Artificial Intelligence Training Research Articles

The Role Of AI In Strengthening Data Privacy For Cloud Banking

The rapid adoption of cloud banking has transformed the financial sector by enhancing efficiency, scalability, and accessibility. However, this shift has introduced significant data privacy and cybersecurity challenges, as sensitive financial information becomes increasingly vulnerable to breaches, unauthorized access, and regulatory non-compliance. Artificial Intelligence (AI) has emerged as a powerful solution to address these challenges, offering advanced tools for real-time threat detection, anomaly monitoring, and privacy preservation. This study systematically reviews 62 peer-reviewed articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to explore the role of AI in strengthening data privacy within cloud banking systems. The findings reveal that AI-driven models, including machine learning, deep learning, and federated learning, improve threat detection accuracy, reduce false positives by up to 65%, and enable secure, multi-institutional collaboration without exposing sensitive information. Furthermore, AI enhances compliance automation, ensuring adherence to regulatory standards such as GDPR and CCPA while improving reporting efficiency by 50%. Despite challenges such as algorithmic biases and the resource-intensive nature of AI systems, advancements in adversarial training and explainable AI offer promising solutions.

Determination of artificial intelligence literacy and attitudes towards artificial intelligence of teachers working with gifted students and examining them according to some variables

This research aimed to determine the level of artificial intelligence literacy and attitudes towards artificial intelligence of teachers working with gifted students and to examine the results according to some variables. The study was conducted with the participation of 107 science and art center (BİLSEM) teachers selected by convenience sampling method. Data were collected using the Artificial Intelligence Literacy Scale and the General Attitude Towards Artificial Intelligence Scale. The findings show that teachers generally have high levels of artificial intelligence literacy and attitudes towards artificial intelligence. In gender comparisons, it was found that male teachers had higher AI literacy and attitudes than female teachers. Other variables such as age, professional experience, working time in BİLSEM, education level and branch did not have a significant effect on artificial intelligence literacy and attitude. As a result, it was observed that the knowledge and attitudes of teachers working with gifted students towards artificial intelligence were generally positive, but there were gender differences. Therefore, it is recommended that teachers' access to artificial intelligence training and professional development opportunities should be increased and especially female teachers' knowledge and attitudes towards technology should be improved.

Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?

Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transforming diagnosis and treatment by applying deep learning algorithms, notably convolutional neural networks, which mimic human brain function to analyse two-dimensional (2D) and three-dimensional (3D) data. This article provides an overview of AI applications in endodontics, evaluating its use in 2D and 3D imaging and examining its role as a beneficial tool or potential challenge. Through a narrative review, the article explores AI's use in 2D and 3D imaging modalities, discusses their limitations and examines future directions in the field. AI significantly enhances endodontic practice by improving diagnostic accuracy, workflow efficiency, and treatment planning. In 2D imaging, AI excels at detecting periapical lesions on both periapical and panoramic radiographs, surpassing expert radiologists in accuracy, sensitivity and specificity. AI also accurately detects and classifies radiolucent lesions, such as radicular cysts and periapical granulomas, matching the precision of histopathology analysis. In 3D imaging, AI automates the segmentation of fine structures such as pulp chambers and root canals on cone-beam computed tomography scans, thereby supporting personalized treatment planning. However, a significant limitation highlighted in some studies is the reliance on in vitro or ex vivo datasets for training AI models. These datasets do not replicate the complexities of clinical environments, potentially compromising the reliability of AI applications in endodontics. Despite advancements, challenges remain in dataset variability, algorithm generalization, and ethical considerations such as data security and privacy. Addressing these is essential for integrating AI effectively into clinical practice and unlocking its transformative potential in endodontic care. Integrating radiomics with AI shows promise for enhancing diagnostic accuracy and predictive analytics, potentially enabling automated decision support systems to enhance treatment outcomes and patient care. Although AI enhances endodontic capabilities through advanced imaging analyses, addressing current limitations and fostering collaboration between AI developers and dental professionals are essential. These efforts will unlock AI's potential to achieve more predictable and personalized treatment outcomes in endodontics, ultimately benefiting both clinicians and patients alike.

Adoption of Artificial Intelligence in Public and Private Libraries of China: Determinants, Challenges, and Perceived Benefits

Artificial intelligence (AI) has much significance in different industries, including education. Due to increasing technological advancement, universities show their growing concerns about adopting several facilities linked with AI, specifically in their library management systems. Motivated by such an emerging trend, this research was conducted to examine AI adoption in both public and private libraries in China. The study explored the existing landscape of AI powered services in libraries, the perception of AI benefits, challenges related to AI adoption, and key determinants of such adoption for the targeted libraries. Using a questionnaire technique, the study collected data from different public and private libraries for which a sample of 154 respondents was finalized over the time span of 4.5 weeks. The analysis of the collected data shows that both the situations of launching and planning to launch different AI-related services were observed in Chinese libraries. Moreover, participants also confirmed several benefits of AI, including the enhancement of library operations by improving search accuracy, automating repetitive tasks, offering personalized resource recommendations, revealing usage trends through analytics, curating digital collections, improving cataloging, digitizing rare materials, and providing quick responses via chatbots, thereby allowing librarians to conduct their other more complex tasks. Additionally, the study confirmed several challenges associated with AI adoption. The results also revealed that along with other factors, the support from university administration, maturity and reliability of AI applications, and training and human resource management are positive and significant determinants of AI adoption. On the contrary, funding/cost associated with implementation and innovative services/alignment with technological trends reflected negative coefficients for AI adoption in Chinese libraries.

Evaluation of the effect of artificial intelligence training equipment in physical training of table tennis players from a biomechanical perspective

In table tennis competitions, the movement data of table tennis players plays an important role in daily training. In recent years, artificial intelligence technology has been widely used in sports competitions. With the development of artificial intelligence, how to use artificial intelligence to analyze sports video to obtain sports data of athletes has become more and more important. This paper is to use artificial intelligence to research and analyze the athletes in the table tennis competition, and then obtain the sports data of the athletes. This has important guiding significance for athletes and coaches in their daily training. Table tennis is a widely developed sport in China, and as China’s “national ball”, table tennis has always been in a leading position in the world table tennis competitions. It is widely loved by the public for its simple and easy access to sports facilities, low site requirements and sports methods. In this study, mathematical statistics and experimental methods were used. Combined with the application of the functional sports screen function test, 20 table tennis players from Shandong Sport University were selected as the test objects, and their sports performance was tested. The total score of 10 first-level athletes was 162 points, and the average score was 16.2 points. The total score of the 10 second-level athletes was 144 points, and the average score was 14.4 points. The performance of the first-level athletes was significantly higher than that of the second-level athletes. It showed that the first-level athletes were generally better than the second-level athletes in all aspects.

Enhancement of early proximal caries annotations in radiographs: introducing the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset

BackgroundProximal caries datasets for training artificial intelligence (AI) algorithms commonly include clinician-annotated radiographs. These conventional annotations are susceptible to observer variability, and early caries may be missed. Micro-computed tomography (CT), while not feasible in clinical applications, offers a more accurate imaging modality to support the creation of a reference-standard dataset for caries annotations. Herein, we present the Academic Center for Dentistry Amsterdam—Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset, which is the first dataset pairing dental radiographs and micro-CT scans to enable higher-quality annotations.MethodsThe ACTA-DIRECT dataset encompasses 179 paired micro-CT scans and radiographs of early proximal carious teeth, along with three types of annotations: conventional annotations on radiographs, micro-CT-assisted annotations on radiographs, and micro-CT annotations (reference standard). Three dentists independently annotated proximal caries on radiographs, both with and without micro-CT assistance, enabling determinations of interobserver agreement and diagnostic accuracy. To establish a reference standard, one dental radiologist annotated all caries on the related micro-CT scans.ResultsMicro-CT support improved interobserver agreement (Cohen’s Kappa), averaging 0.64 (95% confidence interval [CI]: 0.59–0.68) versus 0.46 (95% CI: 0.44–0.48) in its absence. Likewise, average sensitivity and specificity increased from 42% (95% CI: 34–51%) to 63% (95% CI: 54–71%) and from 92% (95% CI: 88–95%) to 95% (95% CI: 92–97%), respectively.ConclusionThe ACTA-DIRECT dataset offers high-quality images and annotations to support AI-based early caries diagnostics for training and validation. This study underscores the benefits of incorporating micro-CT scans in lesion assessments, providing enhanced precision and reliability.Graphical

Ethical Implications of AI-Driven AAC Systems: Ensuring Inclusivity and Equity in Assistive Technologies

This study investigates the ethical implications of Artificial Intelligence (AI) integration into Augmentative and Alternative Communication (AAC) systems, focusing on inclusivity, accessibility, and data privacy in assistive technologies. AI-driven AAC systems enhance communication for individuals with impairments by offering features like predictive text, speech recognition, and symbol-to-text translation. However, ethical challenges arise as these systems increasingly rely on sensitive user data, which could jeopardize privacy without strict safeguards. Furthermore, limitations in linguistic diversity within AI training data compromise inclusivity, potentially excluding users from varied cultural and linguistic backgrounds. Accessibility also remains an issue, as AI-driven AAC systems are often financially inaccessible for marginalized communities, exacerbating social inequities. Through a comprehensive analysis of current systems, including Proloquo2Go, Tobii Dynavox and Google’s Project Euphonia, this paper evaluates both the advancements and ethical shortcomings in AI-driven AAC technologies. The findings advocate for privacy-preserving AI practices, culturally diverse training datasets, and affordable AAC solutions to foster a more inclusive, accessible, and equitable digital communication environment.

Prawa autorskie wobec sztucznej inteligencji. Czy istnieje jeszcze szansa na ich skuteczną ochronę?

We have all been affected by the euphoria connected with AI, and we have all followed the on-going discussion about our future, supported by thinking machines, which can replace us in many activities, and maybe professions as well. We can observe the media race to the news feeds about the use of generative AI. What is often left aside, however, is the inconvenient topic of legal consequences of such modifications to human work that is used to train AI, and there are many. This article addresses the issue of the clash of copyright with AI. It discusses the matters of using someone’s creative work to design AI systems, as well as the consequences of a designer’s work with AI, especially on commercial projects, including the notion of copyrights to the content generated by means of such tools, and attempts to determine when there is a chance to grant their protection. These considerations are complemented with examples of legal actions taken against suppliers of AI systems. Part of the text has been dedicated to discussing the pending regulations, especially the Act on AI. Keywords: copyright, artificial intelligence (AI), fair use

Generative artificial intelligence and optimisation framework for concrete mixture design with low cost and embodied carbon dioxide

This research presents a generative Artificial Intelligence (AI) and design framework that integrates machine learning (ML) and optimisation methodologies to discover new concrete mixture designs. Unlike traditional ML models that predict based on existing data, this framework innovatively generates new concrete mix designs that meet specific requirements such as strength, cost-efficiency, and reduced embodied CO2. To propose a powerful and reliable generative AI model, several advanced ML algorithms were considered, e.g., CatBoost, XGBoost, and LGBM. These models were trained on a unique dataset consisting of 4,936 data points collected from five different batching plants and have not been published yet. Bayesian Optimisation was employed to fine-tune model hyperparameters, resulting in the most effective models attaining R2 values of 0.94 and 0.89 for raw and grouped data, respectively. To verify the trained generative AI model, a case study was conducted, in which the model was requested to provide designs of a mix with pre-determined strength and optimised cost and embodied CO2. The mix designs generated by the framework were successfully validated through experimental tests, corroborating the predictive outcomes. The research culminated in the development of a web application, a tool crafted to streamline the concrete mixture design and optimisation process. This generative AI design framework can be applied to many other aspects of material design and engineering problems.

Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review.

Mycotoxin contamination of foods is a major concern for food safety and public health worldwide. The contamination of agricultural commodities employed by humankind with mycotoxins (toxic secondary metabolites of fungi) is a major risk to the health of the human population. Common methods for mycotoxin detection include chromatographic separation, often combined with mass spectrometry (accurate but time-consuming to prepare the sample and requiring skilled technicians). Artificial intelligence (AI) has been introduced as a new technique for mycotoxin detection in food, providing high credibility and accuracy. This review article provides an overview of recent studies on the use of AI methods for the discovery of mycotoxins in food. The new approach demonstrated that a variety of AI technologies could be correlated. Deep learning models, machine learning algorithms, and neural networks were implemented to analyze elaborate datasets from different analytical platforms. In addition, this review focuses on the advancement of AI to work concomitantly with smart sensing technologies or other non-conventional techniques such as spectroscopy, biosensors, and imaging techniques for rapid and less damaging mycotoxin detection. We question the requirement for large and diverse datasets to train AI models, discuss the standardization of analytical methodologies, and discuss avenues for regulatory approval of AI-based approaches, among other top-of-mind issues in this domain. In addition, this research provides some interesting use cases and real commercial applications where AI has been able to outperform other traditional methods in terms of sensitivity, specificity, and time required. This review aims to provide insights for future directions in AI-enabled mycotoxin detection by incorporating the latest research results and stressing the necessity of multidisciplinary collaboration among food scientists, engineers, and computer scientists. Ultimately, the use of AI could revolutionize systems monitoring mycotoxins, improving food safety and safeguarding global public health.

Implementation of a semi-automatic approach for detection and extraction of oil slicks using Sentinel-1: Case study of the Moroccan exclusive economic zone

Due to its strategic geographic location, Morocco experiences heavy ship traffic, which inevitably has consequences. Ship accidents and illegal discharges, commonly referred to as oil spills, are frequent and pose significant harm to the marine ecosystem. Therefore, employing new technologies, such as satellite monitoring, is crucial and will greatly enhance the ability to monitor and assess these oil spills. The present work is based on Sentinel-1 SAR images and aims to take into evidence the extent of the hydrocarbon marine pollution in all of the Moroccan EEZ. More than 220 possible cases were detected and mapped during the last decade (2014–2024). A batch process was developed to automate the detection of oil spills by identifying areas with reduced Normalized Radar Cross Section (NRCS) values. This process incorporates corrections, masks, wind field estimation, and oil clustering. The method successfully detected approximately 80% of the cases between Kenitra and Casablanca. The closest detected spill is located 1 km from the coast, the largest spill covers an area of 90 km2, and the most extended spill measures about 145 km. This study can help raise awareness among governmental authorities about potential environmental damage and can be used to train artificial intelligence models, which could simulate oil slick propagation.

SWOT analysis of the application of artificial intelligence in radiologic technology and radiology

Artificial intelligence (AI) is bringing changes to radiology and radiologic technology, enabling the development of programs and algorithms that facilitate diagnosis and decisions. It is essential to understand how AI can improve patient outcomes, increase the efficiency of investigations and reduce costs. Machine and deep learning have proven to be extremely useful in the detection and characterization of lesions, improving routine imaging techniques and facilitating the work of radiologists by reducing workload and improving the quality of reporting. The practical application of artificial intelligence in radiology and radiologic technology has been slowed down by the lack of integrated solutions and well-structured data archives, as well as challenges such as non-transparency of decision-making systems and large amounts of quality data needed to train artificial intelligence models. There are concerns about the potential impact of AI on the work of radiologists and radiologic technologists, which may hinder the development and implementation of this technology. Textual data derived from image reports can provide valuable healthcare insights. Natural language processing (NLP), a subset of artificial intelligence, offers promising solutions for handling unstructured text in these reports, opening a new era in extracting information from medical images and related reports. Due to the challenges in training experts for the application of AI in healthcare, a multidisciplinary approach will have to be used and investments in collaboration and education will have to be made. There are outstanding issues of liability and regulation regarding data storage and privacy, particularly in the case of cloud storage. The concern of the workforce and their lack of education about artificial intelligence represents an obstacle to its adoption, but also offers an opportunity for the technological advancement of the profession.

The Dual Nature of AI in Information Dissemination: Ethical Considerations.

Infodemics pose significant dangers to public health and to the societal fabric, as the spread of misinformation can have far-reaching consequences. While artificial intelligence (AI) systems have the potential to craft compelling and valuable information campaigns with positive repercussions for public health and democracy, concerns have arisen regarding the potential use of AI systems to generate convincing disinformation. The consequences of this dual nature of AI, capable of both illuminating and obscuring the information landscape, are complex and multifaceted. We contend that the rapid integration of AI into society demands a comprehensive understanding of its ethical implications and the development of strategies to harness its potential for the greater good while mitigating harm. Thus, in this paper we explore the ethical dimensions of AI's role in information dissemination and impact on public health, arguing that potential strategies to deal with AI and disinformation encompass generating regulated and transparent data sets used to train AI models, regulating content outputs, and promoting information literacy.

The Impact of Artificial Intelligence on Design: Enhancing Creativity and Efficiency

Artificial Intelligence (AI) has emerged as a transformative force in the design industry, reshaping the way creativity and efficiency are approached in the design process. This paper explores the profound impact AI has on design, highlighting its role in enhancing creativity, improving efficiency, and expanding possibilities through advanced algorithms and machine learning. AI assists designers by automating repetitive tasks, generating new design variations, and providing insights into consumer preferences, thus enabling data-driven and personalized design solutions. However, the integration of AI in design is not without challenges, including concerns about the loss of human touch, ethical implications regarding authorship and intellectual property, and the potential perpetuation of biases present in AI training data. Through a mixed-method research approach involving semi-structured interviews and content analysis, this study investigates how AI serves as a collaborator in the design process, offering valuable insights for leveraging AI to push the boundaries of creativity while addressing ethical and practical challenges. The findings indicate that AI has the potential to revolutionize design by acting as a powerful tool that augments human creativity, enhances productivity, and facilitates personalized user experiences.

Optimizing Catheter Verification: An Understandable AI Model for Efficient Assessment of Central Venous Catheter Placement in Chest Radiography.

Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection. The study utilized 2 datasets: the publicly accessible RANZCR CLiP dataset and a bespoke in-house dataset of 1006 annotated supine chest x-rays. Three deep learning models were trained: a classification network, a segmentation network, and a combination of both. These models were evaluated using receiver operating characteristic analysis, area under the curve, DICE similarity coefficient, and Hausdorff distance. The combined model demonstrated superior performance with an area under the curve of 0.99 for correctly positioned CVCs and 0.95 for misplacements. The model maintained high efficacy even with reduced training data from the local dataset. Sensitivity and specificity rates were high, and the model effectively managed the segmentation and classification tasks, even in images with multiple CVCs and other support materials. This study illustrates the potential of AI-based models in accurately and reliably determining CVC placement in chest x-rays. The proposed method shows high accuracy and offers improved interpretability, important for clinical decision-making. The findings also highlight the importance of dataset quality and diversity in training AI models for medical image analysis.

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

AI Methods and Algorithms for Diagnosis of Intestinal Parasites: Applications, Challenges and Future Opportunities

Artificial intelligence (AI) transforms intestinal parasite diagnosis, particularly through deep learning models like convolutional neural networks (CNNs). This paper reviews the application of AI, especially CNNs, in automating parasite detection and classification from microscopic images. Integrating AI into parasitology diagnostics speeds up the process, reduces human error, and enhances treatment and patient outcomes. However, there is a need for more datasets reflecting the African context to ensure accurate ground-truthing, particularly in low- and middle-income countries (LMICs) across Africa. Most AI models for medical diagnosis are trained on datasets from high-income countries, which may not capture the unique epidemiological, genetic, and environmental factors prevalent in African populations. This can lead to less accurate diagnoses and treatment recommendations in African LMICs. For example, intestinal parasitic infections are common in many African regions, yet the datasets used to train AI models often lack sufficient representation from these areas. Developing datasets that reflect the diverse African context is crucial for improving the accuracy and reliability of AI-based diagnostic tools. Issues like overfitting, data privacy, and cost also require attention. Collaboration between researchers, healthcare professionals, and technologists is essential to address these challenges. Standardized protocols for data collection, model training, and validation are necessary for reliable AI systems. Combining AI with traditional techniques holds promise for better parasite diagnosis, ultimately improving African healthcare outcomes

A Comprehensive Study: AI Literacy as a Component of Media Literacy

The widespread use of AI-based technologies has sparked educational, social, and political interest in AI training. Education systems must prepare individuals for a world with AI. AI literacy is a cognitive and pedagogical difficulty. AI's language and intricacies need redefining literacy. Because these systems are easy to use, more individuals are utilizing them than those with limited conceptualizations (such as an inability to grasp the future relevance of these systems) or competencies (like an inability to comprehend how these systems function). The study investigates the increasing significance of artificial intelligence (AI) literacy within the context of media literacy. As AI technologies permeate various aspects of modern media, the capacity to comprehend and engage critically with these systems has become essential. The paper begins by analyzing the intricate intersection of AI and media, focusing on content creation, dissemination, and consumption. It then emphasizes the importance of AI literacy, which is the ability to comprehend, implement, and evaluate AI technologies critically, similar to traditional media literacy skills. Finally, the paper proposes that AI literacy, as part of media literacy, entails understanding how these systems function and their ethical and societal implications. The paper's conclusion offers a comprehensive framework for incorporating AI literacy into media education curricula, aiming to empower individuals to navigate, evaluate, and responsibly participate in the evolving AI-mediated media landscape.

Оптимизация расчета оптической силы интраокулярной линзы с использованием возможностей искусственного интеллекта

Purpose. Development of a technology for optimal formula determination of the IOL required optical power, based on the artificial intelligence (AI) and the individual clinical characteristics of the patient’s eye. Material and methods. A retrospective analysis of 1337 uncomplicated phacoemulsification with implantation of a monofocal IOL RAO100C (Rayner) was performed. For artificial intelligence training and models testing, a database was created containing 1080 clinical cases (1080 eyes); the process of filling the database was fully automated. The accuracy criterion for IOL prediction was the range of spheroequivalent (SE) values in the postoperative period <|0.5|. Results. A retrospective analysis showed that in uncomplicated cataract surgery, the accuracy of the target refraction achievement is high and does not go beyond ±0.5 D. 16 artificial intelligence models were developed and tested, of which 4 were selected based on accuracy metrics – 1 model for each formula (Barrett, Haigis, Holladay 2, HofferQ). The best models turned out to be DecisionTreeClassifier. Validation of AI models was carried out by comparing the choice of IOL diopter between the AI and an expert. All models showed an advantage over the expert. A software interface for this service was developed and implemented. Conclusion. A selective database has been created for training and testing artificial intelligence models that determine IOL optical power. A system has been developed to help a doctor make a decision to determine the required IOL optical power based on artificial intelligence, depending on the individual clinical characteristics of the patient’s eye. The high efficiency of the developed technology has been proven on a test model (90–95%). The program interface was developed and implemented. Key words: IOL calculation, artificial intelligence

Motivating employee voicing behavior in optimizing workplace generative ai adoption: The role of organizational listening

Given the flourishing of artificial intelligence (AI), organizations are increasingly exploring ways to seamlessly incorporate novel technologies in the workplace. The emergence of generative AI has further propelled the transformation of a technology-led managerial era. This study aims to investigate the role of organizational listening during workplace generative AI training in helping employees adapt to and embrace generative AI adoption by voicing suggestions for its improvements. Within this framework, we examined the employees’ psychological adaptation process, focusing on perceived autonomy and competence through the lens of self-determination theory (SDT), as well as employee attitudes toward generative AI adoption. Results of an online survey with 500 full-time employees in the United States suggest that organizational listening was positively associated with employees’ perceived autonomy and competence, which in turn, boosted their positive attitudes toward workplace generative AI adoption. This, ultimately, leads to employee voicing behavior. Theoretical and practical implications of the findings are elaborated.