Intelligent Tutoring Systems Research Articles

Bibliometric Analysis using Vos Viewer with Publish or Perish of Intelligent Tutoring System in Private Universities

The objective of this study is to analyze the development of intelligent tutoring systems in private universities. We conducted the analysis using bibliometric methods, utilizing the Publish or Perish and VOSviewer applications. Data was obtained by using the publish or perish application with the keyword "intelligent tutoring system in private university" from the Google Scholar database from 2019 to 2024. According to search results, the number of research papers has decreased from 117 to 23 from 2020 to 2024. Mapping using VOSviewer application produces three types of visualization, namely network, overlay, and density visualization. In its conclusion, this research notes a decrease in the number of studies discussing in private universities since 2020, but still shows great potential for development by other researchers.

Application of large language models in the field of education

This paper delves into the application of generative artificial intelligence and large language models in the field of education, with a particular focus on the rising trend of large language models. Large language models play a crucial role in intelligent tutoring systems by accurately understanding student queries through deep learning and providing personalized responses. Case studies showcase the exemplary utilization of natural language processing techniques and reasoning engines, albeit facing challenges related to real-time processing and privacy concerns. The latter part of the paper concentrates on the application of generative artificial intelligence and large language models in curiosity-driven learning and the integration of multimodal educational systems, emphasizing the technical frameworks and challenges associated with multimodal integration. Finally, the paper provides insights into future developments, highlighting research on the potential benefits in the field of education, while emphasizing concerns related to ethics and privacy.

There Are Multiple Paths to Personalized Education, and They Should Be Combined

The ubiquity of digital devices has made it feasible to assign different tasks and levels of support to different learners, also in the classroom. Ideally, this is done with the help of formative assessment software or intelligent tutoring systems. However, personalized assignment of tasks and support levels by a teacher or teaching agent has limitations and is only one path to successful personalization. Self-regulated learning and adaptable learning activities, such as generative learning strategies and differentiating tasks, are promising paths to personalization, too, and combine well with personalized assignment. Initial examples of such combinations are presented. I argue that, in order to be maximally effective, different paths to personalized education need to be combined. This combination promises to boost both immediate learning outcomes and successful learning in the long term, and it is facilitated by recent advances in artificial intelligence.

Enhancing Digital Learning Outcomes Through the Application of Artificial Intelligence: A Comprehensive Review

This research investigates the application of artificial intelligence (AI) in digital learning environments and its impact on learning outcomes. A comprehensive review of literature was conducted, encompassing studies from various educational levels and settings. The analysis reveals promising findings regarding the effectiveness of AI interventions, including intelligent tutoring systems, adaptive learning platforms, virtual assistants, and content recommendation systems, in enhancing learning outcomes. Learners exhibit high levels of engagement and satisfaction with AI-enhanced learning environments, appreciating the interactive features and personalized support provided by AI technologies. However, challenges and limitations associated with AI implementation, such as technical issues, privacy concerns, and ethical considerations, were identified. The research contributes valuable insights into the potential benefits and risks of AI in education, with implications for both research and practice. Future research directions include optimizing AI algorithms, exploring ethical and social implications, and addressing educator training needs to ensure the successful integration of AI technologies into teaching and learning processes.

Integrating learning factors and Bayesian network for interpretable knowledge tracing

Knowledge tracing aims to model learners’ knowledge mastery based on their historical interaction records and predict their future performance. Due to its great potential in enabling personalized learning in intelligent tutoring systems, it has received extensive attention. However, most deep learning-based knowledge tracing methods have significant predictive performance. It is difficult to extract meaningful interpretations from the thousands of parameters in neural networks. The interpretability of knowledge tracing refers to the ability of learners to easily understand the predicted results.To address this problem, based on learning factors that influence the learner’s exercise performance, this paper proposes a novel knowledge tracing model which is named Integrating Learning factors and Bayesian network for interpretable Knowledge Tracing (LBKT). Firstly, meaningful learning factors, including knowledge mastery, learning ability, and exercise difficulty, are calculated from learners’ historical interaction records using deep learning and statistical methods. Then, Bayesian network is constructed to capture the causal relation between the three learning factors and exercise response. Finally, the Bayesian network is generated through structure and parameter learning to obtain interpretable prediction of future exercise performance. The proposed model named LBKT is evaluated on three public real-world educational datasets. The experiment results demonstrate that our approach achieves better predictive performance compared to baseline knowledge tracing methods, while also exhibiting significant superiority in model interpretability.

ADVANCEMENTS AND CHALLENGES IN AI INTEGRATION FOR TECHNICAL LITERACY: A SYSTEMATIC REVIEW

This systematic review explores the advancements and challenges associated with the integration of artificial intelligence (AI) in promoting technical literacy. Technical literacy is increasingly important in today's digital age, where understanding and utilizing technology are essential skills. AI has the potential to enhance technical literacy by providing personalized learning experiences, facilitating hands-on learning, and offering innovative tools and resources. However, the integration of AI in education also presents challenges, such as ensuring equitable access, addressing ethical considerations, and overcoming technical barriers. The review examines a range of studies and literature related to AI integration for technical literacy, focusing on key themes such as personalized learning, hands-on learning, and innovative tools. It highlights the potential of AI to transform technical education by providing tailored learning experiences that cater to individual needs and preferences. AI-driven tools, such as simulations, virtual laboratories, and intelligent tutoring systems, have been shown to enhance student engagement and understanding of technical concepts. Despite the benefits, the review also identifies challenges associated with AI integration, including the need for teacher training, concerns about data privacy, and the risk of reinforcing existing inequalities. Addressing these challenges requires careful planning, collaboration between educators and technology developers, and a commitment to ensuring equitable access to AI-driven educational resources. Overall, this review provides insights into the current state of AI integration for technical literacy and highlights the opportunities and challenges associated with this approach. By addressing these challenges and leveraging the potential of AI, educators can enhance technical literacy and prepare students for success in a technology-driven world.
 Keywords: Advancement, Challenges, AI, Integration, Technical Literacy.

Factors affecting Chinese undergraduate medical students’ behavioural intention and actual use of intelligent tutoring systems

This study examined Chinese undergraduate medical students’ acceptance and adoption of intelligent tutoring systems (ITSs) using the general extended technology acceptance model for e-learning via a Likert-scale questionnaire. Specifically, it examined the relations between the five antecedents and the four core components in the model (i.e., perceived usefulness (PU), perceived ease of use (PEOU), behavioural intention and actual use of ITSs). The results of PLS-SEM showed that perceived enjoyment was the most influential antecedent as it significantly impacted both PU and PEOU. Both self-efficacy and prior experience only significantly contributed to PEOU but not PU. Both PU and PEOU significantly and positively predicted behavioural intention, which in turn had a significant and positive path to actual use. The results provide some practical implications to teachers as to how to encourage Chinese undergraduate medical students’ adoption of ITSs: by integrating some gamification elements into the learning activities in ITSs to foster students’ enjoyable feelings or familiarising students with using ITSs so that they can quickly adapt to learning through them. This could be achieved by providing guidance in using ITSs via videos, websites or booklets, or at the beginning of the course, inviting senior students to share their perceived advantages and usefulness of using ITSs. Implications for practice or policy: Because of the importance of perceived enjoyment, teachers may integrate some gamification into the learning activities in ITSs to nurture students’ enjoyable feelings. To enable students to quickly accommodate learning via ITSs, institutions may provide students with information on common features of ITSs or how to navigate a specific ITS. Teachers may explicitly explain how learning objectives can be better achieved through using an ITSs so that students will appreciate its usefulness.

Artificial Intelligence in Education: Understanding Benefits, Limitations, and Prospects for the Future

The paper provides a comprehensive analysis of the use of artificial intelligence (AI) in education, focusing on its definition, advantages, disadvantages, and future prospects. It explores various applications of AI in education, such as chatbots, learning analytics, and intelligent tutoring systems. The study addresses key issues like data-driven decision-making, efficient administration, personalized learning opportunities, and potential ethical concerns. Furthermore, it discusses the future prospects of AI in education, including advancements in virtual reality integration, adaptive learning systems, and universal access to high-quality education.

Transforming Education: The Evolving Role of Artificial Intelligence in The Students Academic Performance

As technology continues to advance, its integration into various aspects of education has become inevitable. This article delves into the transformative impact of Artificial Intelligence (AI) on students' academic performance. AI's role in education has shifted from a mere supplementary tool to a fundamental component reshaping teaching and learning paradigms. This paper explores how AI-powered educational technologies, such as adaptive learning platforms, intelligent tutoring systems, and automated assessment tools, are revolutionizing traditional educational practices. It examines the benefits AI brings to students, educators, and educational institutions, including personalized learning experiences, enhanced student engagement, and efficient assessment mechanisms. Furthermore, the article discusses the potential challenges and ethical considerations associated with AI integration in education, such as data privacy concerns, algorithmic biases, and the digital divide. By analyzing recent research findings and real-world implementations, this paper provides insights into the evolving landscape of education empowered by AI and underscores the importance of responsible AI adoption to optimize students' academic outcomes in the digital era.

Empowering Distance Education with Artificial Intelligence

This article investigated the integration of Artificial Intelligence (AI) in the context of Distance Education (DE), with the aim of exploring its advantages, disadvantages, and the challenges faced by teachers and students. The research focused on how AI can be employed to promote meaningful learning, using a bibliographic research methodology, as proposed by Severino (2007). This approach involved the critical analysis of existing literature, including relevant case studies and theories pertinent to the use of AI in education. The main authors cited were Castro (2002) and Tavares, Meira, and Amaral (2020), whose works provided insights on the application of Intelligent Tutoring Systems (ITS) and other AI technologies in education. The literature review highlighted the potential of AI to personalize the learning experience and the associated challenges, such as the need for adequate infrastructure, digital skills, and ethical considerations. A case study from Georgia State University illustrated a successful practical application of AI to prevent student dropout, offering a model for future implementations in DE. The analysis showed that, despite obstacles, the integration of AI in DE has the potential to positively transform education, offering opportunities for more adaptive and personalized learning. In conclusion, the article emphasized the importance of addressing technical, ethical, and pedagogical challenges in adopting AI in education, highlighting the need for careful strategies that ensure the effectiveness and inclusivity of technological interventions in DE.

ARTIFICIAL INTELEGENCE TRENDS IN EDUCATION AMONG SCHOOL ADMINISTRATORS IN MALAYSIA

In recent years, artificial intelligence (AI) has gained significant attention in the education sector in Malaysia. With the rapid advancement of technology, school administrators are recognizing the potential of AI to improve student learning outcomes and streamline administrative processes. In this narrative overview, we will discuss some of the AI trends in education that are currently being implemented or considered by school administrators in Malaysia. The awareness of the importance of digital learning are very much needed to be emphasize because without it, students will be left behind in terms of education. The main objective of this research is to identify the prospective impact of artificial technologies among school administrators in Malaysian schools with personalized learning, chatbots, predictive analytics, intelligent tutoring systems, and automated grading. This study also conducted to examine the level of competence of school administrators on the technology management and instructional technology using artificial technologies. The respondents of this study consisted of 40 secondary schools from Malaysia. Using the method of questionnaire survey and interview this study takes the entire education system of Malaysia as an example to explore the effect of AI on role cognition in the education system to streamline administrative processes. The findings of the study show that the level of competence among school administrators in technology management and instructional technology in leveraging AI to improve student outcomes and streamline administrative processes to the study process and to predict possible changes in educational landscape is at a moderate level. Therefore, school administrators need to give their full commitment to the use of instructional technology in order to increase productivity in teaching and learning as well as school management with the impact of artificial intelligence. This research will provide appropriate knowledge for all school administrators to adopt technology leadership features to enhance the school administration system and teacher teaching implementation at a higher level.

A Study of Artificial Intelligence-Driven Change in Higher Education in the Age of Digital Intelligence

The arrival of the age of digital intelligence signifies the increasingly important role of artificial intelligence (AI) in driving change in higher education. The incorporation of AI technology improves the efficiency of teaching and learning and facilitates fundamental changes in the mode, content, and management of education. This paper discusses the application of AI in personalized learning path design, intelligent tutoring systems, virtual labs, and course content development, and analyzes the challenges and countermeasures of technology access and equality, data privacy and security, education quality and AI dependency, and resistance to changing the traditional education system.

AI Study Partner : Development of an LLM and Gen AI-Enhanced Study Assistant Tool

In recent years, Generative AI has started to play a pivotal role in transforming the educational landscape, making learning more personalized, engaging, and accessible. Unlike traditional educational tools, Gen AI can adapt to each student's unique learning style and pace, offering customized support that can significantly enhance understanding and retention of information. It enables the creation of intelligent tutoring systems, interactive study aids, and personalized learning experiences that can assess and respond to individual needs in real time This paper introduces the AI Study Partner, an innovative tool designed to revolutionize the educational landscape by integrating Generative Artificial Intelligence (Gen AI) and Large Language Models (LLMs). The AI Study Partner is engineered to cater to the diverse needs of learners by offering a suite of six key features: the ability to upload and interact with any type of content, summarization of extensive lessons, generation of flashcards for effective study, automated question creation with auto-evaluation capabilities, a conversational chatbot assistant, and an advanced smart search function. These features collectively aim to create a more personalized, engaging, and efficient learning experience. The development of the AI Study Partner responds to the pressing need for educational tools that accommodate the varying paces and styles of learning, making education more accessible and effective. By leveraging the latest advancements in AI, the tool not only facilitates a deeper understanding of complex subjects but also encourages independent study habits and critical thinking skills. This research outlines the conceptual framework, design methodology, and technical implementation of the AI Study Partner, highlighting its potential to positively impact education by providing a versatile and interactive learning platform.The AI Study Partner represents a significant step forward in the pursuit of creating adaptive, responsive, and personalized educational experiences for learners worldwide. Key Words : Artificial Intelligence in Education, Personalized Learning , Large Language Models (LLMs), Generative AI (Gen AI), User Engagement in Learning Data-Driven Education, Vector database, AI based Feedback Conversational Chatbots, Smart Search.

Causal-Driven Skill Prerequisite Structure Discovery

Knowing a prerequisite structure among skills in a subject domain effectively enables several educational applications, including intelligent tutoring systems and curriculum planning. Traditionally, educators or domain experts use intuition to determine the skills' prerequisite relationships, which is time-consuming and prone to fall into the trap of blind spots. In this paper, we focus on inferring the prerequisite structure given access to students' performance on exercises in a subject. Nevertheless, it is challenging since students' mastery of skills can not be directly observed, but can only be estimated, i.e., its latency in nature. To tackle this problem, we propose a causal-driven skill prerequisite structure discovery (CSPS) method in a two-stage learning framework. In the first stage, we learn the skills' correlation relationships presented in the covariance matrix from the student performance data while, through the predicted covariance matrix in the second stage, we consider a heuristic method based on conditional independence tests and standardized partial variance to discover the prerequisite structure. We demonstrate the performance of the new approach with both simulated and real-world data. The experimental results show the effectiveness of the proposed model for identifying the skills' prerequisite structure.

Impact of Artificial Intelligence in Education

Artificial Intelligence (AI) stands out as one of the paramount technological breakthroughs of the 21st century, holding immense promise for reshaping various facets of society, including the realm of education. This systematic review aims to explore the impact of AI on education by analyzing empirical studies from various academic databases. The review identifies three main areas where AI has been implemented in education: intelligent tutoring systems, personalized learning, and grading automation.Research findings indicate that educational tools utilizing Artificial Intelligence (AI) yield favorable effects on student learning outcomes, leading to enhanced academic achievement, heightened retention rates, and increased engagement.. Moreover, AI-based tools can also benefit educators, by automating administrative tasks and providing real-time feedback to students. Nevertheless, the review underscores various concerns associated with AI in education, encompassing privacy issues, biases, and the apprehension regarding technology potentially supplanting human educators. The study concludes that while AI- based educational tools show promise, more research is needed to better understand their effectiveness, as well as their potential drawbacks, in order to fully leverage their benefits for education.

Have Courage to Use your Own Mind, with or without AI: The Relevance of Kant's Enlightenment to Higher Education in the Age of Artificial Intelligence

Artificial intelligence (AI) in higher education is a complex issue that can be discussed from many different perspectives. There is currently a great need for ethical discussions about the use of AI in universities. For example, educational researchers and teachers are already talking a lot about fairness, accountability, transparency, bias, autonomy, agency and inclusion of AI applications, and discussing these in terms of concrete teaching-learning settings. However, less explored are the implications of AI-enhanced teaching and learning in relation to fundamental educational ideals and goals. The article takes this research desideratum as a starting point by relating the use of AI in universities to Kant's reflections on enlightenment. The aim of this article is to theoretically analyse the compatibility of various AI tools with the ideal of maturity on an educational philosophical level and to formulate recommendations for action based on the results. Through a comprehensive literature review, the article analyses the impact of intelligent tutoring systems, ChatGPT and AI-supported research tools on students’ maturity and discusses both opportunities and challenges for higher education. The theoretical analysis shows that intelligent tutoring systems and ChatGPT threaten student maturity, while AI-supported research tools can have a positive effect. In addition, the analysis provides several recommendations that can help to minimise the risks of AI applications in terms of student maturity. The educational principle of research-based learning is of particular importance in this context, illustrating how students can learn to use AI tools responsibly and maturely. In this sense, the paper presents a theoretical study that fundamentally reflects on the maturity of students in the age of AI and thus both encourages teachers in the field of e-teaching to critically reflect on AI-based tools and provides a basis for further empirical research.

Bayesian Intelligent Tutoring System for Vocational High Schools

The absence of individualized tutorials during regular school hours has resulted in a suboptimal learning method at Vocational High Schools(SMK), limiting students' ability to reach their optimum competency. Several Computerized self-study systems have been created as potential solutions to these challenges. Regrettably, a notable drawback of the system lies in its failure to address students' diverse range of abilities adequately. This study presents a proposed model for an Intelligent Tutoring System (ITS) utilizing the Bayesian Network (BN) at Vocational High Schools. The model aims to assess students' proficiency levels and deliver skill-based instructional materials tailored to individual students' abilities. This type of research is called research and development (R&D), to develop and know the validity of a product. The system under development will undergo trials within the Computer and Network Engineering (TKJ) program at SMK Negeri 4 Gowa. These trials will employ a quasi-experimental approach, explicitly utilizing a one-group pretest-posttest design.The findings indicated that there were notable disparities in the learning outcomes of students following the implementation of the proposed ITS. To put it otherwise, the proposed ITS has improved students' proficiency in Vocational High Schools. The evaluation outcomes suggest that the BN model had a significant level of accuracy, reaching 84%.

The effect of different question types on vicarious learning

This study explored the role of question types and prior knowledge in vicarious learning with an intelligent tutoring system. In experiment 1, the participants were assigned to three conditions (deep questions, shallow questions, control), the results showed that participants in the deep questions condition had higher retention test scores than those in the shallow questions condition and those in the control condition. In experiment 2, we conducted a 2 (prior knowledge: high, low) × 3 (question types: deep question, shallow question, control) between-subjects experimental design, and participants in the deep question condition again had higher retention test scores than the other conditions. Also, participants in the shallow question condition had higher retention test scores than those in the control condition. Additionally, no interaction between prior knowledge and question types was observed. In conclusion, deep questions can promote learning of experimental design, which replicates prior research results on deep level questions.

An Intelligent Mathematics Problem-Solving Tutoring System Framework

This study proposed a novel framework for redesigning problem-solving activities in an intelligent tutoring system (ITS) called the intelligent neural-mechanistic mathematics problem- solving tutoring system (IN-MP-STS). This concept paper presents a new approach to ITS by incorporating elements of neuroscience mechanisms as a learning strategy that focuses on optimizing the brain’s ability through neural mechanisms. It also introduces fuzzy neural networks (FNNs) as a tool for modulating assessment and analyzing outcomes. This framework offers an alternative perspective on delivery methods and learning approaches in the ITS module. By effectively integrating neuroscience mechanistic elements such as motivation, activation, regulation, execution, memorization, and interactivities, deep learning can be achieved, leading to improved student competence. This framework also proposes an adaptive assessment component based on FNNs, which will enhance the measurement and feedback modules in the system. It is necessary to modify the way that ITS and soft computing methods, such as the study of neural networks (NNs), are combined to make learning measurement and assessment more transparent. This innovation has not been fully disclosed, so researchers are encouraged to further test the concepts presented to assess their alignment with the existing system and ethical considerations. This framework enhances the conceptual research findings of FNNs and incorporates neuroscience-based strategies into architecture and autonomous problem-solving skills within an ITS model. It also offers references for the development of problem-solving learning. IN-MP-STS has the potential to significantly enhance students’ competencies and abilities, thereby fostering the development of more comprehensive, holistic, and sustainable ITS. This approach also has the potential to enrich the existing literature on the sustainability of neural networks.

The Rise of Artificial Intelligence in Education

Artificial intelligence (AI) has emerged as a transformative force, reshaping the landscape of many sectors at an unprecedented pace. This shift features the integration of intelligent machines, capable of learning, reasoning, and problem-solving, into diverse domains. The concept of AI encompasses a range of technologies, including machine learning, natural language processing, and computer vision, collectively contributing to its increasing prevalence. The integration of artificial intelligence into education heralds a transformative era, redefining traditional teaching paradigms and learning experiences. AI's potential impact on education is multifaceted, encompassing personalized learning, intelligent tutoring systems, and data-driven insights. As AI technologies evolve, educators and learners alike are poised to benefit from adaptive and responsive educational environments. The intersection of AI and education holds the promise of fostering a dynamic, technology-infused learning landscape for the generations to come.