Artificial Language Research Articles

Individual differences in the acquisition of shared syntactic representations: a re-analysis of studies using an artificial language learning paradigm

Individual differences in the acquisition of shared syntactic representations: a re-analysis of studies using an artificial language learning paradigm

Exploring knowledge management technologies to enhance sustainability and mitigate technostress from a collaborative perspective

Purpose The relevance of digital technology and knowledge management (KM) has been increasing continuously owing to their importance in sustainability. However, research on which technology leads to collaboration in KM and how it affects sustainability is lacking. Therefore, this study aims to analyze knowledge collaboration management technologies for sustainable growth based on academic literature and technology patents. Design/methodology/approach This paper analyzed current technology and academic trends in KM using BERTopic, an artificial intelligence natural language processing model, and proposed future promising candidate technologies and potential application areas in terms of collaboration and sustainability. Findings From the textual data of research papers and patents, this paper extracted 10 and 47 major topics, respectively. Moreover, five potential and feasible candidate patents along with their applicable fields were recommended. Originality/value The strength of this study is its analysis of the technologies affecting collaboration and sustainability in KM. This paper found that new digital technologies (i.e. artificial intelligence, cloud computing and big data) impact organizations’ KM and have the potential for collaboration. Digital technologies have a positive impact on corporate smartness directly and sustainability and inclusion indirectly. This paper argued that organizations should understand and identify various promising technological factors and apply them to their KM from a collaboration perspective.

Enhancing Clinical Reasoning with Virtual Patients: A Hybrid Systematic Review Combining Human Reviewers and ChatGPT

Objectives: This study presents a systematic review aimed at evaluating the effectiveness of virtual patients in enhancing clinical reasoning skills in medical education. A hybrid methodology was used, combining human reviewers and ChatGPT to assess the impact of conversational virtual patients on student learning outcomes and satisfaction. Methods: Various studies involving conversational virtual patients were analyzed to determine the effect of these digital tools on clinical competencies. The hybrid review process incorporated both human assessments and AI-driven reviews, allowing a comparison of accuracy between the two approaches. Results: Consistent with previous systematic reviews, our findings suggest that conversational virtual patients can improve clinical competencies, particularly in history-taking and clinical reasoning. Regarding student feedback, satisfaction tends to be higher when virtual patients’ interactions are more realistic, often due to the use of artificial intelligence (AI) and natural language processing (NLP) in the simulators. Furthermore, the study compares the accuracy of AI-driven reviews with human assessments, revealing comparable results. Conclusions: This research highlights AI’s potential to complement human expertise in academic evaluations, contributing to more efficient and consistent systematic reviews in rapidly evolving educational fields.

IndoGovBERT: A Domain-Specific Language Model for Processing Indonesian Government SDG Documents

Achieving the Sustainable Development Goals (SDGs) requires collaboration among various stakeholders, particularly governments and non-state actors (NSAs). This collaboration results in but is also based on a continually growing volume of documents that needs to be analyzed and processed in a systematic way by government officials. Artificial Intelligence and Natural Language Processing (NLP) could, thus, offer valuable support for progressing towards SDG targets, including automating the government budget tagging and classifying NSA requests and initiatives, as well as helping uncover the possibilities for matching these two categories of activities. Many non-English speaking countries, including Indonesia, however, face limited NLP resources, such as, for instance, domain-specific pre-trained language models (PTLMs). This circumstance makes it difficult to automate document processing and improve the efficacy of SDG-related government efforts. The presented study introduces IndoGovBERT, a Bidirectional Encoder Representations from Transformers (BERT)-based PTLM built with domain-specific corpora, leveraging the Indonesian government’s public and internal documents. The model is intended to automate various laborious tasks of SDG document processing by the Indonesian government. Different approaches to PTLM development known from the literature are examined in the context of typical government settings. The most effective, in terms of the resultant model performance, but also most efficient, in terms of the computational resources required, methodology is determined and deployed for the development of the IndoGovBERT model. The developed model is then scrutinized in several text classification and similarity assessment experiments, where it is compared with four Indonesian general-purpose language models, a non-transformer approach of the Multilabel Topic Model (MLTM), as well as with a Multilingual BERT model. Results obtained in all experiments highlight the superior capability of the IndoGovBERT model for Indonesian government SDG document processing. The latter suggests that the proposed PTLM development methodology could be adopted to build high-performance specialized PTLMs for governments around the globe which face SDG document processing and other NLP challenges similar to the ones dealt with in the presented study.

Starting from the very beginning: Unraveling Third Language (L3) Development with Longitudinal Data from Artificial Language Learning and EEG

ABSTRACT The burgeoning field of third language (L3) acquisition has increasingly focused on intermediate stages of language development, aiming to establish the groundwork for comprehensive models of L3 learning that encompass the entire developmental sequence. This article underscores the importance of a robust epistemological foundation, advocating for incremental knowledge building through longitudinal research. In the study presented here, we use artificial languages to investigate L3 acquisition from initial exposure with complete input control, factoring in individual differences in executive functions and history of bilingual exposure/engagement to assess the role of these variables in shaping learning trajectories and modulating cross-linguistic influence (CLI). This approach not only advances our understanding of L3 development under controlled conditions but also links L3 acquisition research to broader cognitive science inquiries.

AI-powered language learning: The role of NLP in grammar, spelling, and pronunciation feedback

Abstract. The introduction of artificial intelligence (AI) and natural language processing (NLP) in language learning has transformed the practices of education. This paper analyzes the role of AI-powered tools in English language learning with specific focus on automated essay scoring (AES), speech recognition, and advanced grammar and spelling checkers. By examining the accuracy of the most effective AES systems, it is evident that automated feedback has increased the quality of writing by providing detailed, consistent, and objective feedback on submitted essays. Speech recognition is another tool that is being used to assess pronunciation and fluency of learners. The software is constantly improving, with advances in NLP and machine learning. The technology for fluency tests is becoming more accurate, and is helping to improve learner outcomes significantly. Lastly, we examine advanced grammar and spelling checkers, which are increasingly used for correcting the contextual errors of individual learners. The AI systems adapt to the user and their needs, which make the corrections more effective. This paper offers an overview of how AI is leading to personalized and effective language learning, ultimately enhancing the educational experience of students and embracing future possibilities in education technology.

Perceptions of Artificial Intelligence and ChatGPT by Speech-Language Pathologists and Students.

This project explores the perceived implications of artificial intelligence (AI) tools and generative language tools, like ChatGPT, on practice in speech-language pathology. A total of 107 clinician (n = 60) and student (n = 47) participants completed an 87-item survey that included Likert-style questions and open-ended qualitative responses. The survey explored participants' current frequency of use, experience with AI tools, ethical concerns, and concern with replacing clinicians, as well as likelihood to use in particular professional and clinical areas. Results were analyzed in the context of qualitative responses to typed-response open-ended questions. A series of analyses indicated participants are somewhat knowledgeable and experienced with GPT software and other AI tools. Despite a positive outlook and the belief that AI tools are helpful for practice, programs like ChatGPT and other AI tools are infrequently used by speech-language pathologists and students for clinical purposes, mostly restricted to administrative tasks. While impressions of GPT and other AI tools cite the beneficial ways that AI tools can enhance a clinician's workloads, participants indicate a hesitancy to use AI tools and call for institutional guidelines and training for its adoption.

Generative artificial intelligence (GAI): From large language models (LLMs) to multimodal applications towards fine tuning of models, implications, investigations

This research explores the transformative integration of artificial intelligence (AI), robotics, and language models, with a particular emphasis on the PaLM-E model. The exploration aims to assess PaLM-E’s decision-making processes and adaptability across various robotic environments, demonstrating its capacity to convert textual prompts into very precise robotic actions. In addition, the research investigates Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), providing a historical overview of PEFT and highlighting their significance in enhancing task performance while reducing the number of trainable parameters. The broader scope of Generative AI is examined through an analysis of influential models like GPT-3, GPT-4, Copilot, Bard, LLaMA, Stable Diffusion, Midjourney, and DALL-E. These models’ abilities to process natural language prompts and generate a wide range of outputs are thoroughly investigated. The research traces the historical evolution of AI, from its roots in science fiction to its practical applications today, with a focus on the rise of Generative AI in the 21st century. Furthermore, the research delves into the various modalities of Generative AI, covering applications in text, code, images, and more, and assesses their real-world impact on robotics, planning, and business intelligence. The implications of synthetic data generation for business analytics are also explored. The research inspects within both software and hardware landscapes, comparing local deployment on consumer-grade hardware along with cloud-based services, and underscores the benefits of local model deployment in terms of privacy protection, intellectual property security, and censorship resistance. Ethical considerations are central to this research, addressing concerns related to privacy, security, societal impact, biases, and misinformation. The research proposes ethical guidelines for the responsible development and deployment of AI technologies. Ultimately, this work reveals the deep interconnections between vision, language, and robotics, pushing the boundaries of AI capabilities and providing crucial insights for future AI model development and technological innovation. These findings are intended to guide the field through the emerging challenges of the rapidly evolving Generative AI landscape.

Evidence-Based Potential of Generative Artificial Intelligence Large Language Models on Dental Avulsion: ChatGPT Versus Gemini.

In this study, the accuracy and comprehensiveness of the answers given to questions about dental avulsion by two artificial intelligence-based language models, ChatGPT and Gemini, were comparatively evaluated. Based on the guidelines of the International Society of Dental Traumatology, a total of 33 questions were prepared, including multiple-choice questions, binary questions, and open-ended questions as technical questions and patient questions about dental avulsion. They were directed to ChatGPT and Gemini. Responses were recorded and scored by four pediatric dentists. Statistical analyses, including ICC analysis, were performed to determine the agreement and accuracy of the responses. The significance level was set as p < 0.050. The mean score of the Gemini model was statistically significantly higher than the ChatGPT (p = 0.001). ChatGPT gave more correct answers to open-ended questions and T/F questions on dental avulsion; it showed the lowest accuracy in the MCQ section. There was no significant difference between the responses of the Gemini model to different types of questions on dental avulsion and the median scores (p = 0.088). ChatGPT and Gemini were analyzed with the Mann-Whitney U test without making a distinction between question types, and Gemini answers were found to be statistically significantly more accurate (p = 0.004). The Gemini and ChatGPT language models based on the IADT guideline for dental avulsion undoubtedly show promise. To guarantee the successful incorporation of LLMs into practice, it is imperative to conduct additional research, clinical validation, and improvements to the models.

Adapting to Individual Differences: An Experimental Study of Language Evolution in Heterogeneous Populations.

Variations in language abilities, use, and production style are ubiquitous within any given population. While research on language evolution has traditionally overlooked the potential importance of such individual differences, these can have an important impact on the trajectory of language evolution and ongoing change. To address this gap, we use a group communication game for studying this mechanism in the lab, in which micro-societies of interacting participants develop and use artificial languages to successfully communicate with each other. Importantly, one participant in the group is assigned a keyboard with a limited inventory of letters (simulating a speech impairment that individuals may encounter in real life), forcing them to communicate differently than the rest. We test how languages evolve in such heterogeneous groups and whether they adapt to accommodate the unique characteristics of individuals with language idiosyncrasies. Our results suggest that language evolves differently in groups where some individuals have distinct language abilities, eliciting more innovative elements at the cost of reduced communicative success and convergence. Furthermore, we observed strong partner-specific accommodation to the minority individual, which carried over to the group level. Importantly, the degree of group-wide adaptation was not uniform and depended on participants' attachment to established language forms. Our findings provide compelling evidence that individual differences can permeate and accumulate within a linguistic community, ultimately driving changes in languages over time. They also underscore the importance of integrating individual differences into future research on language evolution.

DocBot AI as Your Personal Health Assistant: Bridging the Gap in Consultations Revolutionizing Healthcare Using Natural Language Processing and Artificial Intelligence

The DocBot Chat Bot/AI system is a novel solution to the challenge of acquiring accurate and personalized health information. By utilizing cutting-edge artificial intelligence (AI) algorithms and natural language processing (NLP) methods, the DocBot system creates an engaging and intuitive interface that lets people convey their health concerns and get personalized advice. This study provides an in-depth assessment of the DocBot system, stressing its key features, AI algorithms, advantages, difficulties, and potential applications. To improve healthcare communication and encourage well-informed decision-making, it is important to highlight DocBot's transformative potential in providing users with accurate and trustworthy health knowledge.

Order shaped by cognition. Evidence for (and against) the effect of domain-general biases on word and morpheme order

Abstract A foundational goal of linguistics has been to understand why languages look the way they do. A range of possible explanations exist – from domain-specific representations to cognition-external factors like history and grammaticalization – and all of these undoubtedly play some role. But determining exactly how these link with specific features of language remains challenging, and the role of domain-specific mechanisms has been particularly contentious. In this paper, I highlight a growing new approach, which uses artificial language experiments to link individual-level biases to cross-linguistic trends in language structure. Using word and morpheme order as case studies, I will show how a range of different paradigms and learner populations allow us to make progress on this crucial issue in linguistics. I will focus on typological trends in word and morpheme order. For some ordering trends, experimental evidence points to variation across populations, suggesting that the best explanation for these patterns likely has its root in language history and grammaticalization. In other cases, the evidence points to the role of universal but domain-general cognitive biases, like transparency and simplicity. These domain-general biases interact with linguistic representations in important ways. Taken together these studies help adjudicate between alternative explanations for a number of specific ordering patterns and suggest a new sense of domain-specificity in the evolution of language.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

Mysterious Interrelation: NLP and Literary Imagination

This research study examines the emerging topic of investigating the combination of artificial intelligence (AI) and natural language processing (NLP) in the context of literature. The literary domain has transformed with the incorporation of AI-led NLP beings, illustrating their progression from simple tools to intricate characters wrestling with profound philosophical inquiries. This research paper will explore the burgeoning relationship between Natural Language Processing and literature, focusing on the complex intersections that shed light on the depiction of intelligent machines and the ethical dilemmas they pose in literary works. This investigation will involve multiple aspects: primarily, the depiction of NLP entities as characters within stories, analyzing their emotional complexity, development, and interactions with human protagonists through Qualitative Literature Review and Textual Analysis Methodology. Furthermore, it will examine the ethical quandaries associated with NLP, encompassing issues of autonomy, morality, and the ethical obligations inherent in their development and existence through a few case studies.

Close clinical monitoring of patients after TAVI implantation using artificial intelligence with a virtual voice assistant

Abstract Introduction The number of procedures and patients candidates for transcatheter aortic valve implantation (TAVI) has increased considerably due to population ageing and the rise in scientific evidence supporting this technique in patients with increasingly lower surgical risk. Early discharge would allow for a more efficient use of available resources, in addition to improving patients’ functionality and quality of life. Objective The use of a virtual voice assistant based on artificial intelligence, could be an effective and safe alternative to closely monitor patients after TAVI implantation in order to facilitate early identification of complications and a more efficient use of the resources available in Cardiology services. Material and methods Observational, prospective, single-center and consecutive study of the implementation of a technology based on artificial intelligence and natural language processing to monitor all patients undergoing transfemoral TAVI implantation, according to usual clinical practice, in the hemodynamics unit of our hospital in the year 2023. The virtual voice assistant, "Lola", carries out a telephonic follow-up during week 1, week 2, month 1, month 3 and 12. In these calls, it asks a series of questions mainly related to vascular access and with the patient's cardiovascular situation. After finishing the call, all the information collected is uploaded to a web platform where we monitor the collected data and thus take the appropriate measures. Results After 12 months, 274 patients have been included to whom 1,039 calls have been made with a total of 385 hours of autonomous conversation. In 44% of the calls, no alerts were detected and, therefore, did not required further review. From the remaining calls, a total of 926 alerts were collected, the predominant ones being: dizziness (24.9%), contact with the emergency services (11.3%) and alteration of general status (10.4%). The number of alerts decreases as the follow-up progresses, reflecting the need for closer monitoring in the initial stages after implantation. These alerts generated at least one intervention in 57% of the calls, the predominant ones being: medical contact (43.0%), nursing contact (26.3%) and medication adjustment (4.0%). These calls resulted in a total of 15 in-person examinations (5.4% of patients), detecting in 2 of them a 3rd degree atrioventricular block, in 3 a femoral pseudoaneurysm and in 1 a critical stenosis of the common femoral artery. According to a patient survey, 88.9% of patients were satisfied or very satisfied (Customer Satisfaction score 4.68/5). Conclusions The application of artificial intelligence permits strict control of the patient after TAVI implantation, thus facilitating early detection of complications. Patient satisfaction regarding this follow-up system was very high.

Artificial Intelligence, Language, and Humanization in the Academic Library

Artificial Intelligence, Language, and Humanization in the Academic Library

Reflecting the voices of EFL teachers in the world of Intelligent Computer-Assisted Language Assessment (ICALA): an insight into teacher immunity, reflective teaching, job satisfaction, and L2-teacher grit

Artificial intelligence (AI) and Intelligent Computer-Assisted Language Assessment (ICALA) are transforming the educational landscape by radically changing how lessons are taught and students are evaluated. As the masterminds behind the curriculum, it is critical to consider the emotional and mental health of teachers who applied ICALA as part of their language instruction. To this end, this study’s primary objective was to examine the connections between ICALA, teacher immunity, reflective teaching, job satisfaction, and l2-teacher grit. To achieve this goal, 221 English as a foreign language (EFL) university teachers from Afghanistan participated in the current research. The research outcomes identified and quantified the influences of teacher immunity and reflective teaching on job satisfaction and L2 teacher grit by data screening utilizing confirmatory factor analysis (CFA) and structural equation modeling (SEM). According to the results, TI and RT are vital in implementing AI into language assessment by maintaining an uninterrupted JS and L2TG. The study’s ramifications are further elaborated upon.

Investigating translation for Indic languages with BLOOMZ-3b through prompting and LoRA fine-tuning

In the domain of natural language processing, the rise of Large Language Models and Generative AI represents a noteworthy transition, enabling machines to understand and generate text resembling that produced by humans. This research conducts a thorough examination of this transformative technology, with a focus on its influence on machine translation. The study explores the translation landscape between English and Indic languages, which include Hindi, Kannada, Malayalam, Tamil, and Telugu. To address this, the Large Language Model, BLOOMZ-3b, is utilized, which has been primarily developed for a text generation task. Multiple prompting engineering techniques for machine translation are prominently explored. The study further traverse fine-tuning the BLOOMZ-3b model using a Parameter Efficient Fine-Tuning technique called Low Rank Adaptation, aiming to reduce computational complexity. Hence, by combining innovative prompting approaches using BLOOMZ-3b model and fine-tuning the model, it contributes to continuous development of machine translation technologies beyond traditional borders of what can be done with respect to language processing. In this regard, not only does this research shed light on the intricacy of translation problems but it also sets a precedence for optimizing or adapting big language models to various languages which end up advancing Artificial Intelligence and Natural Language Processing at large.

Access to longitudinal mental health data in Africa: Lessons from a landscape analysis by the INSPIRE network datahub

Background Data from longitudinal mental health research in Africa is critical to understanding the complexities of mental health disorders in the continent’s diverse contexts. To be useful, data need to adhere to the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Methods A literature search from 1970 to 2022 identified longitudinal studies on depression, anxiety, and psychosis in was done. Using Artificial Intelligence (AI) and Natural Language Processing techniques, the search found data from more studies. The search engaged stakeholders in understanding data sharing practices and barriers and categorizing methods and challenges for sharing data. Results The initial search yielded 18,019 articles, of which 284 were eligible for review, and 226 passed quality assessments. A significant effort to access data directly from researchers yielded positive responses for 100 articles with available data statements, from which datasets were requested through online repositories and direct correspondence. Analysis revealed significant disparities in the distribution of mental health research across countries, with a concentration of studies in specific areas and on certain conditions. The study also highlighted a varied adherence to FAIR principles, with only 16 datasets adhering to data-sharing practices. Conclusions Despite the challenges encountered in data accessibility and the manual adjustments required, the study’s findings irradiate the path toward a more equitable and effective mental health research ecosystem on the continent. By fostering collaboration and embracing advanced methodologies and technologies, this study advocates for a concerted effort to improve the accessibility, interoperability, and reusability of mental health data. Ultimately, the project aims to contribute to understanding data-sharing dynamics in Africa, paving the way for informed interventions and policies.

AI-Enhanced Geomatics Engineering: Innovative Solutions and Applications Using ChatGPT, an Advanced AI Language Model

This study investigates the potential of the advanced artificial intelligence language model, ChatGPT, in innovative solutions and applications within Geomatics Engineering, under the title "AI-Enhanced Geomatics Engineering: Innovative Solutions and Applications Using ChatGPT". ChatGPT is renowned for its ability to generate human-like text and has been trained on extensive datasets. Geomatics Engineering encompasses various sub-disciplines such as geographic information systems (GIS), remote sensing, global positioning systems (GPS), geodesy, and geomatics engineering. The research analyzes how AI technology, exemplified by models like ChatGPT, can be leveraged in Geomatics Engineering projects to enhance project management, improve data analysis accuracy, increase process efficiency, and impact project outcomes. AI models like ChatGPT enable the analysis of large geospatial datasets and play a crucial role in understanding and interpreting complex datasets. Furthermore, these technologies are expected to save time and contribute to obtaining more accurate results in Geomatics Engineering applications. The study also demonstrates how the integration of AI into Geomatics Engineering disciplines can be achieved through case studies and practical applications. These case studies illustrate how AI-supported technologies can be applied to real-world problems and improve current practical applications within the industry. In conclusion, this research underscores how AI can play a progressive role in Geomatics Engineering and emphasizes the transformative impact future technological advancements can have in these disciplines. The integration and utilization of such technologies are of paramount importance for exploring potential applications in this field.