Public Perceptions of Generative AI in Creative Industries: A Reddit-Based Text Mining Study

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.

Generative AI, since 2022 when ChatGPT was released, has been widely used in education. While bringing tremendous benefits to teachers and students, it also poses numerous challenges. Exploring the public's opinions and sentiment towards Generative AI in education will greatly promote their integration. Weibo, as the most important social media in China, offers a wealth of data to analyze public attitudes and viewpoints. In this study, 17,799 weibo posts was crawled for topic modelling and sentiment analysis by using BERTopic algorithm and the Large Language Model DeepSeek respectively. It's suggested that the public mainly focus on seven topics related with Generative AI in education, including AI-driven teaching innovation and digital transformation, applications of AI in academic research and the resulting changes in academic publishing, applications and experiences of AI in language education, risks, challenges of using GenAI in education and responses, feedback and complaints after using AI, the development of artificial intelligence major and related fields as well as the impact of AI on the job market and responses. Nearly half users embrace this new technology for its convenience, while some people demonstrate different attitudes. They worry such risks as the job loss, plagiarism, over-reliance and so on. At the end this study, several suggestions are put forward to tackle those negative sentiments to promote the the deep integration of Generative AI in education. Meanwhile, the limitation of this study was also proposed.

This paper discusses how generative AI (GenAI) is reshaping creative labor through three interrelated transformations: the automation and platformization of creative tasks, the redistribution of economic value and authorship, and the redefinition of creative skills and competencies.Rather than framing GenAI as either a threat or a tool, the analysis foregrounds its role as a structural force that reorganizes workflows, market dynamics, and professional norms.The paper highlights how creative work is increasingly governed by digital infrastructures and algorithmic interfaces, raising urgent questions about recognition, attribution, and sustainability.These developments are analyzed within the broader context of how computing can simultaneously exacerbate and alleviate crises, arguing that GenAI exposes deep institutional and economic tensions in creative industries.

Purpose:The primary aim of this study is to analyze the real-world adoption of generative AI within organizational contexts.This work aims to contribute to the broader understanding of how generative AI is reshaping business processes and driving innovation across industries.Design/Methodology/Approach: The study adopts a qualitative content analysis approach, grounded in a systematic review of peer-reviewed articles and grey literature published from 2022 to 2024.Articles were screened based on strict inclusion and exclusion criteria, emphasizing documented real-world applications of generative AI.Data extraction focused on identifying sector-specific use cases, challenges encountered during its integration, and the strategies employed by organizations to overcome these obstacles, as well as the organizational impacts of generative AI.Findings: The review identified six empirical studies documenting real-world implementations of generative AI across diverse sectors, including insurance, finance, creative industries, retail, and fact-checking.Generative AI is shown to enhance efficiency, streamline workflows, and support decision-making, while also fostering creative innovation.Challenges such as data reliability, legal ambiguities, and organizational readiness were commonly observed.Strategies to address these barriers include leveraging human-AI collaboration, ensuring regulatory compliance, and investing in cohesive organizational structures.Research Limitations/Implications: The research is constrained by the limited number of empirical studies available, reflecting the nascent stage of generative AI adoption.Additionally, the findings are subject to the rapid evolution of AI technologies, requiring ongoing updates through longitudinal research.Expanding future studies to include a broader geographic and sectoral scope is essential for generalizability.Practical Implications: The study provides actionable insights for organizations considering the adoption of generative AI.It highlights the importance of strategic planning, ethical data management, and fostering collaboration between human experts and AI systems.These findings are particularly relevant for small and medium-sized enterprises seeking to leverage AI to enhance efficiency and competitiveness.Social Implications: Generative AI democratizes access to advanced technology, enabling non-specialists to use AI tools.However, its rapid adoption demands robust regulation and stakeholder engagement from policymakers.Originality/Value: This study addresses a critical gap in the academic literature by focusing on the documented implementation of generative AI.

Enhancing Library Management Software (LMS) through work automation, more efficient cataloging, and customized user experiences are all made possible by generative AI. In order to better understand how generative AI fits into library systems, this paper looks at how it may provide accurate information, optimize operations, and provide tailored suggestions depending on user preferences. AI-driven systems reduce the human labor necessary for categorization and resource management while boosting user engagement through targeted offerings. Notwithstanding these advantages, problems like prejudice in content produced by AI and worries about data privacy still exist.The integration of generative AI into Library Management Software (LMS) offers substantial potential for improving the efficiency and personalization of library services. Generative AI into LMS opens new opportunities to optimize functionality, improve user experience, and expand libraries digital outreach. The report underlines the necessity of addressing ethical issues and guaranteeing data transparency. It also highlights the necessity of scalable AI solutions, especially for smaller or less well-funded libraries. Libraries must overcome these obstacles as generative AI develops further in order to guarantee fair, efficient, and transparent AI inclusion.

Objectives This study investigates the adoption and integration of Generative AI (GAI) technologies in daily life, focusing on factors that influence user behavior and attitudes. Method Using a mixed‐methods approach, we combined quantitative, qualitative, and semi‐experimental methodologies to capture the complexities of GAI tool usage. A total of 897 individuals completed an online survey with both closed and open‐ended questions; additionally, 38 students participated in a semi‐experimental stage to assess the impact of an online course, “GAI: From Theory to Practice.” Results Key findings underscore the role of personality traits such as openness and self‐efficacy in promoting GAI usage, mediated by behavioural perceptions and digital engagement patterns. In line with the UTAUT2 model, Performance and Effort Expectancy were the strongest predictors of GAI usage. Social media engagement and information overload management also emerged as predictors, illustrating the interplay between digital behaviours and GAI adoption. Qualitative findings revealed a nuanced understanding of user perceptions, emotions, and societal needs, uncovering themes of GAI's revolutionary potential, emotional ambivalence, and the demand for regulatory and educational frameworks. The experimental results demonstrated significant improvements in students' perceptions of GAI utility, self‐reported skill levels, and GAI usage across work, study, and personal contexts following the course. Conclusion This research advances understanding of GAI's societal integration and highlights the need for targeted training and regulatory policies to support responsible adoption and enhance digital literacy. It offers practical recommendations for educators, policymakers, and designers of generative AI tools.

The integration of generative AI in software organizations presents promising opportunities for automating and enhancing security activities; however, its practical impact on the security practices of software organizations and associated risks remains underexplored. This study investigates how generative AI can enhance software security practices through a multi-case study involving five software organizations. By conducting semi-structured interviews with developers and managers, we identified four security practices where generative AI can support threat assessment, security testing, operational management, and education & guidance. At the same time, we uncover sociotechnical risks that may impede adoption, including concerns about inadequate data management of AI, inaccurate output, and a lack of trust. Guided by the OWASP SAMM framework and rooted in the Gioia methodological approach, our findings are synthesized into a theoretical model of generative AI adoption impacting security practices. This model highlights both the perceived benefits and the risks organizations encounter. Our work provides insights for software organizations looking to adopt generative AI into security-related workflows and contributes a foundational understanding of how such tools are perceived in practice. The paper emphasizes the importance of balancing innovation with caution as software organizations increasingly incorporate generative AI within secure development.

AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory

The advent of Generative AI (Gen-AI) has precipitated an epistemological crisis in strategic management, challenging the foundational assumptions of enterprise digital transformation. Traditional frameworks, operating under a “Paradigm of Rational Mastery”, view technology as a predictable, manageable tool. This paper argues that the unique technical characteristics of Gen-AI—specifically its “Emergent Abilities” and “Black-Box Nature”—have given rise to a novel strategic condition termed “Foundational Uncertainty”, rendering these established models obsolete. This study critiques the prevalent “Two-Layer Model” of digital transformation, which is predicated on predictable technological advancement and planned “enablement”. In its place, this paper develops and proposes a “Dynamic-Adaptive Model” designed for the Gen-AI era. This new model reconceptualizes the firm-technology relationship as a co-evolutionary process. It posits that firms must shift from planned implementation to continuous “Adaptive Sensemaking”, an action-driven process of experimentation and learning to navigate the technology’s unpredictability. At the enterprise level, strategy must become an “Evolutionary Process” that emerges from this learning. The model’s central mechanism is a cyclical interplay of “Contention & Adaptation”—a generative tension between top-down managerial control (exploitation) and bottom-up, technology-driven learning (exploration) that propels organizational evolution. Consequently, the source of sustained competitive advantage shifts from possessing static, inimitable (VRIN) resources to cultivating a higher-order “Dynamic Adaptive Capability”—the organizational meta-capability for reconfiguring its own learning processes to thrive amidst unknowable change. The paper concludes by outlining the model’s significant theoretical contributions to the literature on digital transformation, Dynamic Capabilities, and the Resource-Based View, providing a new framework for navigating the Gen-AI frontier.

This project focuses on leveraging generative AI to analyse user reviews from the Play Store for diverse applications. By utilizing advanced large language models (LLMs), the system processes extensive user feedback to identify key trends, sentiments, and actionable insights. The AI analyses reviews to categorize overall user sentiment (positive, negative, or neutral), highlight recurring issues, and identify popular feature requests. The generative AI’s capabilities enable it to provide nuanced suggestions to developers, such as improving app functionality, addressing common complaints, and implementing features aligned with user preferences. Additionally, the system can generate concise summaries of user reactions, offering developers a clear understanding of their app's strengths and areas needing improvement. This data-driven approach enhances app development by prioritizing updates based on real user needs, improving user satisfaction, and fostering higher ratings on the Play Store. The integration of generative AI streamlines the review analysis process, ensuring actionable recommendations for sustained app success. Key Words: Generative AI, Large Language Models (LLMs), sentiment analysis, App improvement, review analysis 1.INTRODUCTION This project utilizes generative AI to analyze Play Store reviews across various applications, providing developers with actionable insights. By leveraging large language models (LLMs), the system processes user feedback to identify sentiment trends, highlight common issues, and suggest improvements. This approach enables developers to understand user reactions effectively and prioritize updates, ultimately enhancing app quality, user satisfaction, and ratings. The integration of AI streamlines review analysis, ensuring data-driven decisions for better app performance.

With the rapid integration of generative AI (GenAI) into higher education, concerns over cognitive offloading, overreliance, and diminished critical thinking underscore an urgent need to prioritize learning-to-learn (L2L) competencies. However, the lack of a clear and detailed conceptualization of L2L, a key 21st-century skill for lifelong learning, hinders interdisciplinary research and limits the development of informed, pedagogically sound AI applications. This paper presents a scoping review of L2L definitions within pedagogical and psychological literature, based on sources retrieved from ERIC, Scopus, and Web of Science. The review follows the PRISMA-ScR framework and identifies 21 relevant publications. We propose a novel three-layered framework organized by conceptual broadness: Dimensions (dispositions like cognitive and metacognitive skills), Processes (actionable activities such as self-regulation), and Tools (concrete strategies like retrieval practice), providing entry points for researchers from varying disciplines. We further highlight existing GenAI research efforts addressing these layers, outline current limitations, and propose directions for future exploration, with a focus on higher education. By unifying L2L theory with GenAI practice, this framework provides an actionable foundation for educational technologies in an AI-driven era. • Three-layer learning-to-learn (L2L) framework (Dimensions, Processes, Tools) to guide AI‑enhanced learning system design. • Synthesizes L2L definitions from 21 studies identified by a PRISMA-ScR review and offers actionable paradigms for teaching L2L, for example, using AI-supported systems. • Maps L2L components to GenAI application use cases in higher education. • Positions L2L as key to reducing GenAI overreliance and fostering learner agency.

Objectives This study aimed to analyze the perceptual changes experienced by pre-service teachers as they derived the final products of lesson plans using generative AI, and to explore educational implications for enhancing their Technological Pedagogical Content Knowledge (TPACK). Methods For this purpose, a preliminary training on the educational use of generative AI was conducted for 20 students in a teacher education course at A Education Graduate School in Seoul. Subsequently, the students explored applicable lesson design strategies for various subjects and reflected on these processes and experiences through mid-term assignments, which were then qualitatively analyzed based on the TPACK framework. Results Firstly, pre-service teachers recognized the educational utility of selecting and using generative AI tools aligned with educational objectives and confirmed their potential as tools for delivering subject content. Secondly, through experiencing the limitations of generative AI, they recognized the importance of carefully integrating technology and cultivating digital and AI literacy among teachers. It was found that their repeated and experimental approaches to lesson design contributed to the enhancement of problem-solving and critical thinking skills. Thirdly, pre-service teachers who were initially hesitant to use generative AI developed a positive perception and showed a willingness to use it more extensively in lesson design. Lastly, they were able to effectively integrate elements of generative AI and TPACK, developing new and innovative customized teaching methods and strategies suitable for each subject, and expanded their educational thinking and perspectives by experiencing lesson design cases from fellow pre-service teachers. Conclusions Teacher training institutions should develop and operate more practical curricula that include excellent examples of technology-integrated classes and practical exercises. Additionally, the curriculum should include content that simulates real issues encountered while using generative AI, and develop customized professional courses that facilitate the systematic integration of generative AI according to each major.

This article examines the transformative impact of Generative Artificial Intelligence (GenAI) on the financial services industry, with a particular focus on strategic implementation and operational transformation in the banking sector. Through a comprehensive analysis of current implementations and strategic initiatives, particularly in North American banks, the article explores how GenAI is revolutionizing traditional banking operations, from automated knowledge management to personalized customer services. The article investigates the multifaceted approach institutions are taking toward AI integration, including investments in infrastructure, talent development, and risk management protocols. The article reveals that successful AI implementation requires a balanced approach between innovation and risk mitigation, with institutions strategically deploying AI solutions across various operational domains. This article contributes to the growing body of literature on digital transformation in financial services by providing a structured framework for understanding the strategic implications of GenAI adoption and its role in shaping the future of banking operations. The article concludes by identifying critical success factors for AI implementation and suggesting future research directions in this rapidly evolving field.

This article develops a non-review, evidence-based analytical argument about how generative artificial intelligence is reshaping personal capability, organizational practice, and the architecture of society. Rather than merely cataloguing prior studies, the paper advances a socio-technical thesis: generative AI is becoming a general-purpose layer of cognitive infrastructure that reconfigures how people access knowledge, produce content, make decisions, coordinate work, and negotiate identity. The paper first clarifies the technical foundations that make contemporary generative systems scalable, multimodal, and interactive. It then examines how those foundations support concrete applications across education, healthcare, business, creative industries, software development, and public services. Building on this, the article identifies the distinctive characteristics of generative AI in use, including probabilistic generation, conversational interfaces, personalization, co-creation, tool integration, and continuous adaptation. The central contribution is a structured discussion of impacts at two levels: individual and societal. At the individual level, generative AI affects learning, productivity, self-expression, well-being, and employability while also introducing risks of overreliance, cognitive offloading, privacy loss, and unequal access. At the societal level, the technology alters media systems, labor markets, institutional trust, governance capacity, and the distribution of opportunity. The article concludes that the future significance of generative AI will depend less on raw model scale alone than on governance design, human oversight, sector-specific implementation, and the ability of societies to convert generative capacity into trustworthy public value.