Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy

PurposeThe fusion of human intelligence with artificial intelligence is a promising approach for addressing complex challenges, giving rise to the concept of hybrid intelligence. However, principles for guiding the design and management of hybrid intelligence are lacking. This study addresses this gap by exploring the key characteristics of job positions necessary for effective hybrid intelligence. Drawing upon the job characteristic theory (JCT), we identify key job features relevant to hybrid intelligence work, elucidating the challenges and considerations inherent in this domain.Design/methodology/approachThe study conducted 23 semi-structured interviews with middle-level managers from ten service companies in China, exploring their experiences with human–AI collaboration within their organisations. The analysis employed both inductive and deductive methods. Inductive coding was used to identify emerging themes from the participants’ responses, while the deductive approach ensured that these themes aligned with the research questions and Hackman and Oldham’s JCT.FindingsThe results contribute to our understanding of the job positions required for successful hybrid intelligence implementation, offering both theoretical and practical perspectives on job design and JCT applications in the era of intelligent technologies.Originality/valueOur findings illuminate the critical job characteristics essential for integrating hybrid intelligence into organisational systems, highlighting the evolution of job roles and the implications for effective job design in the intelligent era. These insights inform the ongoing discourse on job design and adaptation in the face of rapidly evolving technological advancements, guiding organisations seeking to harness the full potential of hybrid intelligence.

To address the misalignment between generative AI’s restructuring of the interior design industry and its insufficient integration into pedagogical practices—and in response to the digital transformation demands of New Engineering and vocational education—this study examines four key issues in current teaching methods: the fragmented use of AI tools, disconnections in human–AI collaboration, an imbalance among technical, creative, and ethical competencies, and outdated evaluation mechanisms. Drawing on research in interdisciplinary collaboration and AI tool application, we propose strategies such as an integrated AI toolchain for the entire design process, interdisciplinary fusion, phased human–AI collaboration, three-dimensional capability development, and diversified quantitative assessment. These strategies are validated through case studies. The study also suggests future optimization pathways across four dimensions, including technological integration. By constructing an adaptive framework and providing actionable teaching solutions, this research aims to support the cultivation of interdisciplinary talents skilled in “AI + Interior Design.”

As quantum computing development advances closer toward achieving fault-tolerant error-corrected realisation, debates on applications, impacts, risks, and benefits of quantum computing are timely and due. While there is awareness of the potential power and complexity of quantum computers, there has been relatively little attention on the social impacts and ethical implications of this technology. In grappling with the social impacts of quantum computing, some stakeholders have turned to applied ethics, specifically (classical) computer and Artificial Intelligence (AI) ethics, for guidance. While computer and AI ethics are useful starting points for evaluating the ethical issues posed by quantum computing, uncritically applying existing sets of ethical principles risks overlooking how quantum computing differs from these technologies. We argue that borrowing of ethical principles and guidelines from AI and computing is inappropriate for several reasons: (1) quantum computing, classical computing, and AI are different technologies with significant material differences; (2) unlike AI and classical computers which have become established, quantum computing is an emerging technology, which has implications on the levels of accessibility and immediate impact in society; and (3) there are significant differences in the way AI and quantum computing have been developed and by whom. We also briefly summarise some of the unique risks and societ al impacts posed by quantum computing. We posit that these reasons support the argument for a new sub-field of quantum computing ethics, which would allow a relevant scholarship to develop and provide guidance as quantum computing technology continues to mature.

With the rapid development of Generative Artificial Intelligence (Generative AI) technology, its application in the field of education has brought innovative opportunities for oral English teaching. This paper systematically analyzes the core advantages of Generative AI in enhancing the effectiveness of oral English teaching from the dimensions of personalized teaching, situational creation, feedback mechanisms, resource expansion, and stimulation of learners’ motivation. It also designs a basic “human-AI collaboration” teaching process covering pre-class, in-class, and post-class stages. Meanwhile, the paper discusses the problems and challenges existing in the application of Generative AI in oral English teaching, thereby providing further insights and reflections for the research on Generative AI empowered oral English teaching.

Generative AI systems such as large language models exhibit jagged intelligence: they combine superhuman performance on some tasks with brittle, often opaque failures on others. Existing mental models, inherited from deterministic technologies and from human teamwork, mischaracterize these systems systematically. This theory-driven synthesis develops a conceptual _Triadic Framework_ for adaptive mental models in human-AI collaboration: First, the _System Layer_ synthesizes current evidence on probabilistic generation, opacity at scale, and rapid model drift, explaining why capability boundaries are uneven and moving. Second, the _Collaboration Layer_ analyzes how users configure prompting practices, division of labor, and handling preferences. Third, the _Metacognitive Layer_ examines how anthropomorphism, metaphors, and cognitive biases shape human interpretations of “intelligent” behavior. Building on this diagnosis, the paper hypothesizes seven adaptive practices that are designed to calibrate mental models, preserve human agency, and make hybrid intelligence more robust under jagged and shifting AI capabilities. Together, the paper reframes the challenge of generative AI from crafting better prompts to cultivating more adequate mental models of probabilistic counterparts.

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

Summary form only given. There has been substantial progress in both intelligent systems and robotics in recent years. While in the past robots were most successful in traditional industrial environments, new generations of hybrid intelligent robotic systems are being developed which focus on higher cognitive capabilities, including reasoning learning and language communication. In this paper we give an overview of learning neural robots from a perspective of integrative hybrid intelligent systems and illustrate some new developments including also examples under development in the Centre for Hybrid Intelligent Systems.

A healthcare system that is transformed by cutting-edge technology, where patient outcomes improve, clinical workflows become more efficient, and the overall quality of care reaches new heights, this is what Generative Artificial Intelligence is promising in healthcare solutions. Unlike traditional AI which analyzes and processes existing data, generative AI has the unique ability to create new content including creating synthetic medical images, crafting personalized treatment plans, and developing new drug therapies. The aim of this chapter is to explore Generative AI in healthcare aligning with human rights. The focus is on healthcare systems benefiting from this technology and provides a comprehensive overview of the current applications in healthcare. Generative AI alignment with human rights principles and standards is also discussed by critically examining its adherence to privacy, equity, and non-discrimination, and evaluating the ethical implications of AI in healthcare

AI has become a disruptive technology in healthcare that is using the breakthroughs in computer science to improve the fields of diagnostics, treatment planning, patient monitoring, and medical research. In this review, the author discusses the historical development of AI starting with the early rule-based systems about modern machine learning, deep learning, and natural language processing applications. The most important areas of healthcare use are medical imaging, predictive analytics, personalized medicine and robotic-assisted surgery. Issues of data quality, interpretability, ethical issues, and regulation are mentioned, and novel trends like federated learning, generative AI, and human-AI collaboration are stated, and AI has potential to enhance patient outcomes and healthcare efficiency.

Introduction. This study explores how generative AI, specifically ChatGPT, influences collaborative travel planning. Understanding its effect on tasks like trip planning reveals insights into human-AI collaboration, particularly how AI tools support decision-making and streamline information gathering. Method. Twenty participants (10 pairs) planned a 1-night, 2-day trip under two conditions: (1) using only Google, and (2) using Google with ChatGPT. This within-subject study measured completion time, satisfaction, and plan quality via questionnaires and observation to capture task performance, user behaviour, and collaboration dynamics. Analysis. Data was analysed using t-tests and Wilcoxon signed-rank tests to compare completion times, satisfaction, and plan quality. Analyses of conversation volume and ChatGPT logs provided insights into AI-assisted collaboration dynamics and interaction patterns. Results. No significant difference in task completion time was found. However, plans made with ChatGPT were more complete and aligned with requirements. Participants found information more easily with ChatGPT, but satisfaction levels remained similar, suggesting that easier information access did not translate to higher satisfaction. Conclusions. Generative AI improves collaborative search task quality but does not enhance efficiency or satisfaction. AI tools like ChatGPT are effective for providing structured information and are best used as complementary resources alongside traditional search engines in planning tasks.

In the Generative AI (GenAI) era, social researchers have explored different tools for different phases of research projects. Despite AI and GenAI being highly relevant topics in current literature, few researchers have addressed the question of how to explore GenAI tools based on human-AI collaboration to improve the quality of methodological literature reviews. Based on a real project as a sample, “a competence framework for social researchers integrating GenAI”, we propose a detailed analytical model that triangulates evidence from both quantitative and qualitative data analyses. A mixed-methods approach is looked at, with a focus on the qualitative dimension (quant-QUAL). The bibliometric analysis is added to the framework analysis. The steps we follow are: (1) bibliometric analysis of Scopus publications (434 with the primary string; 84 with the secondary string from 2021-2024); (2) qualitative analysis of 32 publications using inclusion/exclusion criteria to develop initial categories via Chain-of-Thought prompting with Gemini and ChatGPT; (3) indexing using webQDA; (4) pattern interpretation and categorisation using ChatGPT and webQDA; and (5) mapping and interpretation to identify patterns and connections. These results came from social researchers working together with AI, which helped figure out the pros and cons of using GenAI in different stages of the methodological literature review. During the construction of the model, the researchers identified as a limitation the fact that they did not collect data from all meetings so that their analysis could improve the framework presented.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

Generative Artificial Intelligence (Generative AI) is transforming how travelers plan trips, yet research has not fully examined dynamic interactions between travelers and generative AI or the co-creation of authenticity through these interactions. This exploratory qualitative study employs a retrospective think-aloud method to explore how travelers engage with generative AI. Grounded in the lenses of host-guest relations and authenticity, this study conceptualizes travel planning as a co-performance between the digital guest (the traveler) and the digital host (generative AI). Findings identify six AI roles (mentor, pathfinder, partner, acquaintance, butler, and implementer) shaped by power dynamics and encounter depth, and four authenticity pathways (co-creation, co-recovery, co-reduction, and co-destruction) across five pre-travel experience stages: triggering inspiration, shaping vision, gathering details, evaluating information, and making decisions. The study advances a relational and performative framework for understanding human-AI collaboration in tourism and offers practical insights for enhancing AI-assisted destination marketing and traveler experiences.

This chapter explores the transformative role of AI across various dimensions of healthcare, highlighting the interplay between analytical and generative AI in medical imaging, as well as the implications of generative AI in drug discovery and development. It reviews generative AI in the broader framework of P4 medicine—predictive, preventive, personalized, and participatory—and the potential of AI to improve health outcomes, while tackling the rising costs and increased demand for healthcare professionals in aging populations. The requirement for multimodal longitudinal datasets is examined, as well as the challenges around data sparsity and data bias, and the need for data equity. The chapter further evaluates inherent risks and the most relevant aspects of regulation relating to the use of AI in healthcare, personal data protection, and security. In conclusion, the chapter reviews the potential future impact of agentic AI, and technology convergence with robotics, zero-knowledge proofs and blockchain, and quantum computing.

The rapid development and availability of Generative AI (GenAI) tools, such as ChatGPT, offer new possibilities for academia, yet they also raise concerns for academic integrity. Chapelle's (2024) seminal work on GenAI’s implications serves as an impetus for this study, which explores teaching academic integrity in the context of AI use. Chapelle frames GenAI as an ‘inflection point’, calling for a re-evaluation of pedagogical strategies to support students in understanding its technological and ethical implications. This article presents findings from a mixed-methods study at a university in Cyprus, where ESL undergraduates in academic writing classes engaged with ChatGPT. Participants with B2/C1 English proficiency from diverse cultural backgrounds were assessed through pre- and post-intervention evaluations and qualitative interviews. A semester-long intervention incorporated activities to enhance academic writing skills and comprehension of ethical AI use, focusing on critical thinking, proper citation, and policy adherence. Findings indicate that students demonstrated increased AI knowledge and awareness of academic integrity. This study contributes to the discourse on integrating GenAI in academia, providing insights into effective instructional practices and addressing ethical considerations in academic writing. It offers guidance for educators and institutions aiming to integrate GenAI responsibly, ensuring academic integrity remains central in second language education.