Generative AI in the workplace: how employee experiences influence work outcomes?

With the growing proliferation of generative AI, discussions about the societal implications of AI, including opportunities and risks, have intensified. Ultimately, the success of initiatives to integrate (generative) AI into news production and dissemination will depend on the concerns, trust, and willingness of citizens to accept new AI-driven solutions. This study explores attitudes toward the use of AI in journalism, perceptions of generative AI, and how these factors influence trust in and credibility of information. Using a survey on a representative sample of the Dutch population (N = 1478), we analyze perceived benefits and concerns about AI and explore individual acts of resistance against the application of AI in journalism (e.g., unwillingness to pay for news that AI produces). With this study, we extend previous research on attitudes towards AI by also considering general attitudes towards generative AI, individuals’ risk perceptions towards generative AI, and policy support regarding regulating AI. More importantly, this study also investigates individual follow-up actions in the form of acts of resistance against the use of AI in journalism. The findings of this paper are particularly significant due to the rapid growth of generative AI, its integration into the news cycle, and international policy developments.

BackgroundArtificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medico-legal implications, integration into healthcare service delivery and workforce utilisation. Also, there is not a clear pathway to implement and integrate generative AI in healthcare delivery.MethodsThis article aims to provide a comprehensive overview of the use of generative AI in healthcare, focusing on the utility of the technology in healthcare and its translational application highlighting the need for careful planning, execution and management of expectations in adopting generative AI in clinical medicine. Key considerations include factors such as data privacy, security and the irreplaceable role of clinicians’ expertise. Frameworks like the technology acceptance model (TAM) and the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) model are considered to promote responsible integration. These frameworks allow anticipating and proactively addressing barriers to adoption, facilitating stakeholder participation and responsibly transitioning care systems to harness generative AI’s potential.ResultsGenerative AI has the potential to transform healthcare through automated systems, enhanced clinical decision-making and democratization of expertise with diagnostic support tools providing timely, personalized suggestions. Generative AI applications across billing, diagnosis, treatment and research can also make healthcare delivery more efficient, equitable and effective. However, integration of generative AI necessitates meticulous change management and risk mitigation strategies. Technological capabilities alone cannot shift complex care ecosystems overnight; rather, structured adoption programs grounded in implementation science are imperative.ConclusionsIt is strongly argued in this article that generative AI can usher in tremendous healthcare progress, if introduced responsibly. Strategic adoption based on implementation science, incremental deployment and balanced messaging around opportunities versus limitations helps promote safe, ethical generative AI integration. Extensive real-world piloting and iteration aligned to clinical priorities should drive development. With conscientious governance centred on human wellbeing over technological novelty, generative AI can enhance accessibility, affordability and quality of care. As these models continue advancing rapidly, ongoing reassessment and transparent communication around their strengths and weaknesses remain vital to restoring trust, realizing positive potential and, most importantly, improving patient outcomes.

This paper reports preliminary findings from an ongoing, campus wide research project on effective methods for generative AI applicability in pursuit of effective and engaging teaching and learning activities. Generative AI has had a tremendous adoption rate since the public release of ChatGPT 3.5 on November 30th 2022. This has necessitated that educators and administrators consider the potential opportunities and threats usage of generative AI by students and faculty may have on higher education. Recognizing the inevitability of generative AI, the researchers have proposed a university-wide research project to ascertain the changes in faculty and students perspectives when using generative AI The research project is two-fold. First, a longitudinal survey has been developed to address research questions about usage and perceptions of generative AI change over time. The second prong of this research project focuses on the implementation of new and continuing generative AI professional development workshops. These “AI Institutes” are targeted educational opportunities to provide faculty, staff, and students with hands-on experiences that model appropriate ways to teach and learn with generative AI tools. Workshops change based on audience needs, but will be designed to support such processes as introductory and advanced lessons on building learning activities which engage students with generative AI, administrative shortcuts, best practices for writing, and our university’s AI policy and principles. The longitudinal survey, thus, allows the research team to gauge changes in perspectives as the “AI Institutes'' are deployed and widespread adoption of generative AI tools become more mainstream. This paper reports on the first year of this research project, including one survey and one AI Institute. This research on integrating generative AI technologies into teaching and learning has important implications for the field of networked learning. As the paper explores, rapid advances in AI are changing how students and faculty interact with content and each other. Findings from the longitudinal survey and AI Institutes could provide insights into how to thoughtfully leverage these emerging tools to enhance connections, dialogue, collaboration, and co-creation of knowledge within digital learning networks. While further research is needed, this project takes an important first step in assessing faculty and student perceptions that can inform appropriate AI integration. Lessons learned could guide other institutions exploring the potentials and pitfalls of weaving generative AI into networked learning ecosystems.

This research paper explores the transformative potential of generative AI in the context of document processing within large financial organizations, with a particular focus on fraud detection. As financial institutions increasingly rely on vast amounts of documentation for operations ranging from customer onboarding to compliance, the inefficiencies and limitations of traditional manual processing methods become glaringly apparent. These legacy systems are not only time-consuming and prone to human error but also struggle with scalability, a critical requirement in today’s fast-paced financial environment. Moreover, manual systems and traditional Optical Character Recognition (OCR) engines often lack the necessary accuracy and contextual understanding to reliably process complex financial documents and detect fraudulent activities. While OCR technology has automated certain aspects of document processing, its inherent limitations in accuracy, particularly in dealing with degraded documents or complex layouts, and its inability to interpret context, significantly impede its effectiveness in high-stakes financial applications. Furthermore, OCR’s limited capability in detecting subtle indicators of fraud leaves financial organizations vulnerable to increasingly sophisticated fraudulent schemes. Generative AI emerges as a revolutionary solution to these challenges by enhancing the accuracy, scalability, and security of document processing systems. Unlike traditional OCR, generative AI models are designed to understand and interpret the context of documents, thereby significantly improving the accuracy of text recognition, even in complex scenarios. These AI models, trained on vast datasets, are capable of processing large volumes of documents in parallel, making them ideally suited for the high-speed, high-volume environments characteristic of financial institutions. Additionally, generative AI incorporates advanced algorithms that enhance fraud detection capabilities by analyzing patterns, detecting anomalies, and cross-referencing data across multiple documents. This approach not only improves the detection of fraudulent activities but also reduces the likelihood of false positives, thereby enhancing the overall reliability of the system. The paper further delves into the practical applications of generative AI in various critical areas within financial organizations. Key applications include Know Your Customer (KYC) compliance, where AI streamlines the processing and verification of customer documents, thereby ensuring both compliance with regulatory requirements and the authenticity of the information provided. In loan processing, generative AI accelerates the analysis of loan applications, providing real-time risk assessments that enable faster decision-making. Additionally, the technology is applied in invoice and payment processing, where it automates and verifies transactions, reducing errors and ensuring the timely execution of financial operations. In the realm of contract analysis, generative AI facilitates the extraction and interpretation of key terms and clauses, enabling more effective contract negotiation and management. Beyond its practical applications, the paper also addresses the continuous learning capabilities of generative AI models, which allow them to evolve and adapt to new data and document types over time. This feature is particularly crucial in the financial sector, where the types of documents and the nature of fraudulent activities are continually changing. The continuous learning aspect of generative AI ensures that the systems remain up-to-date and effective, even as new challenges and document types emerge. The research also highlights the comparative analysis between traditional OCR-based systems and AI-powered systems, demonstrating the superior performance, efficiency, and scalability of the latter. Moreover, the paper discusses the challenges associated with the implementation of generative AI in financial document processing. These include technical challenges such as the integration of AI systems with existing IT infrastructure, as well as regulatory and compliance issues that arise when deploying AI technologies in the highly regulated financial sector. Despite these challenges, the paper argues that the long-term benefits of adopting generative AI, including improved accuracy, enhanced fraud detection, and greater operational efficiency, far outweigh the initial hurdles. The research also considers the future of generative AI in financial document processing, suggesting that as the technology continues to advance, its applications and benefits will expand even further. Future research opportunities are identified, particularly in the areas of improving the efficiency and scalability of AI models, enhancing their ability to handle increasingly complex document types, and developing more sophisticated fraud detection algorithms. The paper concludes with a discussion on the potential long-term impact of generative AI on the financial industry, arguing that it will play a crucial role in shaping the future of financial operations by providing more accurate, scalable, and secure document processing solutions. This paper makes a significant contribution to the existing body of knowledge on the application of AI in financial services, particularly in the area of document processing and fraud detection. By providing a detailed analysis of the challenges faced by financial organizations and demonstrating how generative AI can address these challenges, the research offers valuable insights for both academic researchers and practitioners in the field. The findings presented in this paper have important implications for the future of document processing in financial organizations, suggesting that the adoption of generative AI will be essential for maintaining operational efficiency, accuracy, and security in an increasingly complex and fast-paced financial environment. In summary, this research not only highlights the transformative potential of generative AI in financial document processing but also provides a roadmap for its successful implementation in large financial organizations, with a particular emphasis on enhancing fraud detection capabilities.

Wilfredo De Jesus-Rojas, MD Ponce Health Sciences University Scientific Journal August 14, 2024 Dear Editor-in-Chief, As former ESL (English as a Second Language) students who have pursued science, technology, engineering and mathematics (STEM) fields and current researchers and instructors, we intimately understand the challenges faced by students adapting not just to a new language, but to the complex context in which that language is used in academic settings. Our experiences have given us unique insights into the difficulties ESL students encounter in higher education, particularly in STEM disciplines. Research aligns with our personal observations. ESL students often struggle with academic language proficiency, which can severely impact their ability to understand complex materials and engage in academic discourse [1]. These students are also more likely to face higher attrition rates [2], with studies indicating that they are more likely to experience academic probation or drop out compared to their native English-speaking peers [3]. ESL students often encounter significant obstacles in accessing and understanding academic resources that are primarily available in English, which can impede their ability to fully engage with the material. To illustrate the challenges and potential solutions, we would like to share an experience from Nelson's academic journey that led us to appreciate the potential of generative AI in ESL students' education. During his graduate studies, Nelson was enrolled in a rigorous Real Analysis course. Despite his best efforts, he found himself struggling to grasp the material. In a meeting with his then mentor, Prof. Doug Moupasiri, Nelson confessed his difficulties. Prof. Moupasiri asked a simple yet profound question: “What other books on the subject have you read?'' When Nelson admitted that he had only been using the assigned textbook, Prof. Moupasiri encouraged him to explore other authors' works. This advice—to seek out different perspectives—was a turning point in Nelson's academic career. By finding an author whose style resonated with him, he was able to understand concepts that had previously eluded him. This experience made us realize that sometimes, the issue is not with the subject itself but with how it is presented. This is where we see a tremendous opportunity for generative Al tools to support students, particularly ESL students, in their learning journey. ESL students often benefit from different learning approaches, and findings show that they have positive perceptions of AI-based learning tools, appreciating their personalized learning paths and time-saving advantages [4]. As evidenced in recent studies [5], AI technologies are already being successfully used in medical education to provide real-time feedback on quizzes, assist in anatomy learning, and support the recognition and diagnosis of medical images. Additionally, a study performed in a medical school in Puerto Rico showed that integrating a course aimed to bridge the gaps in AI knowledge among participants resulted in more positive perceptions of AI. However, it also revealed a lack of practical experience with AI applications, emphasizing the need for better integration of AI into educational programs [6]. Initiatives aimed specifically at Hispanic students are demonstrating the value of incorporating AI literacy into their education, helping them critically evaluate and effectively use AI technologies across different contexts [7]. By equipping students with essential AI competencies, these programs foster not only academic success but also readiness for AI-rich environments at home and in the workplace. We believe extending such support to ESL students can enhance equitable access to education by providing adaptive and personalized learning paths that cater to specific language needs. Just as different authors can present the same subject in varying ways, generative AI can offer students alternative explanations, analogies, and examples that align more closely with their individual learning styles. This ability to reshape content makes generative AI particularly powerful for students at institutions in Puerto Rico and elsewhere, allowing them to bridge gaps in understanding through tailored explanations in their native language or more accessible rephrasing, ultimately making challenging subjects more comprehensible. Generative AI is not just a tool for information retrieval but a companion in the learning process, helping students navigate complex subjects with a personalized approach that traditional methods may not always provide. The greatest value of generative AI as a tutor lies in its ability to customize learning experiences, tailoring them to fit the context and background of each student. As we continue to integrate AI into the educational landscape, it is our hope that we can leverage these tools to not only enhance learning but also to empower students to overcome the challenges that come with language barriers and different learning styles. Just as Nelson's mentor's advice reshaped his educational path, we believe encouraging the correct use of generative AI can play a similar role for many students, directly impacting academic outcomes. We urge educators and administrators to consider the potential of generative AI as a powerful tool for addressing the unique challenges faced by ESL students and those from diverse backgrounds. By embracing this technology, we can create a more inclusive and effective learning environment that supports all students in reaching their full potential. Sincerely, Nelson Colón Vargas, PhD and Marcos J. Ramos-Benitez, PhD

ABSTRACTThe widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce's experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry‐specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI's transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

Association between Generative AI self-efficacy and Generative AI acceptance: The mediating role of Generative AI trust and the moderating role of Generative AI risk perception.

Generative AI is reshaping the enterprise technology landscape, offering intelligent automation, insight generation, and contextual understanding capabilities that redefine how businesses handle data. Enterprise data management (EDM) - once constrained by rigid architectures, manual processing, and fragmented governance - can now evolve into a dynamic, self-improving ecosystem through the integration of generative AI. With organizations generating petabytes of data from operations, customer interactions, supply chains, and IoT devices, the need for scalable and intelligent data handling systems has never been greater. Generative AI models, including large language models (LLMs) and multimodal transformers, provide new tools for data ingestion, cleansing, integration, transformation, synthesis, and summarization. By applying generative AI to enterprise data workflows, companies can enhance metadata enrichment, automate data cataloging, improve data lineage tracking, and simplify data governance. These capabilities increase data discoverability, trust, and compliance—core principles of modern data management. Additionally, generative AI supports natural language querying, automates report writing, and generates synthetic data for training and simulation, boosting data availability and operational speed. While generative AI brings immense promise, it also raises concerns around hallucination, model transparency, data privacy, and regulatory compliance. Ensuring responsible AI adoption requires rigorous validation, bias mitigation, and alignment with existing data governance policies. Nonetheless, enterprises that embrace generative AI can unlock superior decision-making, improve productivity, and democratize data access across technical and non-technical users. This white paper explores the opportunities, challenges, architectural considerations, and best practices for embedding generative AI into enterprise data management. Through industry examples and forward- looking analysis, it offers a roadmap for transforming data operations and maximizing enterprise intelligence in the era of AI. Keywords: Generative AI, Enterprise Data Management, LLMs, Data Governance, Metadata, Data Cataloging, Synthetic Data, Data Lineage, Natural Language Processing, Responsible AI

Aim: This study investigates the application of generative AI in strategic business planning, with a particular focus on its potential to enhance operational efficiency and economic competitiveness in businesses organizations. Problem Statement: Before the advent of generative AI, businesses relied on traditional methods for routine tasks, business planning, and decision-making. These methods were often associated with significant challenges that hindered overall business efficiency. Significance of the Study: The application of generative AI plays a vital role in strategic planning and decision making of businesses. Recently, generative AI has transformed from a revolutionary concept to a transformative dynamism across various industries. This technology has shown incomparable competences in all ways. Methodology: This review article is based on an analysis of information gathered from high-impact journal publications. Discussion: This study critically examines the role of AI in strategic business planning and the techniques required for its effective implementation. Generative AI enhances competitiveness by facilitating product development and innovation, optimizing sales and marketing strategies, and enabling data-driven decision-making. However, several challenges must be addressed, including legal and ethical considerations, implementation and integration hurdles, and the need for continuous adaptation and learning. The future of generative AI in strategic business decisions is expected to include advancements in AI technologies, increased collaboration between AI and human intelligence, industry-specific AI applications, and the development of ethical AI governance frameworks. Businesses that integrate AI technologies—such as operational optimization, personalized customer interactions, and predictive analytics—stand to achieve significant improvements in customer satisfaction, innovation, and overall efficiency. Conclusion: The application of generative AI has demonstrated a positive impact on strategic business planning and has proven essential for businesses striving to remain competitive in dynamic markets. However, addressing the associated challenges is imperative to ensure the successful implementation and integration of AI into business strategies for sustainable growth and decision-making.

The integration of AI, especially Generative AI (GenAI), is producing a significant change in the ever-changing higher education scene. This chapter investigates how GenAI is transforming teaching, learning, and academic literacy. Academic literacy facilitators must now negotiate a complex landscape that includes conventional materials, digital resources, and AI-enhanced texts. They train scholars in GenAI tools and pioneer creative teaching methodologies. This chapter provides GenAI ontology to help guide you through this revolutionary journey. It prepares facilitators and students to utilize GenAI successfully by promoting specialized teaching techniques and individualized literacy evaluations. In conclusion, this chapter discusses GenAI's potential to innovate, improve access, and boost intellectual prowess in higher education.

Aim/Purpose: This study investigated how undergraduate and graduate students in a teacher education program perceived, used, and ethically reflected upon generative AI tools. The problem is the unclear state of preservice teachers’ understanding and use of generative AI in educational settings, which this study aimed to explain. Background: Given generative AI’s growing presence in education, this study addressed a gap in understanding by examining preservice teachers’ perceptions, experiences, and ethical concerns, particularly in relation to self-regulated learning. Methodology: A convergent parallel mixed-methods design was used to gather quantitative and qualitative data from 73 students enrolled in a teacher education program at the University of Nevada, Las Vegas. Descriptive statistics, correlations, and thematic analysis were used for data analysis. Contribution: This study contributes to the emerging literature by providing insights into preservice teachers’ usage patterns of generative AI, the relationship between AI use and self-regulatory skills, and a detailed examination of ethical concerns, informing effective AI integration into teacher education programs. Findings: Text-based generative AI was the most familiar tool among participants. The primary reasons for the use included editing papers, searching for new ideas, and understanding course material. A significant positive correlation was identified between information search with the help of generative AI and resource management skill. Key themes identified by thematic analysis included perceived benefits for student engagement and creativity, AI’s potential as a writing assistant, and ethical concerns about plagiarism and misuse. Overall, preservice teachers demonstrated surface-level use and a general lack of in-depth knowledge about AI integration. Recommendations for Practitioners: Teacher educators should provide explicit training in generative AI, including practical guidelines and clear ethical frameworks to foster effective and responsible integration into classroom practice. Recommendation for Researchers: Future researchers should develop validated measurement instruments specifically designed for studying generative AI perceptions and usage and explore longitudinal impacts as preservice teachers transition into professional teaching roles. Impact on Society: By guiding preservice teachers toward informed and ethical use of generative AI, this study supports broader societal goals of promoting digital literacy, ethical technology integration, and enhanced learning experiences for future generations. Future Research: Subsequent research should further explore reasons behind the limited adoption of generative AI among preservice teachers, examine the long-term impacts of AI use on pedagogical practices, and investigate structured frameworks for promoting deeper AI integration within the teaching and learning domain.

PurposeMost recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.Design/methodology/approachFindings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.Findings1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovationResearch limitations/implicationsWe are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.Practical implicationsBusiness application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.Social implicationsTo alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.Originality/valueThis research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

This article provides a critical synthesis and analysis of the research on the application of generative AI (GenAI) in second language (L2) writing. It conceptualizes GenAI literacy, synthesizes the research on written feedback, establishes a framework for prompt engineering, critiques the research examining the validity of GenAI ratings in writing assessment, and summarizes empirical evidence on the differences between GenAI and human writing. Specifically, the following findings and arguments are presented and discussed. GenAI literacy consists of four components pertaining to users’ competence and knowledge of GenAI basics, effective use, output evaluation, and ethics. The research on written feedback shows that teacher feedback focuses more on content, while GenAI feedback focuses more on organization. This research also suggests a need for criteria-based feedback and feedback evaluation. Prompt engineering is discussed along three dimensions: input, task, and output, followed by snapshots of prompts used in feedback research. The studies on writing assessment reveal that GenAI ratings are more consistent with human ratings when GenAI is trained using a large number of scored essays and when the rating criteria are well-defined. Comparisons of GenAI and human writing demonstrate that GenAI writing is more formal, academic, and impersonal, while human writing is more personal, creative, and linguistically accessible. This article concludes by making sense of the research findings, identifying future directions, and proposing three principles that may guide the research, practice, and theory construction for GenAI: individualization, domain-specificity, and writer agency.

This research looks at the potential effects of generative artificial intelligence AI on the country's media landscape. Given their pervasiveness, it aims to reveal how AI-powered technologies in media content creation, distribution, and personalisation contribute to the overall process of national progress. Using well-designed questionnaires, the study quantitatively collects data from media professionals, techies, and communication scholars in large cities throughout China. Using statistical tools such as structural equation modelling and regression analysis, one investigated the interplay between the rate of modernisation, the effects of national development, and AI-driven media innovation. Media indices of generative AI demonstrate a clear positive correlation with the effect of modernism and national development programs. As China strives to digitally change its communication infrastructure and increase its cultural influence, technological prowess, and media production, generative AI is playing an increasingly crucial role. This study shows that AI in media may lead to more dynamic stories, practical audience participation, and worldwide outreach, all thanks to modernist techniques. There is no part of this that does not contribute to the advancement of national development goals. The results provide policymakers, media outlets, and AI developers with valuable information for formulating strategies to integrate AI with sustainable development objectives. Via an experimental interaction between generative AI and national development perceived via a modernist lens, this study provides a framework for future research on new media technologies and national change. The discussion of the societal potential presented by AI may now begin.

As generative AI rapidly gains prominence across society, its application and perception are undergoing significant shifts. Even the judiciary, typically slow to adopt advanced technologies, is actively embracing generative AI, signaling a change in perception and the technology's maturity. This suggests that institutional changes for integrating generative AI into civil dispute resolution are likely. For a rational transition, a precise understanding of generative AI's unique characteristics is crucial, along with a preliminary review of essential foundational elements to properly reflect or limit these characteristics. Generative AI possesses advanced characteristics such as the potential for continuous improvement through learning and refinement, the ability for easy and free communication through multimodality, and the capacity for integrated thinking through adaptability and creativity. These advanced characteristics form the basis for utilizing generative AI in civil dispute resolution. However, generative AI also carries inherent limitations like hallucinations, bias, opacity, and personal information exposure. These limitations are critical in the conflict-driven field of civil dispute resolution and must be mitigated or overcome. To effectively utilize generative AI in civil dispute resolution, the institutional framework should be designed to enhance its advanced features while mitigating or overcoming its limitations. This framework should primarily align with the current technological landscape, but also be adaptable to future advancements. Key considerations include: 1. Continuous and Stable Data Acquisition & Timely Refinement: Ensuring a steady supply of high-quality data related to civil dispute resolution, alongside timely adjustments. 2. Court Involvement in Data Acquisition: The judiciary's involvement is necessary for securing high-quality civil dispute resolution data. 3. Court-Led Refinement: Timely adjustments should be spearheaded by the courts. 4. Establishment of a Specialized Closed System: A closed system specifically tailored for civil dispute resolution is required. 5. Court-Led Conciliation or Judicial Settlement as Most Effective Types: At present, the most effective applications of generative AI are in court-led conciliation or judicial settlements. 6. Judges' and Judicial Members' Technical Competence: Judges and other judicial personnel need to acquire technical proficiency. 7. Recognizing Generative AI as a Future Collaborator: Generative AI should be viewed as a future collaborator in civil dispute resolution processes.