Intelligent Tutoring Systems Research Articles

AI-Powered Administration: The Role of Intelligent Tutoring Systems in Education

The integration of Artificial Intelligence (AI) into education is transforming traditional administrative processes and instructional methodologies. This paper examines the pivotal role of Intelligent Tutoring Systems (ITS) in enhancing education administration through AI-powered solutions. By automating routine tasks, providing real-time analytics, and supporting data-driven decision-making, ITS contribute to more efficient and effective management of educational institutions.We explore several models that illustrate the diverse applications of ITS in administration, including automated workflow management, data-driven decision-making, personalized learning support, resource optimization, and enhanced student support services. Each model demonstrates the potential of ITS to streamline operations, improve resource allocation, and offer personalized educational experiences, thereby reducing administrative burdens and fostering a more adaptive and responsive educational environment.Through a comprehensive analysis of current implementations and case studies, this paper highlights the transformative impact of AI-powered ITS on education administration. The findings underscore the importance of careful implementation and continuous evaluation to address challenges such as data privacy and ethical considerations, ensuring that the benefits of AI are equitably distributed among all stakeholders in the educational ecosystem.

On improving conversational interfaces in educational systems

Conversational Intelligent Tutoring Systems (CITS) have drawn increasing interest in education because of their capacity to tailor learning experiences, improve user engagement, and contribute to the effective transfer of knowledge. Conversational agents employ advanced natural language techniques to engage in a convincing human-like tutorial conversation. In solving math word problems, a significant challenge arises in enabling the system to understand user utterances and accurately map extracted entities to the essential problem quantities required for problem-solving, despite the inherent ambiguity of human natural language. In this study, we propose two possible approaches to enhance the performance of a particular CITS designed to teach learners to solve arithmetic–algebraic word problems. Firstly, we propose an ensemble approach to intent classification and entity extraction, which combines the predictions made by two distinct individual models that use constraints defined by human experts. This approach leverages the intertwined nature of the intents and entities to yield a comprehensive understanding of the user’s utterance, ultimately aiming to enhance semantic accuracy. Secondly, we introduce an adapted Term Frequency-Inverse Document Frequency technique to associate entities with problem quantity descriptions. The evaluation was conducted on the AWPS and MATH-HINTS datasets, containing conversational data and a collection of arithmetical and algebraic math problems, respectively. The results demonstrate that the proposed ensemble approach outperforms individual models, and the proposed method for entity–quantity matching surpasses the performance of typical text semantic embedding models.

The Roles of Pedagogical Agent’s Emotional Support: Dynamics Between Emotions and Learning Strategies in Multimedia Learning

The present research conducted two experiments with an intelligent tutoring system to investigate the overall and dynamic impact of emotional support from a pedagogical agent (PA). In Experiment 1, a single factor intergroup design was used to explore the impact of PA’s emotional support (supportive vs. non-supportive) on learners’ emotions, intrinsic motivation, and learning gain. Sixty participants were recruited and randomly assigned to one of the two conditions. Experiment 2 also conducted a single factor between-subjects design to investigated the dynamic patterns between emotions and learning strategies among 30 participants using lag sequential analysis. Results showed that: Compared with the non-supportive pedagogical agent, the supportive pedagogical agent reduced frustration and improved learning gain, but did not increase intrinsic motivation. In addition, learners with the supportive pedagogical agent used more appropriate strategies after frustration and surprise, and use less ineffective strategies after confusion and enjoyment to avoid reaching a wrong answer. If learners did not receive emotional support in such cases, learning strategies following these emotions were more likely lead to negative cognitive results, or negative emotions tended to appear repeatedly. Instructors or PAs should identify learners’ emotions in time and provide the appropriate emotional support according to learners’ emotions.

AI-Powered Classrooms: A Revolution in Learning Environments

This article tackles the reframing impact of Artificial Intelligence on contemporary classrooms, exploring how AI-powered educational environments are transforming traditional teaching methods. The narrative unfolds through an examination of intelligent tutoring systems, adaptive learning platforms and immersive technologies. AI emerges as a catalyst for educational innovation by customizing instruction to individual student needs, promoting personalized learning experiences and preparing students for the demands of the future. The article also deals with challenges related to ethical considerations, emphasizing the importance of considerate implementation and continuing scrutiny. Through a comprehensive exploration of AI-powered classrooms, this article aims to contribute to the ongoing dialogue on the intersection of technology and education, focusing on the potential and challenges of integrating AI into the framework of modern learning environments.

Application of AI Tools in Education- A Conceptual Framework

The evolving demands of education necessitate creativity and innovation in the teaching and learning process. Artificial intelligence (AI) has emerged as a disruptive force in the field of education, offering innovative approaches to enhance instructional design, personalize student experiences, streamline administrative procedures, and boost academic performance. With a focus on significant areas where these technologies are having a significant impact, this study attempts to provide a comprehensive overview of the application of AI tools in education. The incorporation of AI-powered learning platforms, such as intelligent tutoring systems and adaptive learning systems, enables personalized learning experiences tailored to the needs of each individual student. These platforms assess student data, track learning progress, disseminate material in real-time, and adjust it to meet learning objectives by using artificial intelligence (AI) algorithms. Five subsections make up this study. Section 1.1 introduces AI applied educational technology platforms. Section 1.2 introduces Impact of AI Tools on Educational Pedagogy and Learning Outcomes. Section 1.3 introduces Impact of AI-Driven Educational Technologies on Teaching Pedagogy. Section 1.4 introduces Effectiveness of AI-Powered Learning Platforms. Finally, section 1.5 describes Ethical Considerations and Challenges in Implementing AI Tools in Education.

AI-Based Tools in Mathematics Education: A Systematic Review of Characteristics, Applications, and Evaluation Methods

Artificial intelligence has been utilized for enhancement of education. The paper describes the gradual influences of Natural language processing, ChatGPT and Intelligent Tutoring Systems in mathematical education in particular. A systematic review and briefing is targeted for budding researchers in the field of AI based education. A comparison of research with a brief summary is provided along with the analysis of the search. Future scope for the clear understanding and easy implementation of mathematics is aimed with development of the intelligent system with customized solutions to the difficulties faced by the learners of mathematics. The impact of Artificial Intelligence (AI) on the applications in education has been evolving since the past 50+ years. The advent of Intelligent Tutoring Systems has emerged as a rich and important natural environment that deploys and improves AI algorithms. The review carried out shows ITS as an ever growing field for education comes with positive impact on education outcomes, effective increase in learning rates, increase in the learning levels of students and many such benefits. It also has its own challenges in creating authoring environments, making collaborative learning possible, world wide web deployment and creation of virtual reality environments to name a few. In this review paper we focus on the impact of applications that have been developed using NLP, ChatGPT and ITS as education technology.

Acceptance of a mobile telepresence robot used to teach adapted physical activity to isolated older adults: extending and testing the technology acceptance model.

This study aimed to investigate the acceptance of adapted physical activity (APA) by teachers and students before the use of a mobile telepresence robot (MTR), used to remotely supervise isolated older adults' physical activity. While previous studies have shown MTR to be fairly well accepted by older adults, nothing is known about its acceptance by APA teachers themselves. However, if they did not accept it, the MTR would not be used in the end. This would be a public health issue because isolated older adults would not benefit from supervised APA, yet beneficial to their health. To this end, 334 participants answered a survey that measured different psychological variables, based on the technology acceptance model (TAM). Student's t-tests and structural equation modeling were used for data processing. Results showed that, before use, there was not any significant difference between teachers' and students' acceptance of the MTR. Then, perceived usefulness for teaching APA, perceived ease of use, perceived enjoyment, and intention to use the MTR were lower than the mean of the scale, while perceived usefulness for older adults was higher than the mean of the scale. Finally, this study has validated an extended version of the TAM (including the need for competence and MTR self-efficacy), which allowed it to explain 84.3% of the variance of the students' and APA teachers' intention to use the MTR for teaching APA to isolated older adults. Initial obstacles to the use of the MTR seem to exist on the part of APA teachers, prior to their first use, whereas this is not the case for older adults. APA teachers' acceptance should therefore be investigated in future studies to examine whether this trend is confirmed after the effective use of the MTR.

HSAW: A Half-Face Self-Attention Weighted Approach for Facial Expression Recognition

Facial expression recognition plays an increasingly important role in daily life, and it is used in several areas of human–computer interaction, such as robotics, assisted driving, and intelligent tutoring systems. However, the current mainstream methods are based on the whole face, and do not consider the existence of expression asymmetry between the left and right half-face. Hence, the accuracy of facial expression recognition needs to be improved. In this paper, we propose a half-face self-attention weighted approach called HSAW. Using statistical analysis and computer vision techniques, we found that the left half-face contains richer expression features than the right half-face. Specifically, we employed a self-attention mechanism to assign different weights to the left and right halves of the face. These weights are combined with convolutional neural network features for improved facial expression recognition. Furthermore, to attack the presence of uncertain categories in the dataset, we introduce adaptive re-labeling module, which can improve the recognition accuracy. Extensive experiments conducted on the FER2013 and RAF datasets have verified the effectiveness of the proposed method, which utilizes fewer parameters.

Artificial Intelligence in Anatomy Teaching and Learning: A Literature Review

Abstract Medical anatomy is an essential preclinical course for medical undergraduates and provides a fundamental basis for various medical and surgical specializations. Frequently, students encounter difficulties when it comes to studying and comprehending the subject matter. Several pedagogical strategies have been devised and utilized throughout the years to enhance the process of teaching and learning in the field of anatomy. Artificial intelligence (AI) is now transforming anatomy education by utilizing modern technologies such as virtual reality, augmented reality, machine learning, and AI-powered evaluation tools. Recent research explored the AI in anatomy teaching, emphasizing its advantages and constraints. This review provides a thorough overview of the latest developments in the use of AI in anatomical education. It explores how AI-powered technologies can improve the educational experience for anatomy students, including personalized learning, automated grading, and intelligent tutoring systems, and examines the effects of these technologies on student engagement, learning outcomes, and teaching methods.

Transforming and Reforming the Indian Education System with Artificial Intelligence

India’s growing economy and population proves its significant stake in the Artificial lntelligence (AI) revolution. India's education and technology sector has grown exponentially over the last few years, particularly in times of Covid-19 pandemic but owing to various reasons, it is accessible to only one in four students in India. AI has proven to be of benefit to all stakeholders alike by (a) enhancing teachers' efficiency and effectiveness in managing Multi Levelled or Multi Graded Learning Units and enabling automated development of customised teaching content; (b) using Intelligent Tutoring Systems to tailor the learning materials to a child's level of competence, style and pace of learning for improving student interactivity and attention rates; and (c) for effective teacher posting and transfer systems and predicting likely student activities such as dropout rates in government schools. The research provides a comprehensive review of the rigorous inclusion of Al in India's education sector through various policy measures. According to the findings, Al increases the efficiency of grading and encourages critical thinking and analytical skills among students. Further studies can assess effective integration of AI with educational practices and policies to increase learning needs.

Artificial Intelligence in education: A comprehensive study

This comprehensive study delves into the multifaceted role of AI in education, exploring its applications, benefits, challenges, and future implications. The purpose of the study is to show how AI n education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes. As a methodology, the library method and the study and review of various documents have been used in this research. The study examines the diverse range of AI technologies employed in educational settings, including intelligent tutoring systems, personalized learning platforms, educational chatbots, and virtual reality simulations. Furthermore, the study delves into the numerous benefits that AI brings to education. It highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly. Additionally, AI-driven tools promote inclusivity by providing personalized support to learners with diverse needs and learning styles. Despite its transformative potential, the study also acknowledges the challenges and ethical considerations associated with integrating AI into education. Data privacy, algorithmic bias, and the digital divide are examined in detail, emphasizing the importance of responsible AI deployment and ethical guidelines. Looking ahead, the study explores the future implications of AI in education and the evolving role of educators in AI-enabled classrooms. It discusses how AI technologies will continue to evolve, offering new opportunities for collaborative learning, skill development, and lifelong education. In conclusion, this comprehensive study underscores the profound impact of AI on education and the need for thoughtful implementation strategies that prioritize equity, inclusivity, and ethical considerations. By harnessing the potential of AI, education systems can better prepare learners for the challenges and opportunities of the future.

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Analysis of the repercussions of Artificial Intelligence in the Personalization of the Virtual Educational Process in Higher Education Programs

This study examined how artificial intelligence (AI) has transformed the personalization of the virtual educational process in higher education programs. A systematic review of literature published between 2012 and 2023 was carried out, evaluating empirical studies, reports and review articles available in academic databases such as IEEE Xplore, SpringerLink and Google Scholar. Methods discussed include intelligent tutoring systems, learning analytics, and recommendation systems. The results showed that AI significantly improved the personalization of learning. Intelligent tutoring systems provide real-time adaptive feedback, adjusting content and pacing based on students' individual needs, improving their understanding and retention. Learning analytics helps identify student behavior patterns and predict academic issues, thereby facilitating timely interventions that help improve performance. Additionally, recommender systems personalize study materials based on student preferences and progress, thereby optimizing the educational experience. However, significant challenges have been identified, such as the need to protect data privacy and mitigate algorithmic biases that can affect the fairness and efficiency of these systems. In conclusion, the integration of AI into virtual higher education has enhanced the personalization of learning, improving both student satisfaction and academic performance. However, there is a need to continue to focus on developing ethical and equitable AI systems to address identified issues and maximize educational benefits

Educational models for cognition: Methodology of modeling intellectual skills for intelligent tutoring systems

Automation of teaching people new skills requires modeling of human reasoning because human cognition involves active reasoning over the new subject domain to acquire skills that will later become automatic. The article presents Thought Process Trees — a language for modeling human reasoning that was created to facilitate the development of intelligent tutoring systems, which can perform the same reasoning that is expected of a student and find deficiencies in their line of thinking, providing explanatory messages and allowing them to learn from performance errors. The methodology of building trees which better reflect human learning is discussed, with examples of design choices during the modeling process and their consequences. The characteristics of educational modeling that impact building subject-domain models for intelligent tutoring systems are discussed. The trees were formalized and served as a basis for developing a framework for constructing intelligent tutoring systems. This significantly lowered the time required to build and debug a constraint-based subject-domain model. The framework has already been used to develop five intelligent tutoring systems and their prototypes and is being used to develop more of them.

Bayesian Knowledge Tracing Implemented in a Telecommunications Serious Game

The University of Johannesburg has integrated serious games into its teaching, exemplified by Codebreakers, a 2D game teaching information theory. While successful, Codebreakers lacked personalisation and used a criticised assessment method based on answer streaks. Knowledge tracing algorithms, known for their effectiveness in intelligent tutoring systems, were considered to address these limitations. This led to the research question: "Can a new serious game be designed, incorporating knowledge tracing algorithms to deliver personalised learning experiences in telecommunications education?" In response, an escape-themed serious game was developed, integrating Bayesian Knowledge Tracing as a statistical student model for personalised learning. This innovative approach combines free-roam gameplay with tailored educational content, significantly advancing serious game design. While primarily aimed at enhancing Codebreakers, this new game contributes substantially to serious game theory by successfully implementing personalised learning within an engaging format. The project showcases the potential of knowledge tracing algorithms in creating adaptive, student-centered learning experiences within the context of educational games.

Use of an intelligent tutoring system for a curriculum on augmentative and alternative communication: Feasibility for implementation.

Over 8 million children with disabilities live in Africa and are candidates for augmentative and alternative communication (AAC), yet formal training for team members, such as speech-language therapists and special education teachers, is extremely limited. Only one university on the continent provides postgraduate degrees in AAC, and other institutions provide only short modules at an undergraduate level. The need for an introductory training course on AAC that is accessible by university students continent-wide was identified. An online programme, namely an intelligent tutoring system (ITS), was identified as a possible option to facilitate interactive learning without the need for synchronous teaching. The use of an ITS is shown to be effective in developing knowledge and clinical reasoning in the health and rehabilitation fields. However, it has not yet been applied to student teaching in the field of AAC. To determine both the feasibility of an ITS to implement an AAC curriculum for students in four African countries, and the usability and effectiveness of such a system as a mechanism for learning about AAC. The study included two components: the development of a valid AAC curriculum; and using the ITS to test the effectiveness of implementation in a pre- and post-test design with 98 speech-language therapy and special education students from five universities. Statistically significant differences were obtained between pre- and post-test assessments. Students perceived the learning experience as practical, with rich content. The findings suggest that the ITS-based AAC curriculum was positively perceived by the students and potentially offers an effective means of providing supplementary AAC training to students, although modifications to the system are still required. What is already known on the subject Professionals typically lack formal training in AAC. In Africa, this presents a serious challenge as there are over 8 million children who are candidates for AAC. A need for an introductory training course on AAC, which can be accessed by university students continent-wide, was identified. What this paper adds to existing knowledge An AAC curriculum was developed and integrated into an ITS, an online programme allowing interactive learning through asynchronous teaching. Students from four African countries completed the AAC ITS curriculum. The curriculum was positively received by the students and statistically significant changes in knowledge were identified. What are the practical and clinical implications of this work? This feasibility study shows that the use of an ITS is an effective means of providing AAC training to university students in these African countries. The results provide a valuable contribution toward ensuring the equitable distribution of AAC training opportunities in the African context. This will have a significant positive impact on those who are candidates for AAC.

AI-Driven Flipped Classroom: Revolutionizing Education Through Digital Pedagogy

The integration of artificial intelligence (AI) into the flipped classroom model is the subject of this research paper. With the flipped classroom approach, traditional teaching methods are reversed, with instructional content being delivered outside of class and class time being devoted to discussions, activities, and problem-solving. Teachers want to give students a personalized learning experience, and they do this by implementing AI technologies like intelligent tutoring systems, virtual tutors, and adaptive learning platforms. This study uses existing research and empirical studies to analyse the effects, advantages, difficulties, and efficacy of using AI in flipped classrooms. The study explores the use of AI in flipped classrooms, highlighting its potential benefits like improved learning outcomes and scalability. However, it also addresses challenges like technology infrastructure, teacher preparation, privacy, and equity, as well as potential drawbacks.

Distance Education and the Use of Artificial Intelligence Through Agents as an Aggregate Factor in Intelligent Systems

It is proposed to investigate the technological changes that drive Distance Education (EaD) through Artificial Intelligence (AI). It can become a powerful tool, expanding possibilities to have more assertive and effective results in the process of evaluating the knowledge obtained by the student in pedagogical content taught by the teacher. The agents are the resources to the AI technique, working in an Intelligent Tutor System (STI), which allow for flexibility in part of this evaluation process, as their characteristics of responding to inferences from the environment, enable them to display future results, based on statistics, making them autonomous and intelligent. This work proposes to present a project or its insertion of AI agents in the STI, specifically to the MOODLE platform, which allows for a better way of learning in accordance with the needs of students, respecting their level of knowledge. Flexibility that will occur through the classification of activities, to be performed by students in a list of exercises, with difficulty levels in easy, medium and difficult levels. The MOODLE stores and makes available to the student all the material prepared for the teacher's class and, with the incorporation of this AI technique, the process will be flexible when measuring each student respecting their previous knowledge. Respect for their education profile by experiencing the limits of the cognitive aspect, their deficiencies in knowledge of the content(s) not assimilated in high school or in curricular unit(s) in the undergraduate course, encouraging to reach levels of overcoming and acquiring learning.

Generalisable sensor-free frustration detection in online learning environments using machine learning

Learning can generally be categorised into three domains, which include cognitive (thinking), affective (emotions or feeling) and psychomotor (physical or kinesthetic). In the learner model, acknowledging the affective aspects of learning is important for a range of learner outcomes, including motivation, persistence, and engagement. Learners’ affective states can be detected using physical (e.g. cameras) and physiological sensors (e.g., EEG) in online learning. Although these detectors demonstrate high accuracy, they raise privacy concerns for learners and present challenges in deploying them on a large scale to larger groups of students or in classroom settings. Consequently, researchers have designed an alternative method that can recognise students’ affective states at any point during online learning from their interaction with a computer-based learning platform (i.e. intelligent tutoring systems) without using any sensors. Existing sensor-free affect detectors however, are less accurate and not directly generalisable to other domains and systems. This research focuses on developing generalisable sensor-free affect detectors to identify students’ frustration during online learning using machine learning classifiers. The detectors were built by identifying minimal optimal features associated with frustration from the high-dimensional feature space through a series of experiments on a real-world students’ affective dataset, which are generalisable across various learning platforms and domains. To evaluate their accuracy and generalisability, the detectors’ performance was validated on two independent datasets collected from different educational institutions. The experimental results show that cost-sensitive Bayesian classifiers can achieve higher affect detection accuracies with a small number of generalisable features compared to other classifiers.

Evaluating the content structure of intelligent tutor systems—A psychological network analysis

The adoption of intelligent tutoring systems (ITSs) worldwide has led to a considerable accumulation of process data as students interact with different learning topics within these systems. Typically, these learning topics are structured within ITSs (e.g., the fraction topic includes subtopics such as a fraction number line subtopic). However, there is a lack of methods that offer quick, data-driven insights into the content structure of ITSs, particularly through easily accessible visualizations. Here, we applied psychological network analysis to process data (230,241 students; 5,365,932 problem sets) from an ITS for learning mathematics to explore performance interdependencies between 40 different subtopics. We argue that the visualization of these content interdependencies allows for a quick empirical evaluation of the validity of the existing structuring of the respective learning content. These insights allow for deriving recommendations concerning potential changes in the ITS structure and are thus highly valuable for ITS developers. Our results are also relevant for researchers as the interdependencies illustrated through psychological network analysis can contribute towards a better understanding of the interplay between mathematical skills. Together, our results indicate that psychological network analysis represents a valuable data-driven method to evaluate and optimize ITSs.