With the development of artificial intelligence (AI), which makes it possible for robots to carry out jobs that were previously only possible for humans, information technology has rapidly changed on a worldwide scale. Natural language processing-powered tools like virtual assistants serve as examples of this change. Simultaneously, generative AI (GenAI) has become a potent force in artistic creation, posing difficult legal issues with regard to intellectual property, especially copyright. The "output problem," or whether AI-generated works are eligible for copyright protection, is a major concern. Despite being created by humans, AI's ability to be creative on its own defies conventional frameworks that exclusively acknowledge human authorship. This raises questions about who owns the rights: the user, the programmer, or neither? When AI systems use pre-existing copyrighted content in their creation processes, the problem becomes much more complex. WIPO and other international organizations are actively investigating these issues in order to create appropriate regulatory strategies. In order to evaluate the burden GenAI places on copyright law, this article engages with international legal discourse and judicial viewpoints. It draws attention to the shortcomings of existing theories on originality and authorship as well as the more fundamental normative worry that widespread algorithmic replication could weaken artistic originality. In the end, it makes the case for a fair structure that encourages creativity while maintaining originality.

The subject of the research is the phenomenon of the "artificial correlate" of folk tales – a text generated by a large language model that imitates the stylistic and plot framework of the folkloric original. The authors thoroughly examine the lexical, morphological, and syntactic features of authentic folk tales and their generated counterparts. Special attention is given to the analysis of compositional, ritual-mythological, and semiotic characteristics of the folkloric fairy tale text and the artificially generated tale. The aim of the work is to construct a theoretical model that allows for the parameterization of the "naturalness" of fairy tale discourse and to identify the ontological gaps between authentic folklore and its algorithmic imitation. The research material comprises authentic folk tales in Russian (from D.K. Zelinin's collection), as well as fairy tale texts generated by the following LLMs: GigaChat and AliceAI. The research methodology includes corpus analysis, conceptual modeling, methods of computational linguistics, as well as elements of quantitative and statistical analysis using the Python programming language. The scientific novelty of the research lies in the development of a multi-level model for assessing the naturalness of fairy tale narratives, which, unlike existing technical approaches, takes into account the mythological, ritual, and ethical constants of the genre. For the first time, a comprehensive analysis of the deficiencies of the generated text is conducted, not as technical errors, but as symptoms of the model's failure to comprehend the culturally significant code. The main conclusion is the demonstration that the artificial correlate largely successfully reproduces the superficial attributes of the genre ("the texture" of the tale), but exhibits significant deficiencies at the level of motif structure and ethical causality. It has been established that a key difference between a natural fairy tale and its digital counterpart is the presence of a rigid ritual-mythological foundation that ensures the teleology of the plot. The generated correlate, on the contrary, demonstrates a "fragmentary" nature: mechanical combinations of folkloric clichés without maintaining the internal logic of the fairy tale world, as well as euphemization of archaic motifs. The developed model of substantive parameters allows not only to diagnose the nature of the text but also raises questions about the limits of artificial intelligence's ability to reproduce culturally significant codes.

The convergence of Artificial Intelligence (AI) and nanotechnology represents a groundbreaking paradigm shift in science and engineering, opening new frontiers in medicine, materials science, environmental sustainability, and beyond. This review explores the integration of AI with nanotechnology by first introducing their fundamental principles. Nanotechnology, concerned with manipulating matter at the atomic and molecular scale, is significantly empowered by AI's ability to process vast datasets, recognize patterns, and predict outcomes. The synergy between the two fields is analysed through various applications, including optimization of nanodevice design, accelerated material discovery, prototyping, smart biomaterials, and environmentally sustainable nanotechnological solutions. AI-driven models improve efficiency in nanofabrication and enhance decision-making in real-time applications such as nano-enabled diagnostics and therapeutic delivery systems. However, despite the promise, the integration faces several challenges, including the scarcity of high-quality nanoscale data, computational limitations, the complexity of molecular interactions, and a lack of standardization across research platforms. Additionally, the black-box nature of many AI models poses interpretability concerns, especially in sensitive applications such as nanomedicine. Regulatory, ethical, and infrastructural hurdles further complicate implementation, particularly in low-resource settings. The review highlights future prospects, including AI-augmented autonomous laboratories, quantum machine learning for nanoscale modelling, and intelligent nanorobotics for personalized healthcare. With proper ethical oversight and continued interdisciplinary collaboration, the fusion of AI and nanotechnology promises to revolutionize multiple industries and drive a new era of intelligent, scalable, and sustainable technological advancement.

Human resource management (HRM) is one of the many industries being revolutionized by the quick development of Artificial Intelligence (AI). Personalized employee development is one of AI's most revolutionary uses in HRM. Traditional employee training and development programs frequently use a one-size-fits-all approach, which might not take into account each person's unique learning preferences, professional goals, or performance gaps. Nevertheless, these approaches frequently lacked real-time flexibility and customisation. AI integration provides a data-driven, scalable answer to these constraints, empowering HR managers to make well-informed decisions around succession planning, skill development, and employee growth. AI-driven solutions, on the other hand, provide customized learning opportunities, flexible training programs, and data-driven insights, allowing businesses to increase employee engagement and productivity. AI technologies are widely used in numerous aspects of professional development. By addressing skill gaps and providing clear promotion possibilities, AI's ability to evaluate internal career data and recommend personalized development plans has been demonstrated to increase employee engagement, satisfaction, and retention. To achieve fairness and trust, however, successful implementation requires ethical use, transparency, and alignment with human oversight. This study examines the impact of AI-centred career development platforms on employee engagement and explores the elements of AI systems that most significantly influence employee growth and productivity. The findings aim to provide insights for HR managers and organisational leaders on leveraging AI for strategic talent management and workforce engagement.

The rapid evolution of marketing strategies has been profoundly influenced by advancements in artificial intelligence (AI), machine learning (ML), and real-time analytics. This paper explores the role of next-generation marketing automation tools, focusing on AI-driven applications that offer competitive advantages for businesses. AI's ability to enhance customer engagement through personalized experiences, predictive analytics, and automated decision-making processes is transforming traditional marketing paradigms. The study highlights the intersection of AI and machine learning in driving efficiencies across various marketing segments, such as customer targeting, content personalization, and real-time campaign optimization. Moreover, the integration of AI with big data analytics is enabling businesses to forecast market trends, identify consumer behaviors, and improve customer retention strategies. By reviewing the existing literature and case studies from various industries, this research identifies the critical applications of AI in marketing automation, shedding light on the future implications of AI for digital marketing strategies. The findings emphasize the potential of AI to not only automate routine tasks but also to drive strategic insights that inform key marketing decisions. This paper concludes with a discussion on the ethical considerations and challenges in implementing AI technologies in marketing, offering recommendations for organizations looking to leverage AI for long-term growth and customer-centric strategies.

Artificial intelligence (AI) applications are being increasingly explored in pain medicine due to AI's ability to handle multidimensional data and analyze complex, nonlinear relationships. There is a need to identify and understand the advances that have been made in developing AI-based clinical prediction models in interventional pain medicine for spine disorders. Therefore, the purpose of this study is to conduct a systematic review of AI-based prediction models for clinical outcomes in interventional spine medicine with a focus on model field of application, performance, and generalizability. A systematic review evaluating AI-based clinical prediction models in interventional pain medicine for spine disorders was conducted using the PubMed/MEDLINE and Scopus databases in February of 2025. Articles meeting eligibility criteria had standardized data extracted and were assessed for their application, performance (primarily based on area under the receiver operating characteristic curve [AUROC] and accuracy), and generalizability (internal and/or external validation). A final total of nine studies were included in this systematic review. Of these nine, four of the studies were pertaining to epidural steroid injections and five of the studies were pertaining to spinal cord stimulators. Two studies (22.2%) out of nine achieved an excellent (> 0.90) AUROC or accuracy for their AI-based prediction models. One study (11.1%) externally validated their AI-based prediction model. AI-based clinical prediction models are limited to epidural steroid injections and spinal cord stimulators. Additionally, there is a need to improve model performance and generalizability through external validation prior to clinical translation.

Abstract An emerging literature suggests that artificial intelligence (AI) can greatly enhance autocrats' repressive capabilities. This paper argues that while AI presents a powerful new tool for authoritarian control, its effectiveness is constrained by the very repressive institutions it is designed to serve. This constraint stems from what I term the “authoritarian data problem”: citizens' strategic behavior under repression diminishes the amount of useful information in the data for training AI. The more repression there is, the less information exists in AI's training data, and the worse the AI performs. I illustrate this argument using an AI experiment and censorship data in China. I show that AI's accuracy in censorship decreases with increasing repression, especially during times of political crisis. I further show that this problem cannot be easily fixed with more data. Ironically, international data—especially data from less repressive settings—can help improve AI's ability to censor.

Artificial Intelligence (AI) has significantly reshaped the field of robotic surgery, heralding a transformative shift in contemporary surgical methodologies. Evolving from rudimentary mechanical aids to sophisticated AI-integrated systems, robotic surgery now operates as a high-precision, real-time, and data-adaptive domain. The incorporation of AI introduces cognitive intelligence into surgical robotics, empowering these systems to analyse intricate anatomical structures, identify anomalies, propose optimized surgical strategies, and, in certain instances, perform procedural steps semi-autonomously under clinical supervision. This global review investigates the development and current landscape of AI in robotic surgery, covering its technological foundations, core algorithms, clinical applications, and economic implications. Modern AI-driven surgical platforms utilize advanced technologies such as machine learning, deep learning, computer vision, and natural language processing to assist with decision-making, precision incision, intraoperative diagnostics, and personalized patient care. These intelligent systems facilitate pre- operative planning using comprehensive patient data, deliver real-time imaging and assistance during surgery, and predict post-operative outcomes. One of the most remarkable innovations lies in AI's ability to guide surgeons during procedures—enabling automated segmentation of tissues, recognition of surgical movements, and identification of anatomical landmarks with greater precision than human visual perception. Furthermore, AI contributes to skill evaluation, simulation- based training, and the reduction of intraoperative errors through proactive alerts. The global market for AI in surgery is experiencing rapid expansion, projected to grow from USD 6.4 billion in 2022 to approximately USD 25.2 billion by 2030. This growth is driven by the increasing demand for minimally invasive techniques, rising incidences of chronic diseases, an aging global population, and continuous technological advancement. Despite these promising trends, several challenges persist—including high implementation costs, complexities in data governance, ethical dilemmas, and limited infrastructure in under-resourced regions. In addition to technical and economic factors, ethical and psychological dimensions play a critical role in the discourse around AI in surgery, raising vital questions about autonomy, control, trust, and accountability. This review synthesizes insights from academic literature, clinical research, and global trends to offer a comprehensive perspective on the influence of AI in robotic surgery. It critically evaluates the profound benefits alongside the intricate challenges, while also drawing philosophical parallels between human cognition and artificial intelligence within life-critical medical contexts.

Frequent monitoring is essential for assessing the progression of adolescent idiopathic scoliosis (AIS), especially during years of growth. Standard practice involves radiographic measurement of Cobb angles every 4–6 months; however, this may not align with curve progression given unpredictable growth patterns. The emergence of clinical artificial intelligence (AI) tools, such as the Momentum SpineTM smartphone application, have been developed to address monitoring concerns. Video-imaging technology is used to measure extra-spinal, topographic markers that are correlated to the magnitude of spinal deformity. The purpose of the current study is 1) to validate the AI-generated Cobb angle produced via Momentum SpineTM and 2) to assess the AI's ability to track disease progression overtime. Eligible AIS patients (n=131) consented to partake in the study and underwent same-day radiographic imaging and AI scan at the baseline visit. Subsequent same-day AI scans were performed at any follow-up visits with radiographic imaging, with 17 patients having completed an additional follow-up visit thus far. Paired t-test was used to compare all same-day radiographic and AI-generated Cobb angles. Two-way ANOVA was used to assess the accuracy of AI-generated Cobb angle compared to radiographic Cobb angle overtime. Paired t-test results of all scans demonstrated that mean Momentum SpineTM main curve Cobb angle differed by an average of 6.72° compared to the radiographic measurement ((MAI = 26.8±12.7° vs. Mradiograph = 33.5±14.2°). Despite this difference being statistically significant, (−6.72°, 95% CI [−8.19°,−5.26°], t(127)=9.09, p < 0 .0001), it falls within the accepted range for inter-rater variability of ~7° between clinicians. In the population with follow-up, post-hoc analysis showed the agreement between AI-predicted Cobb angle and radiographic measurement improved to an average difference of 4.05° (AI:24.24±13.4° vs radiograph:28.29±14.0°, p=0.6788). Momentum SpineTM can estimate Cobb angles within the accepted range of inter-rater variability between clinicians when compared to same-day radiographic Cobb angles. Therefore, it can be used to detect and monitor AIS. The next step in this study is to evaluate at-home AI scans performed on a monthly basis and determine potential practice change by scheduling clinic visits when progression is detected, as well as eliminating planned visits when the curve is reported to be stable.

The integration of Artificial Intelligence (AI) into marketing and branding has catalyzed a paradigm shift in how Small and Medium-sized Enterprises (SMEs) and startups establish market presence, interact with consumers, and drive purchase intentions. This research paper presents a comprehensive bibliometric and thematic review of the literature published between 2014 and 2025, a critical decade that witnessed the evolution from rudimentary predictive analytics to advanced Generative AI (GenAI) ecosystems. Utilizing a curated dataset from Scopus and supplementary high-impact literature, this study investigates the dualistic nature of AI adoption in the SME sector: while AI tools democratize access to enterprise-grade branding capabilities – enabling hyper-personalization, automated content creation, and predictive market intelligence – they concurrently introduce profound challenges regarding consumer trust, brand authenticity, and ethical transparency. The analysis employs a mixed-methods approach, combining quantitative bibliometric mapping (analyzing publication trends, citation networks, and keyword co-occurrences) with a qualitative thematic synthesis. Key findings reveal that the literature has bifurcated into two distinct streams: a "techno-optimist" stream focusing on efficiency and adoption models like the Technology-Organization-Environment (TOE) framework, and a "critical-ethical" stream examining the deleterious effects of synthetic media, deepfakes, and "brand hate" on consumer-brand relationships. Specifically, the review highlights a "trust paradox" where overt disclosure of AI involvement in branding can diminish purchase intention for high-involvement goods, despite AI's ability to enhance functional service quality. Furthermore, the study identifies "Generative Engine Optimization" (GEO) as an emerging frontier for 2025, necessitating a strategic pivot from traditional SEO. The paper concludes by proposing a strategic resilience framework for SMEs, emphasizing "human-in-the-loop" governance to navigate the reputational minefields of the algorithmic age.

Imaging advantages have raised intracranial aneurysm (IA) detection rates but have also increased radiologists' workloads. Coupled with visual fatigue, this heavier burden heightens the risk of missed or erroneous diagnoses. Concurrently, artificial intelligence (AI) has shown great promise for analyzing medical images. This study aims to evaluate the diagnostic performance of AI software for IAs and to provide an initial, single-centre validation of its potential as a supportive tool in future deployment. Between January 2019 and September 2023, 452 patients with 544 IAs diagnosed by head and neck computed tomography angiography (CTA) who also underwent digital subtraction angiography (DSA) were included. The AI's ability to detect the presence, location, and size of IAs was recorded. Its results were compared with DSA, and the agreement between AI and radiologists in measuring IA size was evaluated. The AI software demonstrated a sensitivity of 88.97% (95% CI [0.861-0.963]) and an accuracy of 75.04% (95% CI [0.715-0.783]) for detecting IAs. Specifically, the accuracy and sensitivity of AI in detecting IAs that are smaller than three mm, between 3-5 mm, and larger than five mm are 58.46% (95% CI [0.462-0.698]) and 66.67% (95% CI [0.537-0.775]), 76.68% (95% CI [0.714-0.813]) and 88.93% (95% CI [0.843-0.924]), 77.10% (95% CI [0.720-0.816]) and 94.24% (95% CI [0.906-0.966]), respectively. There was good agreement between radiologists and DSA, between AI and DSA, and between radiologists and AI for identifying the location of IAs, with kappa values all greater than 0.75. The radiologists and AI also showed good consistency in measuring depth, width, height, and maximum diameter, with intraclass correlation coefficients (ICCs) all greater than 0.75, except for the neck width, which had an ICC of 0.492. The AI software performs well in terms of detecting IAs that are larger than three mm and shows good agreement with radiologists in localizing positions and extracting their morphometric parameters (except for neck width measurements). The AI software has proved to be a reliable adjunct for IA detection and measurement tasks.

Artificial intelligence (AI) is revolutionizing interventional pulmonology (IP) by enhancing diagnostics, procedural precision, and patient outcomes. AI-powered tools improve lung nodule detection, radiomics-based risk stratification, and bronchoscopic navigation. Machine learning (ML) algorithms aid in lung cancer screening by analyzing imaging data, reducing false positives, and improving early diagnosis. AI-driven robotic-assisted bronchoscopy enhances navigation and biopsy accuracy, particularly for peripheral lung lesions. Endobronchial ultrasound (EBUS) and cytopathology benefit from AI's ability to assess lymph node malignancy and optimize rapid on-site evaluation (ROSE). AI applications extend to phenotyping chronic obstructive pulmonary disease (COPD) and identifying candidates for bronchoscopic lung volume reduction (BLVR). Deep learning (DL) models analyze computed tomography (CT) imaging and spirometry data to optimize patient selection. AI-driven algorithms are also advancing pleural effusion detection, differentiation, and classification, supporting clinical decision-making. Education and research in IP are also transforming with AI-driven simulation, virtual reality, and automated assessment tools that enhance procedural training and competency evaluation. The integration of AI into clinical work and procedural training accelerates advancements while presenting challenges in ethical AI implementation, data security, and bias mitigation. As AI continues to evolve, its role in IP will expand, improving procedural efficiency, personalizing treatment plans, and optimizing patient selection for interventions. Future developments will focus on refining AI-driven predictive analytics, enhancing robotic-assisted procedures, and integrating AI seamlessly into clinical workflows. The responsible implementation of AI in IP holds the potential to transform patient care, reduce complications, and advance precision medicine.

Our study investigated the ability of artificial intelligence to differentiate eyelid lesions to support its potential use as a tool to better inform referrals to oculoplastic surgery specialists by other healthcare providers. Specifically, our study tested artificial intelligence's ability to distinguish benign chalazia from alternative eyelid masses that may require advanced subspecialized care with oculoplastic specialists. This retrospective case-control study included 206 photographs of diagnosed chalazia from 183 patients and 517 photographs from 486 patients with non-chalazia eyelid lesions to train and test a convolutional neural network (CNN). Network architectures including VGG-16, VGG-19, ResNet50, Xception, and MobileNetV2 were trained. Their performances were compared using the area under the curve (AUC) as the main outcome metric. Additionally, performances of CNN models were compared to those of frontline physicians. VGG-16 and VGG-19 architectures achieved meaningful performance when trained with photographs of chalazion and eyelid mass achieving AUCs of 0.797 and 0.703, respectively. Adjusting detection thresholding allowed the VGG-16 and VGG-19 models to achieve sensitivity of 93% and 98% in predicting eyelid mass, respectively. This was an improvement over classification by frontline physicians who achieved an accuracy of 61% and a sensitivity of 65% for mass detection. We showed that using a CNN trained with clinical external photographs could successfully distinguish a chalazion from an alternative eyelid mass, supporting its potential use as a tool for healthcare providers to assist in determining whether a mass requires oculoplastic referral for subspecialty care.

We propose, illustrate, and evaluate the use of artificial intelligence (AI) to advance rigorous hypothesis-driven scale validation. Using a qualitative approach, we found that AI provided useful suggestions for measures to be used as criteria in scale validation research. Using data and expert predictions previously used to validate nine scales/subscales, we evaluated AI's ability to produce precise, psychologically reasonable validity hypotheses. ChatGPT and Gemini produced hypotheses with "inter-trial consistency" similar to experts' "inter-rater consistency," and their hypotheses agreed strongly with experts' hypotheses. Importantly, their hypothesized validity correlations were roughly as accurate (in terms of corresponding with actual validity correlations) as were experts' hypotheses. Replicating across nine scales/subscales, results are encouraging regarding the use of AI to facilitate a precise hypothesis-driven approach to convergent and discriminant validity in a way that saves time with little-to-no cost in psychological or psychometric quality.

This study explores the transformative role of Artificial Intelligence (AI) in climate change mitigation, focusing on its potential to enhance predictive modeling for mitigation strategies and promote environmental justice. AI's ability to process vast amounts of data quickly and accurately enables improved predictive models that forecast land-use changes, climate impacts, and environmental risks. These models play a crucial role in identifying vulnerable areas, monitoring deforestation, tracking urban sprawl, and forecasting the effects of climate change. The study emphasizes how AI can inform more effective and targeted mitigation strategies across sectors such as agriculture, energy, and urban planning. Additionally, the study highlights AI's capacity to support environmental justice by providing marginalized and vulnerable communities with real-time data, empowering them to participate in climate-related decision-making processes. AI's role in identifying communities most at risk of environmental degradation ensures that mitigation efforts address social inequities and promote more inclusive and equitable outcomes. Despite these opportunities, challenges such as data accessibility, infrastructure limitations, and ethical concerns need to be addressed for AI to reach its full potential in climate change mitigation. The study concludes that AI, if deployed responsibly and inclusively, can significantly contribute to sustainable development, enhancing both climate resilience and social equity in the fight against climate change.

Abstract. There has been an increasing need for effective, sustainable, and scalable waste management systems due to the rapid increase in global waste generation. This comprehensive review intersects Artificial Intelligence (AI) with municipal solid waste management (MSWM) through the lens of 25 selected publications from the years 2018 to 2024. The review illustrates how AI has transformed waste forecasting, smart bin monitoring, route optimization, robotic waste sorting, and real-time decision making. In examining the core AI techniques machine learning, deep learning, computer vision, and hybrid models, the review places these techniques within the context of the waste life cycle—beginning with generation, through processing, to disposal. Moreover, it looks at integrated frameworks like SWM 4.0 where AI is combined with Industry 4.0 technologies, including the IoT, big data, and even blockchain. The results stress AI's ability to optimize operational activities, mitigate negative environmental effects, and facilitate concrete policy decisions. However, issues related to data quality, system incompatibility, and ethics pose challenges to realizing such opportunities. This review evaluates existing research on AI-based smart systems and sets forth a research agenda aimed at advancing circular economy objectives and fostering sustainable urban frameworks.

Artificial Intelligence (AI) is transforming the landscape of modern physics by enabling breakthroughs in both theoretical and experimental research. AI's ability to process vast amounts of data and perform complex simulations is revolutionizing how physicists tackle problems, from particle collisions in high-energy physics to the intricacies of quantum mechanics. Machine learning algorithms assist in discovering patterns within data, optimizing experimental setups, and even predicting phenomena that were previously beyond the scope of traditional methods. In cosmology, AI aids in analyzing astronomical data to uncover new insights about the universe's formation and evolution. Additionally, AI-driven automation enhances precision in experiments, reduces human error, and accelerates data analysis, enabling more efficient and accurate results. As AI continues to evolve, its integration into physics promises to uncover new dimensions of knowledge, driving the next wave of scientific discovery and shaping the future of physics research.

The advent of generative Artificial Intelligence (AI) was viewed as a threat to the information ecosystem due to generative AI's ability to create 'stories' that might easily be twisted as misinformation. Generative AI was alleged to be a high-risk tool for fact checkers, journalists, public officials, and others responsible for verifying and sharing correct information, increasing the possibility of widespread misinformation and disinformation in the society. However, as technology evolves, so does humans' comprehension of the new machines. There are many benefits that could be explored from generative AI, as the platform offers a plethora of potential applications. To explore the possibility, this research investigates the potential of AI to detect health misinformation. Focusing on COVID-19 misinformation in Indonesia, the study employing Qualitative Content Analysis (QCA) to examine the result of AI machines, namely Copilot, ChatGPT, and Gemini, by using specific prompts in two languages (Indonesia and English) which applied in two different times (August and September 2024). This study concludes that the aforementioned AI platforms are capable of detecting misinformation while providing supporting claims to substantiate their reasoning. However, while generative AI has the potential to be utilized as a tool for detecting misinformation, further improvements should be made to refine the output. Furthermore, due to the nature of generative AI learning through deep learning, users who wish to utilize generative AI platforms to debunk hoaxes have to perform additional research to complement their findings.

English Language Anxiety (ELA) remains a significant psychological barrier for learners in Indonesia, particularly in the context of performance-based assessments. This study investigates the potential of Artificial Intelligence (AI) as a tool to mitigate this anxiety, focusing on student perceptions and the relevance of specific AI features. Employing a quantitative survey design, data were collected from 90 high school students in Bandung using a questionnaire that integrated the Foreign Language Classroom Anxiety Scale (FLCAS) and the Technology Acceptance Model (TAM). Descriptive statistical analysis revealed that students experience high levels of performance anxiety, especially when speaking without preparation. Results indicate a strong positive perception of AI's ability to create a low-stress environment, primarily by reducing the fear of making mistakes (Mean=3.67). Concerning the most relevant AI features, the capability for L1 scaffolding—explaining concepts in Indonesian—was deemed most crucial (Mean=3.88), highlighting its role in alleviating cognitive anxiety. However, a significant concern regarding the transferability of AI-practiced skills to real human interaction was identified (Mean=3.56), moderating the overall perceived efficacy of AI for anxiety reduction. The study concludes that AI is a highly effective affective tool for safe practice but must be integrated within a hybrid pedagogical model that bridges the gap between AI-assisted learning and authentic social communication to fully address the multifaceted nature of foreign language anxiety.

This research explores how Artificial Intelligence (AI) might improve collection techniques in library science, with a focus on effectiveness, user involvement, and creative collection building. A structured online questionnaire with verified Likert-scale questions It was utilized to gather information from 384 participants using a quantitative research technique. Key concepts such AI-powered technology, user engagement, AI-driven recommendation systems, efficiency in library collection creation, and library staff efficiency are the focus of the inquiry. The representativeness of the sample is guarantee using a stratified random sampling procedure, and the analysis is bolstered by secondary data gathered from institutional records, government publications, and earlier studies. SPSS is used for statistical analysis in order to evaluate structural connections, validity, and dependability. The structures' good validity and reliability were confirmed by the results, indicating that they are appropriate for further study. Based on descriptive findings, consumers highly regard AI-based recommendation engines and advanced collection creation methods, whereas employees' efficiency is mostly perceived positively but remains to be improved. Testing hypotheses confirmed that AI is a major impact on user engagement and the growth of library collections. Notably, AI improves engagement both directly and indirectly via better staff performance, as seen by the mediation impact of staff efficiency. The correctness and resilience of the suggested framework are further confirmed by structural model fit indexes. All things considered, the results show that the implementation of AI is a key factor in improving library user engagement, strategic collection development, and operational efficiency. By offering empirical proof of AI's ability to transform collection tactics and change the role of library professionals, this research adds to the expanding conversation on next-generation library practices. The conclusions drawn have applications for academics, library administrators, and legislators who want to leverage AI-powered technology to provide sustainable and user-focused library services.