Learning Principles Research Articles

PEDAGOGICAL MONITORING OF PROFESSIONAL TRAINING FUTURE SPECIALISTS IN THE SOCIAL SPHERE IN THE PROCESS OF APPLICATION OF ELECTRONIC LEARNING

The article examines the content of pedagogical monitoring of e-learning of future social workers in higher education institutions. The modern level of e-learning in the institution of higher education has been revealed. Special forms and methods of working with students - future specialists in the social sphere are analyzed and substantiated. The list of necessary conditions for the formation of applicants for higher education with a system of theoretical knowledge, practical abilities and skills necessary for the effective realization of oneself as a professional social worker is provided; development and formation of theoretical knowledge that will contribute to the development of the student as a future specialist in the social sphere. The purpose of the article is to identify the problems of professional training and to study the special requirements for the development and implementation of electronic training courses in the process of professional training of social sector specialists capable of solving social problems. Considerable attention is paid to the problems of pedagogical interaction between the teacher and the student, based on the analysis, the strengths of e-learning are highlighted, and the expediency of its use is clarified. The main tasks are defined as follows: to identify and generalize the components of professional self-development as one of the complex forms of transformation of the inner world, which occurs in the process of self-movement of a person along his life path, on which a person knows himself, builds himself, understands the essence of his functioning; to investigate the peculiarities of vocational pre-university education as a focus for personal development on self-building, professional self-determination for the successful growth of professionals in the social sphere. Based on the results of the generalized analysis, it was concluded that the effectiveness and feasibility of e-learning takes into account not only the general principles of learning, but also the specifics of a specific field or subject. These technologies allow students to independently organize their educational process, develop independent work skills and improve their knowledge. The proposed e-learning technologies can be used in universities, faculties and departments where future specialists in social spheres are trained, as well as in advanced training courses with the aim of increasing readiness to interact with clients based on mastering a set of knowledge and skills aimed at transforming informational and reflective, professional-practical and professional-development technologies in the real conditions of the environment of the higher education institution. The conclusions confirmed the substantiation of the necessary pedagogical conditions for improving the quality of extracurricular activities in the environment of institutions of professional pre-higher education regarding future social workers in the conditions of globalization.

Development of an Educational Kit Using CAD Software for Simple Machine Learning

Traditional simple machine topic is frequently theoretical and does not provide enough hands-on practise. Students may as a result fail to comprehend the fundamentals of simple machines and their applications. This research presents an innovative educational kit for simple machine learning. Students can utilize this kit to understand the fundamental concepts behind simple machines like levers, gears, and pulleys. This study investigates the creation of a simple machine kit using CAD software as a teaching tool for fundamental mechanics. A survey study is employed as part of this technique in order to ascertain audience needs and expectations. Beginning with idea generation and followed by rigorous evaluation, the process of developing the kit focuses on aligning pedagogical viability, marketability, and profitability. The selected concept is then transformed into a detailed technical drawing specifying dimensions, components, and assembly instructions, ensuring coherence between educational goals and machine learning integration. Iterative prototyping and testing further refine the kit's design, ensuring seamless interaction between machine learning elements and LEGO-based components. This approach culminates in an innovative educational kit that effectively imparts foundational machine learning principles through an engaging platform. The simple machine kit made through this research can then be utilized as an educational tool in primary, secondary, or higher education environments to facilitate students' grasp of basic mechanics. Students learning product design may find this novel approach both enjoyable and useful in their studies.

Time-gated imaging through dense fog via physics-driven Swin transformer

Imaging through the fog is valuable for many areas, such as autonomous driving and cosmic exploration. However, due to the influence of strong backscattering and diffuse reflection generated by the dense fog on the temporal-spatial correlations of photons returning from the target object, the reconstruction quality of most existing methods is significantly reduced under dense fog conditions. In this study, we describe the optical scatter imaging process and propose a physics-driven Swin Transformer method utilizing Time-of-Flight (ToF) and Deep Learning principles to mitigate scattering effects and reconstruct targets in conditions of heterogeneous dense fog. The results suggest that, despite the exponential decrease in the number of ballistic photons as the optical thickness of fog increases, the Physics-Driven Swin Transformer method demonstrates satisfactory performance in imaging targets obscured by dense fog. Importantly, this article highlights that even in dense fog imaging experiments with optical thickness reaching up to 3.0, which exceeds previous studies, commonly utilized quantitative evaluation metrics like PSNR and SSIM indicate that our method is cutting-edge in imaging through dense fog.

Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation

During the process of urban development, there is large-scale laying of underground pipeline networks and coordinated operation of both new and old networks. The underground concrete drainage pipes have become a focus of operation and maintenance due to their strong concealment and serious corrosion. The current manual inspections for subterranean concrete drainage pipelines involve high workloads and risks, which makes meeting the diagnostic needs of intricate urban pipeline networks challenging. Through advanced information technology, it has reached a consensus to intelligently perceive, accurately identify, and precise prediction of the condition of urban subterranean drainage networks. The development process of detection and evaluation methods for underground concrete drainage pipe networks is the focus of this study. The study discusses common algorithms for classifying, locating, and quantifying pipeline defects by combining the principles of deep learning with typical application examples. The intelligent progression of information collection methods, image processing techniques, damage prediction models, and pipeline diagnostic systems is systematically elaborated upon. Lastly, prospects for future research of intelligent pipeline diagnosis are provided.

PBL hybrid: An exploration of the concept of hybrid learning in a resource-constrained university context

In this paper, we explore the concept of hybrid learning in the context of a project aiming to transform the master programmes at Gulu University in Uganda by incorporating problem- and project-based learning (PBL) and the use of digital technologies to prepare graduates to lead community transformation and improve employability. Teachers are experimenting and piloting PBL and digital technologies. Based on the teachers' experiences this paper provides insights into how PBL and hybrid learning are being enacted by teachers and learners in the context of a resource-constrained university environment. What are the challenges of bringing these principles of hybrid learning and PBL together in a resource-constrained environment? The paper understands hybrid learning as the development of a new quality of learning environments that goes beyond a mere mixture of elements, e.g. digital and analogue, on-site and off-site. In this paper we are particularly concerned with exploring how the new PBL hybrid practices are developing, with a interest in how digital tools are integrated into teaching and learning. This paper is based on the results of a co-construction workshop with teachers and researchers based on their experiences from the first pilot phase. The results of the study suggest that flexibility is a core concept in relation to PBL-hybrid. Hybrid learning involves flexibility in terms of how learners carry out educational activities, how PBL is implemented, the use of many different technologies and the use of both analogue and digital learning tools and spaces. Moreover, to achieve hybrid learning, the types of flexibility and technologies, as well as the mix of digital and analogue tools, depend on the specific context of the educational activities, as well as on the competences of learners and teachers. As such, hybrid learning needs to be seen as a concept in relation to the contexts in which it unfolds. This insight is important for networked learning, which cuts across contexts, actors, materials/spaces and digital/analogue modalities.

Emotions and Networked Learning

Emotions in networked learning have been underresearched despite their importance. The present research is one of the first few attempts to better understand adult learners’ emotional experiences in a specific networked learning context—an online doctoral programme designed and organised based on networked learning principles. This paper takes the initial step of unpacking the intricate interplay between emotions and networked learning experiences, utilising a longitudinal, mixed methods research approach to demonstrate the diversity and complexity of emotions experienced in networked learning situations. Online doctoral students (n=48) completed a questionnaire four times during the first six months of their PhD program, with an interval of five to six weeks. This is the most critical period for students to develop multiple connections promoting networked learning both within and outside the programme. They connected to new people, ideas, and resources, becoming an integral part of learning networks. The questionnaire quantitatively measured the intensity of 36 different emotions felt by each student, respectively, about their networked learning experiences and general life experiences. Participants also qualitatively reflected on the most intense emotions and wrote “why” they felt such ways. Based on quantitative data, a principal component analysis extracted five meaningful clusters of emotions and repeated-measures ANOVAs further showed meaningful patterns and changes in those emotions. The qualitative data was analysed using a constant comparative method to identify dominant themes to provide deeper insights into student emotions. Findings suggest that emotions are in a constant state of flux, reflecting the multifaceted nature of networked learning experiences, through which learners juggle multiple responsibilities and connections across different times and spaces. Two distinct categories of emotions emerged: positive and negative. Despite the challenges and complexities of online doctoral studies, positive emotions remained resilient, underscoring networked learning as a source of inspiration—with the heightened 'Intrigued' component at specific learning milestones. Negative emotions are nuanced, with negative cognitive emotions, associated with networked learning experiences, intensifying initially and decreasing as students adapt; while negative gut reactions are more prevalent in general life. However, a notable release of negative cognitive emotions in general life suggests the interplay between emotions arising from networked learning and their influence on students' broader experiences. Surprisingly, the study challenges assumptions about isolation in online learning, revealing a consistent sense of social inclusion and support within the learning network. Overall, this study provides a comprehensive understanding of emotions in networked learning, offering valuable insights for supporting adult learners.

IMPLEMENTASI TEORI BELAJAR KONSTRUKTIVISME DALAM PEMBELAJARAN MAHARAH KALAM MENGGUNAKAN COOPERATIVE LEARNING DI MADRASAH TSANAWIAH

Constructivism theory is a learning theory that is oriented towards forming the structure of thought patterns and students' understanding of the material through experience and the learning process. This form of implementation in the field, especially in learning maharah kalam in Arabic, can be supported using the cooperative learning method. This research aims to determine the implementation of constructivist learning theory in maharah kalam learning using the cooperative learning method. This type of research is field research with a descriptive qualitative approach carried out at Madrasah Tsanawiah Satu Atap Al-Hidayah, Batu City, East Java. Data collection techniques use observation techniques and interview techniques. The data analysis used is the Miles, Huberman and Saldana analysis technique with three steps, namely condensing data, presenting data, and drawing conclusions or verification. The results of this research include the following (1) implementation of constructivist learning theory in maharah kalam learning at Madrasah Tsanawiah Satu Atap Al-Hidayah, Batu City with the cooperative learning method applied according to the steps and principles of learning, so as to provide a pleasant learning experience for students . (2) the problems encountered in implementing constructivist learning theory are a lack of initiative in discussing learning material and a lack of motivation in learning Arabic, so that efforts to deal with the problem of implementing constructivist learning theory are for teachers to provide encouragement and motivation in learning Arabic and make improvements- improvements in learning.

Transforming academic librarianship through AI reskilling: Insights from the GPT-4 exploration program

The emergence of Artificial Intelligence (AI) in academic librarianship necessitates a corresponding evolution in librarians' competencies. This case study examines the GPT-4 Exploration Program at the University of New Mexico's College of University Libraries and Learning Sciences, which aimed to foster a culture of continuous learning and innovation by providing hands-on experience with advanced AI technology. Drawing on Malcolm Knowles' Adult Learning Principles, the program's structure and implementation are analyzed, highlighting the diverse range of projects undertaken by participants. The study reveals that effective AI reskilling involves cultivating a culture of continuous learning, adaptability, and collaborative exploration, anchored in a practical, hands-on approach. Participants reported significant improvements in AI literacy and confidence in applying AI tools to their work. The program's success demonstrates the potential of well-structured reskilling initiatives in empowering library professionals to navigate the evolving landscape of AI in academic libraries. The case study offers insights and strategies for other academic libraries seeking to develop AI reskilling initiatives, emphasizing the importance of fostering a supportive learning environment, addressing technical and ethical challenges, and ensuring the long-term sustainability of such programs. The GPT-4 Exploration Program serves as a blueprint for integrating AI into library services and operations.

ANALYSIS OF THE PSYCHOLOGICAL FOUNDATION OF THE IMPLEMENTATION OF LEARNING THE FOUR ARABIC LANGUAGE SKILLS

_______Abstract: This analysis is a form of research activity that aims to re-examine a data or actual situation in the hope of increasing understanding and encouraging educators' decision making to understand several areas of psychological foundation before carrying out the learning process. The focus this time is the relationship between the psychological foundation as well as its principles in learning and the relationship between the four Arabic language skills. This research seeks to understand and present from the most specific points to the most specific in the existing theme. The approach used this time is a descriptive-qualitative-based approach using the Document studies and content analysis methods. The results of this study will later provide an overview of the material and content related to how the relationship between the principles of a theory and the psychological foundation of each person with four Arabic language skills in the present and the future.

영어교육 위기 상황에서의 융합 영어영문학의 역할

This study aims to offer how English language education based on English literary works (including children's literature) can respond more actively to the current crisis in which 'Korean-style' English language education is heading towards collapse. This study also aims to help English language instructors and scholars themselves understand the pedagogical, ethical, and political aspects of their teaching and research so that learning should be at the heart of all transformative actions against the crisis in the way that English language education can help EFL learners acquire a strong sense of agency, practice critical thinking, and respond to and initiate changes against the crisis. This study 1) critically reflects on the communicative approach that has long dominated our English language teaching and learning; 2) examines the hybridity and non-convergence of the various foreign language teaching and learning principles and methodologies introduced so far and attempts to develop new convergent English language teaching principles, directions, and methodologies with a humanistic approach as the centerpiece; and finally, 3) explores the necessity, importance, and methodology of shifting to content-competence- based English language teaching. It is hoped that these efforts can be integrated to overcome the gap between English language teaching practices and the world outside the classroom, thus helping overcome the current crisis in English language education from within and outside.

Animations and food science: A new and effective approach to communicate cheese processing safety

The communication of food safety principles to actors in the informal sector has traditionally been carried out through formal training or demonstrations with human resources. Using a novel approach, an animation was developed to introduce best practices of cheese processing safety and hygiene to processors in the informal sector. The approach used the social cognitive theory (SCT) by engaging principles of visual learning for low literates by developing animations in motion, cell phone technologies and local languages to bridge communication gaps. Twenty-five cheese processors in Ghana were trained using the developed animation with a learner-paced approach. There were significant insights from the processors regarding cheese processing hygiene. The animation provided insight into acceptable modes of introducing important food processing concepts to low literate processors.

Networks of Classical Conditioning Gates and Their Learning

A research project on chemical AI, called the Molecular Cybernetics Project, was launched in Japan in 2021 with the goal of creating a molecular machine that can learn a type of conditioned reflex through the process of classical conditioning. In this project, we have developed a learning method for the network of such learning molecular machines, which is reported in this paper. First, as a model of a learning molecular machine, we formulate a logic gate that can learn conditioned reflex and introduce the network of the logic gates. Then we derive a key principle for learning, called the flipping principle, by which we present a learning algorithm for the network to realize a desired function.

“EDUPLAY”- Interactive Game Based Learning Platform

This research introduces an innovative approach to inclusive education through the development of a game-based Learning platform tailored for Persons with Disabilities (PwDs). With a focus on fostering an accessible and engaging educational environment, the platform aims to break down barriers and promote equal learning opportunities for individuals with diverse abilities. The present study investigates the use of gamification to foster commitment and engagement among users with disabilities. Two case studies demonstrating the application of gamification are provided. The first is the development of an application to teach a blind person, and the second one is the creation of an application to aid individuals with Autism Spectrum Disorder (ASD) in navigating their environment. The study reveals that universal design for learning principles can be used indirectly to adjust apps for users to utilize the software consistently. This study provides preliminary evaluations for both case studies, which were undertaken with relatively small samples. The first case study revealed that three blind individuals who took part in the review scored an average of 91.7on the system usability scale. At the same time, the second case study involving the observation of a single individual with ASD also revealed that utilizing the designed app improved performance. Despite the limited sample size, the findings suggest that gamification may effectively encourage and generate commitment among the users with disabilities.

Can school become a non-adultist institution?

To answer the question of whether school can become a non-adultist institution, this article examines the unequal adult–child (teacher–pupil) power relations that characterize school under the framework of bourgeois-capitalist society and that are upheld by certain functions, methods, norms and knowledge standards. Under the influence of the anti-authoritarian youth protest movements from the 1960s onwards, overt power in school (e.g. by means of corporal punishment) has been criticized and, in most countries, abolished. However, power imbalances between teachers and pupils have not disappeared, but rather developed towards a more subtle, covert power, e.g. by framing certain expectations. The hierarchical top-down and one-way learning principles of school institutions are mostly left uncontested. This article does not content itself with simply stating that schools repeatedly reproduce adultism under these societal conditions, but also discusses possible strategies and interventions that could shift unequal power relations and ultimately overturn them. It looks at various concepts and alternative forms of education critical of adultism that have emerged since the beginning of the 20th century both within and outside the state school system. It further explores the contradictions that should be expected to arise in their implementation within the framework of existing educational institutions. Particular attention is paid to the respective possibilities for action on the part of teachers and pupils. We offer a decisive challenge to teachers to rethink how they deal with their power, which is always derived and fragile, and how they can contribute to pupils empowering themselves and significantly influence learning and decision-making processes at school.

Artificial intelligence-based adaptive anomaly detection technology for IaaS cloud virtual machines

As infrastructure-as-a-service clouds quickly grow, an increasing number of businesses and people are moving their application development to the cloud. The purpose of the research is to solve the problem of identifying memory anomalies in cloud virtual machines and improve the accuracy of the model in detecting abnormal situations. This paper presents a model for detecting virtual machine anomalies in IaaS cloud platform. The model considers the unique properties of monitoring metrics as time-series data and proposes an approach based on four important virtual machine monitoring metrics. The study also develops an adaptive anomaly detection system based on deep Q-network algorithms and migration learning principles for the variety of VM monitoring data in the cloud. The testing findings reveal that utilizing a Zoom layer with a 2-kernel size can increase detection accuracy to 96.7%. This demonstrates that a portion of the experimental data can extract the temporal features using the Zoom layer and different kernel sizes. The research model for anomaly detection had a classification accuracy of 99.8%. The deep Q-network model’s final anomaly detection accuracy varies from 96.7 to 98.6%. The outcomes of the research improved the system’s security and dependability, showed the worth of the overall framework design, and significantly decreased the number of resources needed for system operation and maintenance.

Design and Application of English-Chinese Bilingual Teaching Model Based on Multimodal Learning

In the globalized landscape of education, the demand for effective bilingual teaching models has become increasingly prominent, particularly in English and Chinese instruction. this study introduces the Bilingual Teaching Random Conditional Field (BTRCF) as a novel approach to designing and implementing an English-Chinese Bilingual Teaching Model based on Multimodal Learning. The principles of multimodal learning, which integrates various modes of communication such as visual, auditory, and kinaesthetic, the BTRCF model aims to provide students with a comprehensive and immersive language learning experience. The BTRCF model capitalizes on the complementary strengths of both English and Chinese languages, aiming to cultivate bilingual proficiency while fostering cross-cultural understanding among students. By incorporating elements from each language and leveraging multimodal learning strategies, the BTRCF model provides students with a comprehensive language learning experience that extends beyond mere linguistic proficiency to encompass cultural appreciation and intercultural competence. By capitalizing on the complementary strengths of both languages, this model seeks to foster bilingual proficiency while promoting cross-cultural understanding among students. with the BTRCF model have shown promising results, with an average increase of 25% in students' bilingual proficiency scores compared to traditional teaching methods. Additionally, qualitative assessments reveal high levels of student engagement and satisfaction with the multimodal learning approach employed by the BTRCF model, indicating its potential to revolutionize bilingual education practices.

Optimization and Application of English Word Memory Algorithm Based on Reinforcement Learning

The optimization and application of an English word memory algorithm based on reinforcement learning involve refining a system that utilizes reinforcement learning techniques to enhance vocabulary retention. By leveraging reinforcement learning, the algorithm can adapt its strategies for presenting and reviewing English words based on users' responses and performance. This adaptive approach enables personalized and effective learning experiences, wherein the algorithm adjusts the frequency and timing of word repetitions to maximize memorization efficiency. Applications of this optimized algorithm span language learning platforms, educational apps, and personalized tutoring systems, offering learners tailored support to strengthen their English vocabulary acquisition and retention skills. This paper introduces the Spider Swarm Optimized English Word Memory Algorithm (SSOEWMA) algorithm, a novel approach aimed at optimizing language processing and learning tasks. Leveraging principles of reinforcement learning and swarm intelligence, SSOEWMA demonstrates its effectiveness in enhancing various aspects of language processing, including word difficulty assessment, sentence estimation, memory retention, and student performance evaluation. Through a series of experiments and evaluations, we showcase SSOEWMA ability to optimize dataset attributes, accurately estimate sentence characteristics, and improve memory retention performance. Simulation results achieved an average word recall accuracy of 85% across multiple evaluations. Through its utilization of reinforcement learning principles and swarm intelligence, SSOEWMA significantly improved memory retention performance, reducing recall latency by an average of 20% compared to baseline measures. Additionally, the algorithm demonstrated strong learning efficiency, converging to optimal solutions with a speed increase of 30% compared to traditional methods.

An Investigation of Brain-Based Learning Principles in Today's Changing Conditions

The increasing prevalence of brain research and the fact that the findings reveal important arguments about the learning process have led to the proliferation of brain-based learning studies. Brain-based learning principles, introduced by Caine and Caine in 1991, are frequently used in the literature and shed light on educational research. However, in today's changing conditions, educational environments have remarkably differentiated, the opportunities for learners have increased more than ever before, threats such as the Covid-19 pandemic and natural disasters have directly affected education, and technological advances have made a breakthrough with artificial intelligence. There are studies evaluating 12 accepted and frequently used brain-based learning principles in the literature, but there is no review study that addresses brain-based learning principles in today's changing conditions. Therefore, this article aims to examine the principles of brain-based learning in today's changing conditions. Increasing threats in the rapidly changing world pose a risk to brain-based learning. Also, increasingly common helicopter parenting and artificial intelligence hinder brain-based learning, although they have some advantages. However, on the other hand, the fact that technological developments make educational environments richer and increase the opportunities for learners greatly facilitates brain-based learning. Thus, brain-based learning principles have become much more meaningful and gained great importance in today's world.

Exploring the Optimization Model of University English Blended Teaching Mode Combined with Deep Learning

With university English blended teaching mode combined with deep learning marks a significant advancement in language education. With integrating deep learning techniques into blended teaching approaches, educators can leverage vast amounts of educational data to enhance the effectiveness and personalization of English language instruction. Deep learning algorithms analyze student interactions, performance data, and learning preferences to tailor instructional content and activities to individual needs. This model enables educators to create dynamic and adaptive learning experiences that blend traditional classroom instruction with online resources and digital tools. The paper presents a comprehensive examination of blended teaching methodologies within the realm of English education, integrating principles of deep learning to enhance learning outcomes. Through a series of experiments and analyses, the study investigates the effectiveness of various teaching components, the utilization of online resources, and the application of diverse deep learning architectures. Findings underscore the significance of a balanced integration of traditional pedagogies and digital resources, with online materials emerging as a pivotal aspect of the teaching strategy. The study conducted experiments evaluating the effectiveness of blended teaching methodologies in English education, with numerical scores ranging from 4.2 to 9.0 across different parameters. Results indicated that experiments with higher scores for traditional teaching effectiveness (ranging from 6.5 to 9.2) and deep learning integration (ranging from 3.9 to 9.1) tended to yield superior overall learning outcomes (ranging from 4.3 to 9.0). Furthermore, online resources received consistently high weighted scores (ranging from 7.2 to 9.5), underscoring their significant contribution to the overall teaching strategy.

Principles of foreign language teaching based on artificial intelligence technologies

Importance. The widespread use of artificial intelligence technologies affects the language education system. In the few years since the advent of artificial intelligence technology, it has transformed from a theoretical concept into a dynamic force that changes ideas about classical language education. The transition to digital didactics defines new types of relationships between participants in the educational process: the teacher, the student, the educational environment and new technologies. Accordingly, the role of the teacher and the content of didactic and methodological principles of teaching a foreign language using artificial intelligence technologies are changing. The integration of artificial intelligence technologies into the process of teaching a foreign language, on the one hand, indicates the intensification of the educational process and automation of a number of pedagogical tasks, and, on the other hand, the imperfection of new technologies and potential risks and challenges when using them in practice. The main purpose of the research is to define and describe the principles of teaching a foreign language based on artificial intelligence technologies, acting as a full-fledged individual assistant for each student.Research Methods. To conduct research in the field of linguodidactics, an integrated approach is used, including theoretical and empirical methods. At the first stage of the research, theoretical methods are used, including the study of scientific literature on the research topic, analysis, generalization and classification of the information received. At the second stage of the study, empirical methods are applied, including observation and description.Definition of Concepts. Two key concepts of “artificial intelligence technologies” and “neural networks” are considered, and neural networks are classified by type of architecture and scope of application.Results and Discussion. The use of hybrid forms of learning makes it possible to use artificial intelligence technologies as an effective tool for teaching a foreign language right now. Artificial intelligence technologies play a leading role in digital didactics, allowing you to personalize the learning process and enhance the importance of students’ learning autonomy. Digital didactics, based on traditional didactic and methodological principles, involves the implementation of the following principles of learning: the principle of expediency, the principle of contextual learning, the principle of interactivity, the principle of accessibility, the principle of personalization of learning, the principle of multimodality, the principle of gamification, the principle of inclusive assessment.Conclusion. A system of principles of teaching a foreign language based on artificial intelligence technologies is presented, taking into account the peculiarities of the organization of the educational process in relation to digital didactics. It is worth noting that this system of principles is open and can be supplemented with new learning principles, as well as the content of these principles may change with the development of artificial intelligence technologies. The results obtained can be used in further research on the integration of artificial intelligence technologies into language education, as well as in the methodology of teaching foreign languages based on modern technologies.