining a process flow of mitigating AI-driven hallucinations that engages critical flaws in promoting innovative and strategic decision making for professional practices. Theoretically, the framework explains the effects of hallucinations and how it impacts decision making, following multiple theories and models. We present a metatheoretical framework to study the hallucinations of LLMs-based AI, which will allow researchers and practitioners to capture a multidisciplinary theoretical lens to investigate the complexities of AI-driven hallucinations in the data-driven innovative decision-making process. 2026-11-20T00:00:00Z http://hdl.handle.net/10453/197208 Title: Innovative Decision Making Through Artificial Intelligence in a Connected Healthcare System Authors: Kabir, ZS Abstract: Artificial intelligence (AI) has transformed the healthcare industry by improving diagnosis, recordkeeping, and predictive analysis. To provide an in-depth picture of artificial intelligence-enabled healthcare technology, this chapter centers on the emergence of AI capabilities in medical applications. This chapter develops an AIpowered innovative decision-making support framework that explains sequential steps to get an optimum level of healthcare outcomes through data input, perception analysis, decision intelligence, cognitive reasoning, explainable decision, and feedback loop. By leveraging innovative decision making and advanced artificial intelligence techniques, the chapter seeks to generate actionable and valuable insights that can improve patient outcomes with the least possible resources like health professionals, accommodation, and medical instruments. The chapter reveals considerable importance for hospital systems transitioning from reactive care to proactive that engages in innovative data-driven decision-making in a connected healthcare system. 2026-11-20T00:00:00Z http://hdl.handle.net/10453/197207 Title: Cultural Influences on AI-Driven Decision-Making in Digital Commerce Systems Authors: Shao, C; Kang, K Abstract: This chapter explores how cultural diversity shapes consumer interactions with AI-driven digital commerce systems. While AI technologies revolutionize online shopping through personalized recommendations and intelligent assistants, their effectiveness varies significantly across different cultural backgrounds. This chapter proposes a theoretical framework examining how cultural values influence trust, acceptance, and usage patterns of AI commerce systems. This research introduces the Cultural-Adaptive Decision-Making (CADM) framework to guide the development of culturally-sensitive AI commerce systems. This framework provides theoretical design principles for creating adaptive interfaces that respond to cultural preferences while maintaining system efficiency. The study offers conceptual insights for global commerce companies seeking to optimize AI implementations across multicultural markets, ultimately enhancing user experience and business performance through AI design. This work provides a new perspective and possibilities for AI-driven commerce innovation. 2026-11-20T00:00:00Z http://hdl.handle.net/10453/197206 Title: Co-designing culturally responsive simulation-based learning: AI, First Peoples’ knowledges, and the implications for social work field education Authors: Bennett, B; Loban, R; Beetson, S; Evans, K; Dalziel, MV; Jefferies, G; Harris, S Abstract: This chapter explores the role of artificial intelligence (AI) in the teaching and learning of cultural responsiveness to Australian social work students. The aim of the project is to provide dynamic learning environments that facilitate co-designing culturally responsive social work practices with First Peoples and rural and remote communities in Australia. This encompasses the co-creation of simulated learning experiences as part of social work field education placements. The following critical questions guided this chapter: How are cultural understandings constructed by and embedded in AI technologies? What practice implications might arise at the intersection of social work education founded in settler educational contexts? What First Nations knowledges should be included in AI-generated learning? Most importantly, given that humans have traditionally been the primary source of creating and directing teaching and learning experiences (human-in-loop) in simulations, can AI-generated learning appropriately represent First Nations knowledges? 2026-01-01T00:00:00Z