Intelligent Language Research Articles

Design of an intelligent English learning platform combining biomechanical analysis with biological data analysis and text semantic matching

Intelligent classrooms have demonstrated significant promise in enhancing learning efficiency as a result of the quick development of big data and artificial intelligence technologies. This study proposes a text semantic matching model (OM) that combines deep learning and K-means clustering algorithm, aiming to optimize vocabulary. Importantly, it delves into the biomechanical aspects of learning by considering how physical and physiological processes interact with language acquisition. By mimicking the learning mechanism of biological neural networks and further exploring the biomechanical correlates of neural activity during learning, such as the muscle tensions and postural changes associated with cognitive efforts, this model simulates how the brain processes and stores language information. These biomechanical factors can have an impact on concentration and fatigue levels, which in turn affect semantic understanding and memory performance during the learning process. The experimental results indicate that this method not only improves teaching effectiveness, but also provides a solid foundation for future research on intelligent language learning environments, taking into account the biomechanical underpinnings of learning.

Digital Language Services: Evolution, Challenges, Opportunities, and Ethical Implications

Digital language services (DLS), powered by advanced technologies, have become effective instruments for promoting cooperation and communication across linguistic boundaries. We selected 25 publications to guide this study based on both inclusion and exclusion criteria. Through a systematic review based on the framework of PRISMA, it is revealed that there is a huge potential for innovation and progress in the intelligent language service industry, even while there are still difficulties and ethical concerns unaddressed. We can fully utilize Digital language services to construct and maintain a more interconnected and inclusive world by tackling these obstacles and utilizing cutting-edge technologies. The collaboration between humans and technologies also plays an important role in DLS. It is expected that humans will improve the development of DLS by assisting with technologies rather than replacing them. This study formulates a solid foundation for future researchers and practitioners, based on which numerous research directions can be delved into shortly. The findings can be applied to various fields such as education, marketing, social studies, and computer science.

A New Path to Language Education Innovation Based on Networked Smart Language Laboratories

This paper discusses the importance of networked intelligent language laboratory as a new path for language education innovation. It elaborates on its background, technical support (Internet of Things, big data, cloud computing, artificial intelligence technology), advantages (resource sharing and utilization efficiency improvement, personalized learning support, enhanced teaching interaction and feedback, environmental perception and intelligent management), innovative applications (diversified teaching models, intelligent learning assessment, integration of language practice and cultural experience), analyzes the challenges in construction (technical security and privacy protection, teacher role transformation and capacity improvement, education resource integration and quality assurance), and summarizes the research results and looks forward to future development trends, including continuous technological innovation, expanding and deepening applications, and strengthening globalization and cross-cultural communication and cooperation.

Optimization Strategies for the Construction and Application of Networked Intelligent Language Laboratory Practice Bases under the Perspective of Collaborative Education

Under the background of collaborative education, this paper discusses the construction and optimization strategies for the use of networked intelligent language laboratory practice bases. By analyzing its importance, including meeting the needs of educational informatization, improving the quality of language teaching, and cultivating students' innovation and practical abilities, measures such as strengthening school-enterprise cooperation, optimizing the practical teaching system, building a collaborative operation mechanism for laboratories, and optimization strategies for use are proposed. Case studies of Zhejiang International Studies University and the School of Foreign Languages of Xiamen University are conducted to emphasize the significance and development trend of the construction and optimization of this practice base.

A New Path to Enhance Students' Comprehensive Language Literacy based on Intelligent Language Laboratory

With the acceleration of globalization and the rapid development of information technology, the enhancement of language ability has become an important part of students' comprehensive literacy. This paper discusses the concept of “Intelligent Language Laboratory” and its application in improving students' comprehensive language literacy. By integrating modern information technology, language learning theory and teaching practice, the Smart Language Lab provides an interactive and personalized language learning environment for students. The study shows that the laboratory can effectively improve students' language expression, listening and speaking skills and cross-cultural communication skills, thus opening up a new path for cultivating comprehensively developed language talents.

Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics.

The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human-computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era.

Cross-Cultural Intelligent Language Learning System (CILS): Leveraging AI to Facilitate Language Learning Strategies in Cross-Cultural Communication

This research presents the Cross-Cultural Intelligent Language Learning System (CILS), a novel approach integrating artificial intelligence (AI) into language education to enhance cross-cultural communication. CILS utilizes advanced AI technologies to provide adaptive, personalized learning experiences that cater to the unique linguistic and cultural backgrounds of each learner. By dynamically adjusting content and methodology, CILS significantly improves linguistic proficiency and cultural understanding, essential for effective global interactions. The implementation of CILS in platforms such as Busuu and HelloTalk has demonstrated marked improvements in engagement and communication skills among learners. Empirical studies validate the system’s effectiveness in real-world settings, showing enhanced learner performance and increased intercultural competence. Additionally, the Technology Acceptance Model (TAM) applied confirms that the usability and perceived usefulness of AI-driven systems strongly influence learner acceptance and sustained use. This study not only underscores the potential of AI in transforming language education but also highlights the critical role of cultural sensitivity in designing educational technologies.

Intelligent Language Acquisition Model for Online Student Interaction with Educators Using 6G-Cyber Enhanced Wireless Network

Intelligent Language Acquisition Model for Online Student Interaction with Educators Using 6G-Cyber Enhanced Wireless Network

Enhancing AI competence in health management: students’ experiences with ChatGPT as a learning Tool

BackgroundThe healthcare industry has had to adapt to significant shifts caused by technological advancements, demographic changes, economic pressures, and political dynamics. These factors are reshaping the complex ecosystem in which healthcare organizations operate and have forced them to modify their operations in response to the rapidly evolving landscape. The increase in automation and the growing importance of digital and virtual environments are the key drivers necessitating this change. In the healthcare sector in particular, processes of change, including the incorporation of artificial intelligent language models like ChatGPT into daily life, necessitate a reevaluation of digital literacy skills.MethodsThis study proposes a novel pedagogical framework that integrates problem-based learning with the use of ChatGPT for undergraduate healthcare management students, while qualitatively exploring the students’ experiences with this technology through a thematic analysis of the reflective journals of 65 students.ResultsThrough the data analysis, the researcher identified five main categories: (1) Use of Literacy Skills; (2) User Experiences with ChatGPT; (3) ChatGPT Information Credibility; (4) Challenges and Barriers when Working with ChatGPT; (5) Mastering ChatGPT-Prompting Competencies. The findings show that incorporating digital tools, and particularly ChatGPT, in medical education has a positive impact on students’ digital literacy and on AI Literacy skills.ConclusionsThe results underscore the evolving nature of these skills in an AI-integrated educational environment and offer valuable insights into students’ perceptions and experiences. The study contributes to the broader discourse about the need for updated AI literacy skills in medical education from the early stages of education.

Research on the Concept, Architecture and Construction Strategy of Intelligent Language Laboratory

With the rapid development of artificial intelligence technology, smart language laboratory plays an increasingly important role as an important research platform in the field of artificial intelligence. This paper discusses the concept, characteristics and functions of the smart language laboratory, and analyzes the architecture of the smart language laboratory, including the construction of hardware facilities, software system, data resources and talent team. Meanwhile, the construction strategy of smart language lab is proposed, including construction planning, technology selection, team building and operation mode. The practical experience of smart language labs at home and abroad is analyzed through case studies, and the development trend and future outlook of smart language labs are summarized.

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

Research on the application of large language model in electric power enterprise audit

Large language model is a new technology in the field of natural language processing, which can be applied in the audit scenario of electric power enterprises to provide auditors with automatic audit methods based on intelligent language model. Based on the application background of big language model in the audit field of electric power enterprises, this paper introduces the main advantages of big language model, and analyzes the implementation methods, application processes and principles of big language model in electric power enterprise audit, so as to provide reference and help.

Research on the Language Acquisition Model of Non-Native English Learners Based on Artificial Intelligence

Abstract The widest application field of intelligent teaching supported by artificial intelligence technology is language learning, and enhancing learners’ language acquisition ability is the top priority. On the basis of analyzing the guidelines for the construction of a language acquisition model for non-native English learners, this paper combs through the main advantages of intelligent technology-assisted English teaching and establishes an intelligent language acquisition model based on artificial intelligence. Fuzzy cognitive diagnosis of the language acquisition level of non-native English learners is carried out using the Fuzzy-CDF model, the language acquisition ability of learners is modeled, and the regression model is constructed by combining with artificial intelligence technology so as to realize the bidirectional assessment of language acquisition level of learners. When the Fuzzy-CDF model is utilized for the learner’s language acquisition level, its MAE value is the lowest at 0.1385, and the influence coefficient of the teacher’s teaching method on the learner’s language acquisition ability is the largest at 23.506. The difference between the learner’s language acquisition response time at different levels on the experimental example sentences and the native learners is small, and close to 90% of the students indicated that they were more satisfied with the intelligent language acquisition model. Relying on artificial intelligence technology to construct intelligent language acquisition models can help non-native English learners improve their language acquisition ability.

Integrating an Intelligent Language Tutoring System in Teaching English Grammar

This paper describes a research project using an intelligent language tutoring system model in teaching grammar in English. The study aims to investigate the perceptions of a group of students regarding the effectiveness of this system and its service quality. This investigation is significant since it gives further insights into how Intelligent Language Tutoring Systems can help students learn grammar in a personalized manner based on their levels. Research has revealed that classroom teaching may fall short in providing customized instruction and feedback. Therefore, intelligent language tutoring systems can support adaptivity to individual learner needs. In order to assess the effectiveness of this system, the participants answered a survey questionnaire at the end of this study. The research project involves a class of adult mixed-ability students in a continuing education program at the National School of Applied Sciences in Fes, Morocco. The results demonstrate that these students have a positive attitude toward the merits of such technological tools. These advantages are mainly related to enhancing learner achievement and motivation. Based on these findings, some implications for both teaching and learning grammar can be drawn.

Research on intelligent language training model in the background of public security application

Research on intelligent language training model in the background of public security application

The characteristics of artificial intelligence model description language and its research direction

As an important branch of the development of computer science, artificial intelligence not only belongs to the basic component of computer science and technology research, but also has been widely used in computer engineering. As the basic environment for the research of artificial intelligence system, it is the focus of Chinese researchers to deeply explore the intelligent model and describe the characteristics and future development direction of language. Therefore, after understanding the development trend of artificial intelligence and intelligent language, this paper mainly studies how to use modeling elements in UML to describe infrastructure, cooperation model, information model and behavior model in business models, and describes language and algorithm generation according to intelligent models oriented to multi-objective problems, providing an effective basis for artificial intelligence language research in the new era.

Innovative Personal Assistance: Speech Recognition and NLP-Driven Robot Prototype

This paper presents the development and evaluation of a personal assistant robot prototype with advanced speech recognition and natural language processing (NLP) capabilities. Powered by a Raspberry Pi microprocessor, it is the core component of the robot's hardware. It is designed to receive commands and promptly respond by performing the requested actions, utilizing integrated speech recognition and NLP technologies. The prototype aims to enhance meeting efficiency and productivity through audio-to-text conversion and high-quality image capture. Results show excellent performance, with accuracy rates of 100% in Indonesian and 99% in English. The efficient processing speed, averaging 9.07 seconds per minute in Indonesian and 15.3 seconds per minute in English, further enhances the robot's functionality. Additionally, integrating a high-resolution webcam enables high-quality image capture at 1280 x 720 pixels. Real-time integration with Google Drive ensures secure storage and seamless data management. The findings highlight the prototype's effectiveness in facilitating smooth interactions and effective communication, leveraging NLP for intelligent language understanding. Integrating NLP-based speech recognition, visual documentation, and data transfer provides a comprehensive platform for managing audio, text, and image data. The personal assistant robot prototype presented in this research represents a significant advancement in human-robot interaction, particularly in meeting and collaborative work settings. Further refinements in NLP can enhance efficiency and foster seamless human-robot interaction experiences.

Intelligent language analysis method for multi‐sensor data fusion

AbstractLanguage intelligence analysis oriented to multi‐sensor data fusion is of great significance for language analysis in real scenarios. On the one hand, intelligent language analysis technology can greatly improve the performance of applications such as information retrieval and machine translation, and provide technical support for semantic‐level applications. On the other hand, each language has its own unique characteristics, and the advancement of the language system through language analysis technology is of great benefit to natural language analysis. In this letter, an intelligent language analysis method for multi‐sensor data fusion is elaborated. Specifically, the Kalman filter algorithm is combined to perform the first preprocessing filter fusion on multi‐sensor data. Then, the deep learning model is used to design a language analysis model using Bidirectional Long‐Short Memory Neural Networks (Bi‐LSTM) to obtain deep fusion of multi‐sensor data. In the experiment, the multi‐sensors are used to collect real language data and public language datasets for verification, and the results show the effectiveness of the method proposed in this letter in terms of syntactic label classification.

A Review of Intelligent Language Teaching Systems in Foreign Language Teaching

A Review of Intelligent Language Teaching Systems in Foreign Language Teaching

DEEP QUALITY-CONSTRAINED LSTM FOR TEXTUAL DATA ANALYSIS

The creation of language models is a vital object of natural language processing because it creates a possible distribution in a sequence of words and offers a unique representation of each occurrence of a word. Therefore, an intelligent language model can differentiate the subtle nuances of language and model its syntax and semantics. It also allows the acquisition of high-level representations. In addition, it serves as an initial model with helpful information, which can be transferred to various other word processing methods for a deeper and more complete understanding of the language. This work proposes an innovative Long Short-Term Memory (LSTM) neural network whose implementation is based on the use of an attention mechanism as a measure of alignment of the output with the input sequence and a corresponding coverage optimization mechanism. The optimization mechanism informs the model about the possible outcomes at each step which have already been produced in previous actions. The adaptation and evaluation of the system’s design parameters were tested in multidimensional and challenging data sets We were considering parametric and heuristic procedures for finding the optimal combination of hyper-parameters. Experimental results demonstrate the quality of the proposed system, revealing essential directions for the design of similar systems.