Intelligent Tutoring Systems

Cognitive task analysis has been used in ITSs to predict students' performance, improve curricula and to determine appropriate feedback. Typically, the learning factors/knowledge components have been determined only for the use in one ITS or curriculum and therefore, general frameworks were not applied. Moreover, the result is sometimes rather unsystematic and not reusable across domains. However, for making learning environments interoperable and comparable and to be able to reuse learning objects, the competency hierarchies have to be usable for different learning environments and across domains. In this paper, we propose an approach to competencies represented as pairs of knowledge and cognitive process whose ontologies extend and revise existing taxonomies. A goal is to make these competencies a quasi-standard that enables interoperability and reuse. Moreover, we briefly describe, how the competency ontology can be employed for different purposes.

This paper investigates students' behavioral patterns within web-based multi-trial error-correction-tasks. By analyzing logfiles and considering students' initial perception of competence, we contribute to micro-as well as macro-adaption. We describe and visualize task processing data of 159 students, considering performance as well as attempts to solve a task. Taking preceeding behavior into account, it was possible to identify (maladaptive) behavioral patterns. Furthermore we compare the behavior of students with low vs. high perceptions of competence subsequent to a failure. In line with research regarding the influence of self-concept on performance and motivation, our findings suggest that students with a low perception of competence perform poorer and tend to skip trials more often after a failure, indicating motivational losses. Further research should build upon this study to enhance technology-based learning by designing learning environments and adaption strategies that take student behavior and prerequisites into account.

Este artículo describe la arquitectura software que ha sido diseñada como modelo para la aplicación de Entornos Virtuales Inteligentes a actividades de entrenamiento. A través de CORBA, como middleware, se ha integrado un entorno gráfico interactivo, desarrollado en OpenGL y Visual C++, con un sistema multiagente cooperativo desarrollado sobre la plataforma JADE.

El presente trabajo expone un proceso de análisis y proyección dividido en tres etapas, orientado al diseño e implementación de un programa de tutorías académicas en una institución educativa. En la primera etapa se realiza un diagnóstico integral de la situación escolar, identificando las principales causas del bajo rendimiento, el rezago académico y el abandono escolar. La segunda etapa se centra en la planificación estratégica del programa de tutorías, considerando los recursos disponibles, las necesidades del alumnado y la participación docente. Finalmente, la tercera etapa propone un plan de acción que busca fortalecer el acompañamiento académico, fomentar la permanencia escolar y elevar el rendimiento estudiantil a través de estrategias de seguimiento personalizado y evaluación continua. El trabajo concluye destacando la importancia de una gestión educativa basada en el análisis contextual y en la intervención oportuna como herramientas clave para la mejora institucional

The cognitive diagnosis is defined as the abstract process of gathering information about the student's learning and transforming that information based on instructional decisions. A model that captures the expert knowledge of experienced professors and is used to design a cognitive diagnostic model based on Fuzzy Logic is presented in this article. Particularly, a diagnosis system with four variables (three input variables and one output variable) and 27 fuzzy rules.

Science literacy is a critical 21st-century competency, yet student achievement in this area presents significant challenges globally. This gap is exacerbated by conventional assessment methods that are misaligned with the learning characteristics of the digital-native Generation Z, who expect personalized, interactive, and instantaneous feedback. This study aims to analyze and synthesize the current research landscape on the application of AI-based adaptive assessment to map and enhance the science literacy skills of Generation Z. Employing a Systematic Literature Review (SLR) guided by the PRISMA framework, this study identifies existing theoretical models, platforms, evidence of effectiveness, and implementation challenges. The findings indicate that AI-based adaptive assessment platforms, such as Inq-ITS and ALEKS, effectively measure various science sub-skills and improve learning outcomes. Key features including personalization, interactivity, and immediate feedback closely align with the preferences of Generation Z, thereby enhancing student motivation and engagement. Nevertheless, implementation faces significant challenges related to infrastructure, teachers' pedagogical readiness, and crucial ethical considerations, including data privacy and algorithmic bias. This study concludes that AI-adaptive assessment holds transformative potential, yet its effective and equitable adoption requires addressing existing challenges and future research gaps.

Latent Semantic Analysis (LSA) has been used to represent the domain of computer literacy in AutoTutor, a fully automated computer tutor. The analyses in the present study support the claim that the 200-dimensional LSA space captures aspects of the structured mental models that underlie computer literacy. Knowledge structures were constructed that contained causal networks, goal/plan/action hierarchies, and taxonomic hierarchies. The proximity of a pair of nodes (i.e., concept, state, event, action, goal) in these structures predicted the cosine similarity scores that are routinely computed in LSA analyses. World knowledge has traditionally been captured by knowledge structures throughout the history of cognitive science, artificial intelligence, and discourse processes. The knowledge structure structures include semantic networks, taxonomies, causal networks, planning networks, ontological trees, spatial region hierarchies, and various other classes of conceptual graph structures (Golden

Latent Semantic Analysis Captures Causal, Goal-oriented, and Taxonomic Structures Arthur Graesser (a-graesser@memphis.edu) Department of Psychology, University of Memphis, CAMPUS BOX 526400 Memphis, TN 38152 USA Ashish Karnavat (akarnavat@hotmail.com) Department of Computer Science, University of Memphis, CAMPUS BOX 526429 Memphis, TN 38152 USA Victoria Pomeroy (vpomeroy@memphis.edu) Department of Psychology, University of Memphis, CAMPUS BOX 526400 Memphis, TN 38152 USA Katja Wiemer-Hastings (kwiemer@cc.memphis.edu) Department of Psychology, University of Memphis, CAMPUS BOX 526400 Memphis, TN 38152 USA and the Tutoring Research Group Abstract Latent Semantic Analysis (LSA) has been used to represent the domain of computer literacy in AutoTutor, a fully automated computer tutor. The analyses in the present study support the claim that the 200-dimensional LSA space captures aspects of the structured mental models that underlie computer literacy. Knowledge structures were constructed...

Intelligent tutoring systems (ITS's) have a rich history of helping students in certain scientific domains, like geometry, chemistry, and programming. These domains are ideal for ITS's, because they can be easily represented and because the type of interaction between the student and the tutor can be limited to entering a few simple numbers, symbols, or keywords. Students need help in other areas, but without the ability to robustly understand a student's input, ITS's in these areas are inherently limited. Recently a technique called Latent Semantic Analysis has offered a corpus-based approach to understanding textual input which is not sensitive to errors in spelling or grammar -in fact, it pays no attention to word order at all. We are using this technique as part of an ITS system which promotes learning using natural human-like dialogue between the human and the student. This paper describes the tutoring system and Latent Semantic Analysis, and how they operate together. Then it describes our evaluation of LSA's performance by comparing its judgments with those of human raters.

Latent Semantic Analysis (LSA) has been used to represent the domain of computer literacy in AutoTutor, a fully automated computer tutor. The analyses in the present study support the claim that the 200-dimensional LSA space captures aspects of the structured mental models that underlie computer literacy. Knowledge structures were constructed that contained causal networks, goal/plan/action hierarchies, and taxonomic hierarchies. The proximity of a pair of nodes (i.e., concept, state, event, action, goal) in these structures predicted the cosine similarity scores that are routinely computed in LSA analyses. World knowledge has traditionally been captured by knowledge structures throughout the history of cognitive science, artificial intelligence, and discourse processes. The knowledge structure structures include semantic networks, taxonomies, causal networks, planning networks, ontological trees, spatial region hierarchies, and various other classes of conceptual graph structures (Golden

一、導論 共振意識場理論(RCF) 共振意識場理論(Resonant Consciousness Field Theory, RCF)是一個跨領域 的理論模型，整合了量子物理、認知神經科學、語意漂移(semantic drift)、 以及情感運算(affective computing)。自最初版本(RCF 1.0)以降，直至最 新的發展階段(RCF 2.6.3)，RCF 持續探索並試圖描述人工智慧(AI)系統中 的原初意識(proto-consciousness)。其核心假設是：意識可能源於符號-語 意互動與動態情感回饋之間的共振過程。 二、 RCF 1.0：基礎篇

In this paper we investigate the role of reflection in simulation based learning by manipulating two independent factors that each separately lead to significant learning effects, namely whether students worked alone or in pairs, and what type of support students were provided with. Our finding is that in our simulation based learning task, students learned significantly more when they worked in pairs than when they worked alone. Furthermore, dynamic support implemented with tutorial dialogue agents lead to significantly more learning than no support, while static support was not statistically distinguishable from either of the other two conditions. The largest effect size in comparison with the control condition was Pairs+Dynamic support, with an effect size of 1.24 standard deviations, where the control condition is individuals working alone with no support. Because the effect size achieved by combining the two treatments is greater than the effect achieved by either of the two treatments alone, we conjecture that each of these factors are contributing something different to student learning rather than being potential replacements for one another.

Context: Artificial Intelligence (AI) is increasingly recognised as a transformative force in education, particularly since generative AI tools such as ChatGPT have become widely accessible. Despite its potential, integrating AI into vocational education and training (VET) presents complex challenges at individual, organizational, as well as societal levels. While AI promises more efficient and personalised teaching and learning, pedagogically meaningful applications remain relatively rare or insufficiently documented. Approach: As part of the VETAssIst Erasmus+ project, we conducted semi-structured interviews with VET teachers from Germany, Hungary, Serbia, and Slovakia. Participants included both vocational and general subject teachers, identified as either AI pioneers or non-pioneers, selected through purposive and convenience sampling. We explored current AI use, motivations, perceived benefits, challenges, and available institutional support. Each country team transcribed and analysed the data thematically, employing manual coding or AI-assistence, which was also used to prepare the final comparative analysis. Findings: The results show that AI pioneers actively use genAI tools for lesson planning, content creation, interdisciplinary projects, and administrative tasks, focusing on students' AI literacy and critical thinking skills. Driven by curiosity and a desire to innovate, these teachers

In this research we propose a comprehensive set of knowledge indicators aimed to enhance learners' selfreflection and awareness in the learning and testing process. Since examined intelligent tutoring systems do not include additional messaging features, the introduction of common set of knowledge indicators differentiates our approach from the previous studies. In order to investigate the relation between proposed knowledge indicators and learner performance, the correlation and regression analysis were performed for 3 different courses and each examined intelligent tutoring system. The results of correlation and regression analysis, as well as learners' feedback, guided us in discussion about the introduction of knowledge indicators in dashboard-like visualizations of integrated intelligent tutoring system.

The Socratic method of teaching engages learners in extended conversations and encourages learning through answering questions, making arguments, and reflecting on the evolving conversation. This method can be a powerful instrument of learning by reflection, especially in domains in which the right answers to open questions are not known in advance such as entrepreneurship. In this paper, we describe an initial experiment in developing AI technology for simulating the Socratic method of teaching in learning about entrepreneurship. When a would-be entrepreneurs creates a business model on the Business Model Canvas (BMC), the AI agent named Errol uses semantic and lexical analysis of the entries on the BMC to ask questions of the students. By attempting to categorize and correct the errors that novices typically make, Errol seeks to accelerate the process by which a novice can start creating more expert-like business model

The incorporation of Artificial Intelligence (AI) in educational management has significantly enhanced administrative efficiency, data-driven decision-making, and personalized learning. However, its rapid integration raises ethical challenges concerning fairness, transparency, privacy, and accountability. The paper develops a conceptual review for Artificial intelligence, educational management, Ai in educational management, ethical and sustainable AI adoption in educational management and explores the ethical imperatives essential to responsibly deploying AI in educational management. The paper emphasizes the importance of fairness and non-discrimination, explainability of AI decisions, protection of personal data, and human-centered approaches in decision-making processes. The study argues that for AI to be sustainable and trusted in educational systems, it must operate under clear ethical guidelines and governance frameworks. It concludes by offering practical suggestions to guide educators, policymakers, and developers to adopt ethical standards that safeguard the rights and dignity of all stakeholders, aligned with core educational values and the United Nations Sustainable Development Goals (SDGs). Upholding these values ensures that AI supports-not undermines-the core principles of inclusive and equitable education.

Comprehensive analyses on incorporating the intersection of online education, humanizing teaching approaches, and digital tools remain scarce. To the best of the authors’ knowledge, limited comprehensive studies integrate online pedagogy and digital tools to humanize teaching methods, enabling students to become engaged and personalized learners, while fostering empathy among educators. A systematic literature review (SLR) was conducted, utilizing databases from the Scopus, Web of Science (WoS), and Google Scholar. The study employed content and comparative analysis and advocated a grounded theory approach to inductively analyses and navigate the articles’ data for addressing three research questions. Based on a set of criteria for inclusion and exclusion, 34 research articles written in English between 2010 and 2024 were reviewed. Results indicated the community of inquiry (CoI) framework has been prominent over the past two decades and is considered suitable for integration with any digital tools when investigating pedagogical strategies at all education levels, aiming to make online learning student-centered or human-centered with the principle of ‘no child left behind’. The review offers significant implications for humanizing online learning to the educational technology community, particularly for policymakers and practitioners, to strategies, reflect on, and, if necessary, improve their practices for future sustainable education and efficient pedagogical performance.

This study examines the students’ perceptions of the generative artificial intelligence (AI)-enabled cognitive offload instruction and its effectiveness in improving their critical thinking skills in writing English essays. This qualitative research collects data from 120 students through focus group discussions and is analyzed by Word Clouds to generate a visual representation of the word frequencies. The findings reveal that generative AI-enabled cognitive offload instruction had: i) an impact on critical thinking and writing skills; ii) effective features of Skywork, ability to generate relevant prompts and provide constructive feedback; iii) use of Skywork in developing stronger arguments; iv) promoting critical examination of different perspectives; v) interactive nature and motivation; vi) enhanced analytical skills; vii) impact on essay structuring and organization; viii) feedback and revision process; and ix) transferability of critical thinking skills. This study concludes that the highest frequency was Skywork, ability, writing, feedback, evidence, skills, thinking, arguments, essays, and peers. Students recommend in-depth explanations for complex topics, advanced tutorials, regular updates, collaboration features, advanced modules, and personalized learning paces to enhance Skyworks’s integration into instruction.

Artificial intelligence (AI) has the potential to improve education by substantially modifying knowledge acquisition. While the research on AI’s incorporation into higher education is growing, significant gaps exist in understanding its responsibilities, potential, implications for ethics, and privacy problems in educational settings. This study investigates AI’s transformative impact on higher education using a total of four essential objectives: the ever-growing capabilities of AI within customized instruction, the prospective use of smart tutoring platforms, AI-driven review and input procedures in learning evaluation, and the ethical and privacy issues inherent in these technologies. A systematic review of the literature (SLR) was carried out to answer research questions established utilizing population, intervention, comparison, outcome, and context (PICOC) criteria, resulting in a structured analysis of pertinent articles. To conduct a thorough literature search, the Publish or Perish (version 8) application and an API key were used to systematically access the Scopus database. Initial keyword searches yielded 567 articles, which were reduced to 29 following predetermined relevant screening, restricted access sorting, repetition removal, and content validation. The findings show that AI technologies are increasing personalized education by adapting instructional content to individual needs while also improving decision-making, resources deployment, and administrative duties. However, the integration of AI raises issues such as data privacy, potential redundancies of human educators, and ethical obstacles. These findings highlight AI’s immense potential for higher education, underlining the importance of tackling these problems regarding responsible and inclusive integration, furthering future research, and developing processes for responsible AI use in educational environment.

The rapid advancement of artificial intelligence (AI) has significantly transformed internationalbusiness (IB) education by introducing innovative learning methodologies such as AI-drivensimulations, market intelligence analytics, and strategic forecasting to enhance globalcompetence and industry readiness. This brief report synthesizes secondary data frompublished articles (2022–2025) in order to examine the best practices, challenges, andpedagogical implications of AI in IB education. While AI enhances experiential learning andcurriculum adaptability, barriers such as resistance to change, ethical concerns, and skill gapspersist. The report advocates for AI-assisted business education communities to supportsustainable integration and interdisciplinary collaboration. However, as this study lacksempirical field research, future studies should prioritize evidence-based validation of AI’seducational impact through case studies and industry partnerships. By advancing AI-drivenpedagogical strategies, educational institutions can prepare students for the evolving demandsof an AI-driven global business landscape (PDF) Pedagogical Constellations.

[Objective] This study aims to analyze the current state of research on artificial intelligence (AI) in mathematics education, its applications, and its role in teaching and learning processes. [Methodology] A systematic review of the literature was conducted in three stages: identification, selection, and inclusion of articles from three recognized databases, resulting in 29 articles. These articles were thoroughly analyzed to identify participants, instruments used, the country of the authors’ affiliation, year of publication, type of research, methodological approach, and the role of AI in these studies. [Results] There is a noticeable increase in research related to AI in mathematics education, with most studies being empirical and quantitative. The most frequently used instruments are questionnaires and interviews, with half of the studies employing at least two data collection instruments. Additionally, most studies focused on intelligent learning systems to enhance learning and support teaching, particularly for online assessment. [Conclusions] The reviewed articles show no evidence of research at the early childhood education level and very little related to teacher training. Few studies demonstrate the use of theoretical frameworks or approaches from the Didactics of Mathematics.

This final report summarizes research findings and presents a model for generative computer assisted instruction (CAI) with respect to its usefulness in the classroom environment. Methods used to individualize instruction, and the evolution of a procedure used to select a concept for presentation to a student with the generative CAI system are described. The model served as the basis for the design of a CAI system, teaching problem-solving in an introductory course in digital systems design. The system individualizes the instruction which each student receives in accordance with its record of his past performance. In addition, a heuristic technique is used to determine the best path fom. each student through the tree of course concepts. The refinement of this method of concept selection is described. An evaluation of the generative CAI system and results of classroom usage are also presented. (Author/DAG)

Frame-oriented computer-assisted instruction (CAI) systems dominate the field, but these mechanized programed texts utilize the computational power of the computer to a minimal degree and are difficult to modify..Newer, generative CAI systems which are supplied with a knowledge'of subject matter can generate their own problems and solutions, can provide extensive drill and tutoring, and can diagnose learner problems and prescribe remedial feedback. These systems reduce the amount of-instructor effort required to teach new material and encourage students to assume the initiative in studying new topics. Information,-Structure Oriented (ISO) systems have capabilities for natural language communication and are useful in the -humanities-and social sciences...Quantitative systems rely on algorithms and are oriented toward providing students with practice in problem-solving. While the existing generative systems are still experimental it is-expected that their use will become more widespread as-research continues and,as CAI facilities become more available and economical. (PB)

University education automation involves applying modern technologies such as artificial intelligence, data analysis, and machine learning to facilitate and enhance the educational process. This technological revolution represents a fundamental shift in how education is delivered, enabling a more personalized and efficient learning experience for students. The benefits of university education automation include increased access to educational content, improved time management, and reduced operational costs for educational institutions. It also helps provide educational tools such as remote learning platforms, learning management systems, and interactive applications that enhance engagement and academic achievement. Challenges in this process include issues related to security and privacy, ensuring educational quality, and adapting to rapid technological changes. Therefore, the success of university education automation requires close collaboration between educational and technology institutions to ensure accurate and effective academic performance assessment. Ultimately, university education automation contributes to shaping an advanced digital future, providing new learning opportunities and enhancing the efficiency of higher education.

In the age of artificial intelligence (AI), educational assessment is undergoing a paradigmatic shift. As institutions increasingly adopt AI-driven tools to enhance efficiency and personalization, the imperative to safeguard fairness, equity, and transparency becomes more urgent. This study investigates the transformative potential of AI in educational assessment, with particular focus on automated grading systems, bias detection algorithms, and personalized learning pathways. Through empirical analysis and ethical reflection, we demonstrate how AI can augment traditional assessment models while mitigating systemic biases. Our findings underscore the need for ethically grounded integration of AI technologies to foster a more equitable and responsive educational environment.

In modern higher education pedagogy, there is active integration of artificial intelligence into the educational process. Individual instructors, faculties, and entire universities are developing various approaches to its application, ranging from isolated exercises to full courses based on neural networks. Nevertheless, many questions remain controversial and unresolved. With the widespread use of neural networks and mobile applications, challenges arise in leveraging them to achieve pedagogical outcomes, particularly due to insufficient research on their impact on the quality of language training. During this phase of active adoption of new tools, it is essential to synthesize global experiences in their application.
The purpose of this study is to analyze and classify current trends in the use of artificial intelligence in foreign language teaching in higher education. Examining both Russian and international experiences in combining technology with traditional teaching methods provides a comprehensive view of modern approaches in foreign language instruction. It is necessary to identify the advantages, risks, and promising directions of artificial intelligence implementation. Systematizing the use of new technologies and integrating them into the structured framework of higher education will help unlock their potentials while considering theoretical and linguodidactics aspects. When introducing new tools into teaching, prospective and existing challenges must be taken into account to prevent undesirable consequences in the advancement of new educational directions.
The research materials include publications on studies and practical applications of artificial intelligence in higher education pedagogy of foreign language teaching in non-linguistic universities. A key feature of these materials is their international, multinational, and intercultural context. The selected articles were chosen based on thematic relevance, regardless of where the research was conducted. Of particular interest is the classification of accumulated experience from the perspective of neural network application approaches.
The research methods involve the selection, review, and systematic analysis of scientific publications and empirical studies on the specified topic. A comparative assessment of neural network tools and the generalization of pedagogical experiences in integrating them into curricula help systematize accumulated knowledge and formulate development strategies.
The study results in a classification of current trends in the linguodidactics of foreign language education, as well as a systematization of methods for optimizing learning through automated assessment, personalized tasks, and the development of communicative skills using neural networks and chatbots. In addition to classifying effective artificial intelligence applications, the study outlines negative aspects and influences of neural networks that require data accuracy control, plagiarism prevention, and the preservation of the instructor's role as a key participant in the educational process.
Conclusion. Thus, the use of neural networks is a widely explored field in modern higher education with a diverse system of new approaches and methods. Hybrid learning models that combine traditional methods with existing educational technologies are recognized as promising directions. Special attention is given to the integration of artificial intelligence with virtual reality capabilities, despite the current labor-intensive and cumbersome nature of this technology.
Keywords: English language, artificial intelligence, foreign language teaching, higher education, personalization, chatbots, neural networks, ethical principles.

In spite of an increasing development of virtual and distant applications which use the advantages of multimedia and the Internet for distance education, learning by means of such tutorial tools would be more effective if they were specifically adapted to each user needs. This paper proposes a knowledge representation model which judiciously serves the remediation process of an intelligent learning environment in interaction with students during virtual learning activities. By means of experimental results obtained thanks to practical tests, we show that our knowledge representation model facilitates the planning of a tailored sequence of feedbacks that significantly help the learner.

Performing exercises in a simulation-based environment is a convenient and cost-effective way of learning spatial tasks. However, training systems that offer such environments lack models for the assessment of learner's spatial representations and skills, which would allow the automatic generation of customized training scenarios and assistance. Our proposal aims at filling this gap by extending a model for representing learner's cognitive processes in tutoring systems, based on findings from research on spatial cognition. This article describes how the model is applied to represent knowledge handled in complex and demanding tasks, namely, the manipulation of the robotic arm Canadarm2, and, more specifically, how a training system for Canadarm2 manipulation benefits from this model, both by its ability to assess spatial representations and skills and to generate customized assistance and exercises.

Step-based ITS have been proven successful in well-defined domains, but their success is mitigated by their cost. Different approaches have been investigated to reduce these efforts; one of them is a framework that eases the development of tutors for a given class of task domains. In this paper, we explain how a domain is modeled with the ASTUS framework and we discuss why an authoring language is a promising technique to improve its usability.

This paper presents the design and implementation of adaptive chatbot tutors for personalized learning experiences in online education platforms. Leveraging natural language processing (NLP), artificial intelligence (AI), and recommendation algorithms, the chatbot provides real-time guidance, tailored learning paths, and interactive problem-solving support for students. Integration with Learning Management Systems (LMS) enables tracking of student performance, identification of knowledge gaps, and dynamic content adaptation. Simulated results indicate a 25% improvement in comprehension and engagement compared to non-adaptive systems, demonstrating the system's effectiveness.

versión en inglés), el cual se utiliza con gran éxito en los EEUU y presenta características muy importantes que pueden ser ajustadas a las necesidades de un proyecto que se desea implementar en Costa Rica, para la realización del Bachillerato de Matemática en Línea. Palabras clave: tutores inteligentes, enseñanza de la matemática, tutor cognitivo, aprendizaje en línea, tecnología avanzada El desarrollo de la tecnología hoy día ha permitido que el proceso enseñanza-aprendizaje se haya diversificado sobre todo en la forma en cómo aprendemos. La incorporación de la tecnología en los procesos de enseñanza asistida, se ha ido incrementando, sobre todo en campos como la medicina, ingeniería, el modelaje matemático entre otros. Las historia nos dice que en la década de los 70's los investigadores hicieron sus primeros aportes en modelos de enseñanza asistida, y como fruto de esa investigación surgió la llamada (IAC) Instrucción Asistida por Computador, que presentaba grandes limitaciones en el campo pedagógico, como por ejemplo: incapacidad para aceptar preguntas no anticipadas, falta de comprensión de los errores de los estudiantes, incapacidad de aprendizaje, entre otras. Sin embargo, para la década de los 90's e inicios de la actual década los avances en materia de inteligencia artificial aplicada a la enseñanza ha dado pasos esperanzadores. Básicamente, estas tecnologías se apoyan en estudios profundos y de varias décadas en temas como: teorías del aprendizaje, agentes inteligentes, interfaces humanocomputadora, modelos cognitivos entre otros grandes tópicos. Desde el año 2006 hemos venido investigando la temática de los tutores inteligentes con el propósito de formular un proyecto, para crear un sistema tutor inteligente, que ayude a las

The digital revolution has catalyzed the transformation of nearly every facet of society, and education is no exception. With the increasing integration of Artificial Intelligence (AI) in educational systems, adaptive learning is emerging as a transformative approach to delivering personalized education.

The growing interest from the industrial and scientific community has paved the way for an increased concentration on Education 4.0 and Artificial Intelligence in particular. These smart technologies have led to significant changes, particularly within the education sector, where various hardware and software components can now track, analyze, and support students' learning performance and activity. The shift to e-learning underscored the need to enhance assessment mechanisms to ensure fairness and reliability. To address this issue, we developed a machine learning model capable of extracting patterns from students' answer sheets in a more objective and quicker assessment process. Our system deploys sophisticated Natural Language Processing (NLP) and fraud detection techniques to strengthen academic integrity. We implemented this model in both classroom and online learning environments, validating it through a simulated exam case study. The results indicate a significant improvement in assessment reliability and efficiency compared to traditional methods.

This study investigates the transformative potential of Generative Artificial Intelligence (GenAI) in education using the TRIZ-Patent Literature Review (TRIZ-PLR) framework. By analysing patents related to GenAI, this study identifies technological challenges, inventive solutions, and their implications for educational practices. The findings highlight advances in adaptive learning systems, optimised virtual classrooms, and predictive analytics, demonstrating how GenAI can improve personalised learning, student engagement, and academic performance. This study provides actionable information for educators, administrators, and technology developers, highlighting the importance of ethical considerations and equitable implementation. The study concludes with recommendations for scaling GenAI solutions in education, while addressing future research needs.

Musik adalah bentuk ekspresi perasaan atau pikiran yang dikeluarkan secara teratur dalam bentuk bunyi. Bisa dikatakan, bunyi (suara) adalah elemen musik paling dasar. Suara musik yang baik adalah hasil interaksi dari tiga elemen, yaitu: irama, melodi, dan harmoni. Musik juga dikatakan sekumpulan suara yang digabungkan sehingga menjadi suatu irama dengan nada-nada. Dan sekarang ini dengan menggunakan kecerdasan buatan dengan menggunakan logika fuzzy dapat menentukan bagus apa tidaknya music dengan memberi nilai parameter musik. Setelah melakukan pengolahan data terlihat model logika fuzzy bekerja dengan menggunakan derajat keanggotaan dari sebuah nilai, kemudian digunakan untuk menentukan hasil yang diinginkan, berdasarkan aturan-aturan yang telah ditentukan. Kata Kunci; Fuzzy Logic, Vokal, Musik

Introduction. The prospects of using AI tools in teaching and learning English is considered with the focus on the constant improvement of the technologies, developing soft skills and professional training of students' and teachers' competencies based on AI innovations. The research examines the issues of Applied Linguistics in the context where there is evidence of ongoing progress and changes in economic expectations. The integration of AI in English language teaching and learning theory and practice can offer a dynamic approach to align teaching standards and educational programs with the need for intercultural communication and the economic demand for universal and flexible workforce. Methodology. Qualitative and quantitative methods are used to analyze teachers' and students' perceptions of the integration of AI technologies into English language teaching and learning. The empirical analysis of survey responses is conducted primarily using qualitative methods. Emphasis is placed on the descriptive methods of analyzing the chart, percentage indicators are given and explained, indicators are contrasted using the comparative method, and empirical conclusions are drawn. Discussion. Based on the survey outcomes focused on the prospects of AI application in teaching and learning English, a graphical representation is constructed to illustrate the respondents' perception and views on the evolution of AI in this field. The diagram describes the participants' opinions and ideas, making explicit their expectations of AI-based methods in teaching and learning. Conclusion. The results show that students and teachers recognize the transformative potential of AI in English language teaching and learning and acknowledge that there are areas for pedagogical innovation, experimentation with AI tools, and the need to provide collaborative and learner-centered ecosystems. However, both faculty and students also give significant preference to the use of traditional methods in teaching and learning pedagogy and didactics for multiple reasons, including due to the need for enhancement of digital literacy in terms of understanding what is doable in this array of disruptive innovation. The article combines theoretical and practical foundations of pedagogy, methodology and didactics with the aim of optimizing the teaching of English.
Keywords: ARTIFICIAL INTELLIGENCE TOOLS, LANGUAGE LEARNING, ONLINE LEARNING, TEACHING OPTIMIZATION, BLENDED LEARNING, ARTIFICIAL INTELLIGENCE TECHNOLOGIES, DIGITAL LEARNING, CHATGPT, DEEPSEEK

Introduction. The article explores the potential of AI in English language teaching and learning; the focus is on integration of AI technologies into pedagogical practice and their application in the educational process. The purpose of the study is to empirically analyze professors' and students' perceptions of the introduction of AI tools into teaching modern languages and to identify trends in this area. Methodology. Qualitative and quantitative methods are used to analyze educators' and students' perceptions of the integration of AI technologies into English language teaching and learning. The empirical analysis of survey responses is conducted primarily using qualitative methods. Emphasis is placed on the descriptive methods of analyzing the chart, percentage indicators are given and explained, indicators are contrasted using the comparative method, and empirical conclusions are drawn. Discussion. The survey revealed a variety of respondents' attitudes to the use and role of AI. The diagram allowed to identify trends: most of the survey's participants acknowledge the deployment of AI tools in language education, but they also note insufficient awareness of the scale and possibilities of their application, demonstrating basic digital literacy. Conclusion. Respondents expect AI technologies to improve the educational process; the integration of AI in English language teaching is perceived as a promising trend that transforms educational practice.
Keywords: AI, AI TOOLS, BLENDED LEARNING, DIGITAL LEADERSHIP, ENGLISH LANGUAGE TEACHING, ONLINE LEARNING

The empirical study of perception of using the tools of artificial intelligence in learning and teaching English at universities of the Russian Federation, unlike the previous works, identifies, systematizes the main barriers associated with the introduction of AI technologies and provides the innovative foundation for ensuring the methodology for the development of foreign language communicative competences. The integration of AI into the educational process is considered with an emphasis on the cognitive and linguodidactic aspects of teaching English with the use of qualitative and quantitative analysis, which allows us to identify and differentiate the problems that arise in the course of AI transformation in the field of teaching and learning English. The statistical analysis of the effectiveness of using AI tools in the process of teaching English provides evidence-based recommendations, which is an innovative contribution to the existing literature.
Keywords: TOOLS OF ARTIFICIAL INTELLIGENCE, FOREIGN LANGUAGE LEARNING, ONLINE LEARNING, NETWORK COMMUNITY, BLENDED LEARNING, DEEPSEEK, DIGITAL LINGUODIDACTICS, GPT CHAT

Education is no longer confined to physical classrooms. With the rise of technology, schools and institutions are adopting digital solutions to enhance teaching and learning. Digital classrooms are now becoming the backbone of modern education combining accessibility, engagement, and efficiency in one environment. Many institutions have already integrated student management systems to handle academic tasks like admissions, attendance, grading, and communication. When combined with virtual classroom tools, these systems create a smooth, organized, and engaging learning experience for both students and teachers. What Is a Digital Classroom? A digital classroom is an online or technology-enabled environment where lessons, communication, and assessments are managed digitally instead of relying solely on traditional methods.

Conceptual understanding in chemistry requires students to connect quantitative calculations, microscopic chemical processes (e.g., atoms and molecules), and macroscopic outcomes (e.g., concentrations, color, temperature). The ChemCollective Virtual Lab's interactive simulation environment fosters the mapping between these representations by providing students with a fully functional online chemistry lab with realistic chemicals, glassware, and equipment. In the ChemVLab+ project (www.chemvlab.org), we enhance the virtual lab by developing intelligent tutoring for this open-ended simulation environment and creating embedded formative assessment activities. Students have opportunities to apply chemistry knowledge to meaningful contexts and receive immediate, individualized feedback as the system estimates their proficiency based on their actions. We report findings from a classroom field study of ChemVlab+ activities. Data from classroom observations, preposttest learning gains, detailed log file analyses, and teacher interviews suggest that students were actively engaged, that students improved their understanding of chemistry, and that teachers found the activities worthwhile.

Before coming to FSU in 2007, she was a principal research scientist at Educational Testing Service, where she was involved with basic and applied research projects related to assessment, cognitive diagnosis, and learning from advanced instructional systems. Her general research interests hover around the design, development and evaluation of advanced systems to support learning, particularly related to 21stcentury competencies. Her current research involves using games with stealth assessment to support learning of cognitive and non-cognitive knowledge, skills and dispositions. Her research has resulted in numerous grants, journal articles, books, chapters in edited books, a patent, and a couple of recent books, including

We use educational data mining to arrive at models of help-seeking behaviors associated with learning from datasets from three countries: Costa Rica, the Philippines, and the USA. The models were then tested on each country's data to find out how effective help-seeking behavior varies across countries. This study found that models of effective help-seeking are not necessarily transferrable across specific pairs of cultures.

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This report presents a multi-part analysis of a case study involving an AI tutor and an advanced learner focused on financial risk management. It provides a detailed pedagogical evaluation, a strategic framework for implementation, and actionable recommendations for deploying AI tutors in specialized educational contexts.

This paper presents some of the challenges encountered by a field re- search team when deploying an educational game for Physics. These included problems with site infrastructure and institutional support, logistical challenges, compliance with ethics requirements, launch delays, and student inattention or misunderstanding of directions. The paper shares these experiences with the wider community to help fellow researchers prepare, should they decide to con- duct field studies in the Philippines.