Intelligent Tutoring Systems Research Articles

A web-based intelligent tutoring system for reading comprehension delivered to fifth-grade students attending high-poverty schools: Results from a replication efficacy study.

A web-based intelligent tutoring system for reading comprehension delivered to fifth-grade students attending high-poverty schools: Results from a replication efficacy study.

Artificial Intelligence-Assisted Music Education: A Critical Synthesis of Challenges and Opportunities

Artificial intelligence (AI) is a hot topic that presents new challenges and opportunities for the improvement of educational processes. The disruptive and transformative force of this new technological development implies the adaptation of educational ecosystems for its use and integration as a didactic and pedagogical resource. From this perspective, a systematic literature review has been conducted to analyze the didactic potential of generative AI tools in the field of promoting artistic creativity in music education. The research results confirm that the incorporation of AI in music education is paving the way for a more personalized, interactive and efficient learning experience. In addition, the analysis suggests nine fundamental fields of IA implementation in music education: virtual and augmented reality (VR; VA); learning personalization, intelligent tutoring systems; composition assistants; improved historical and contextual learning; assessment systems; interactive ear training and music theory systems; tools for music collaboration and performance; and assistive technologies. Furthermore, the challenges presented by the intersection of AI and digital didactics in the field of music education are discussed.

The effects of Generative Artificial Intelligence on Intelligent Tutoring Systems in higher education: A systematic review

The effects of Generative Artificial Intelligence on Intelligent Tutoring Systems in higher education: A systematic review

ChatGPT in Higher Education: A Comprehensive Study of the Literature. Friend or Foe?

This paper presents a qualitative, high-quality State-of-the-Art (SotA) extended literature review on integrating ChatGPT, an advanced and contemporary natural language processing model, a sophisticated AI-powered chatbot that OpenAI developed in higher education. Snyder (2019, p.1) emphasised that "a literature review as a research method is more relevant than ever”. According to Barry et al (2022, p. 663), "the fundamental purpose of SotA literature review is to create a 3-part argument about the state of knowledge for a specific phenomenon: This is where we are now. This is how we got here. This is where we could go next”. This 3-part argument will underpin this study. With the rapid advancement of artificial intelligence (AI) and its potential to transform various sectors, including education, integrating AI-powered tools like ChatGPT to enhance teaching and learning has become a topic of significant interest. This study explores the diverse applications of ChatGPT in higher education, including pedagogical practices, student engagement, intelligent tutoring systems, personalised learning, language learning support, academic research, feedback, grading, and administrative support. Additionally, it identifies the challenges and ethical concerns this technology poses, focusing on bias, privacy, data security, reliability, accountability, accuracy and the balance between human interaction and artificial intelligence. Analysing the literature on ChatGPT’s role in higher education presents future directions for implementing it in higher education settings, ushering in an era of exciting possibilities. The research concludes that higher education institutions should provide clear and transparent guidelines for their staff and students on using ChatGPT responsibly for academic purposes to mitigate any ethical concerns.

O POTENCIAL DA INTELIGÊNCIA ARTIFICIAL NA APRENDIZAGEM EDUCATIVA

The use of artificial intelligence (AI) in education has emerged as a topic of growing importance as educational institutions seek to innovate and enhance their pedagogical practices. This study aims to explore how AI can reconfigure learning processes and transform traditional educational practices. In a context where personalized learning is vital, AI stands out as a powerful tool to meet the individual needs of students, promoting the development of essential 21st-century skills. The bibliometric analysis conducted reveals a significant increase in academic interest in the application of AI in education, especially in the last five years, indicating an awakening to technological possibilities. The main areas of investigation include personalized learning, intelligent tutoring systems, and educational data analysis, known as learning analytics, which allow real-time monitoring of student progress. AI offers immediate feedback, adapting to each student’s learning pace, improving academic performance, and reducing educational inequalities. However, for this transformation to occur effectively, it is crucial that educational institutions integrate these technological solutions into their pedagogical approaches. However, the research points to significant gaps, such as unequal access to AI technologies, limiting the democratization of benefits. The lack of infrastructure and specialized teacher training hinders effective implementation. Additionally, there is a shortage of studies focused on the development of socio-emotional skills. Therefore, research must advance in the search for new technologies, ensuring an equitable and enriching learning environment. The future of education, transformed by AI, promises to be a space where all students can thrive, developing skills to face the challenges of the 21st century.

Intelligent Tutoring Systems in Mathematics Education: A Systematic Literature Review Using the Substitution, Augmentation, Modification, Redefinition Model

Scholars have claimed that artificial intelligence can be used in education to transform learning. However, there is insufficient evidence on whether intelligent tutoring systems (ITSs), a representative form of artificial intelligence in education, has transformed the teaching and learning of mathematics. To fill this gap, this systematic review was conducted to examine empirical studies from 2003 to 2023 that used ITSs in mathematics education. Technology integration was coded using the substitution, augmentation, modification, redefinition (SAMR) model, which was extended to suit ITSs in a mathematics education context. How different contexts and teacher roles are intertwined with SAMR levels were examined. The results show that while ITSs in mathematics education primarily augmented existing learning, recent ITS studies have transformed students’ learning experiences. ITSs were most commonly applied at the elementary school level, and most ITS studies focused on the areas of number and arithmetic, algebra, and geometry. The level of SAMR varied depending on the research purpose, and ITS studies in mathematics education were mainly conducted in a way that minimized teacher intervention. The results of this study suggest that the affordance of an ITS, the educational context, and the teacher’s role should be considered simultaneously to demonstrate the transformative power of ITSs in mathematics education.

Influences Personalisation and Student Engagement in the AI Era: Exploring Effects and Influences

The study is based on a conceptual model to examine the integration of artificial intelligence (AI) technologies in education and their impact on student engagement. This model structures the analysis around several axes: AI technologies, including intelligent tutoring systems (ITS), adaptive learning platforms, and educational chatbots, play a key role in personalizing learning paths, making pedagogical support more accessible, and adapting content to students' specific needs. Student engagement is thus assessed through the personalization of pathways and the accessibility of support, while taking into account individual moderating factors such as learning styles, self-motivation, and prior experience with AI technologies, which influence the effectiveness of these tools. In addition, the study examines contextual conditions, including the importance of adequate technological infrastructure and teacher training, which are essential for the successful integration of AI technologies into pedagogical practices. This conceptual model guides the study in evaluating the assumptions made, providing an in-depth understanding of the interactions between these variables and making recommendations to optimize the use of AI technologies in education.

Unlocking the power of immersive learning: The FAIRI instructional design proposition for adaptive immersive virtual reality

Immersive Virtual Reality (IVR) is a promising tool for providing immersive and adaptive learning experiences by simulating realistic work environments and gathering data on learner's interactions to offer personalized instructional content. However, IVR-supported instruction presents unique challenges due to its heightened immersion, including managing the learner's cognitive load and self-regulation. Incorporating adaptivity into IVR can mitigate these challenges by regulating the complexity of the virtual environment, reducing cognitive strain, and maintaining learner focus through real-time feedback for both learners and instructors. However, recent reviews highlight the underutilization of adaptivity within IVR research, making it difficult to inform the design of adaptive IVR instruction.This paper proposes FAIRI, an instructional design for adaptive IVR in domain-specific training. FAIRI synthesizes recent IVR research relevant to the design of adaptivity for IVR through a purposive literature review, guided by the instructional design elements of the Four-Component Instructional Design-model (4C/ID) and Intelligent Tutoring Systems (ITS). The instructional design elements of the 4C/ID-model and ITS are evaluated for their potential viability and further refined for adaptive IVR-supported instruction based on recent IVR studies. Two key design principles are identified for adaptive IVR: limiting IVR to tasks that benefit from its heightened realism and leveraging its adaptive features to manage the intensity of the experience. Additionally, the application of FAIRI's design principles and elements are demonstrated through a forklift operator training case in IVR. FAIRI aims to catalyse further research into adaptivity for IVR by proposing and aligning promising instructional design elements for adaptive IVR instruction.

Exploring How Education Can Leverage Artificial Intelligence for Social Good

Artificial intelligence (AI) is rapidly transforming society and industries, presenting both opportunities and ethical challenges. AI enables machines to perform tasks traditionally done by humans, such as natural language processing, pattern recognition, decision-making, and problem-solving (Brookings, 2023). In education, AI enhances teaching methodologies, student assessment, and administrative tasks through tools like intelligent tutoring systems, adaptive learning platforms, and educational chatbots. These tools offer customised learning experiences, immediate feedback, and data-driven insights. This research aims to investigate how AI can be leveraged within education to promote social good by identifying how familiar educators and students are with AI tools, identify how educators and students perceive the role of AI in education and what are the current applications of AI technologies in educational settings and how widely are they used. Finally, discuss the opportunities and ethical considerations of integrating AI in education. AI technologies can address critical social challenges such as inequality, accessibility, and personalised learning. According to Luckin et al. (2016), "AI can provide tailored educational experiences that adapt to individual learning needs, thus promoting equity in education." This exploratory research begins with an overview of AI's role and tools in education, followed by a discussion of the challenges, opportunities, and ethical considerations associated with AI integration. Understandings are drawn from educator’s response to a questionnaire and a focus group with first year and final-year third level students. This qualitative data, analysed using NVivo software, reveals key themes and significant findings on effectively utilising AI in education.

Exploring the Ethical Dimensions of Artificial Intelligence and Robotics in Dental Education

Artificial intelligence (AI) and robotics have revolutionized healthcare, particularly dentistry. Their integration in dental education offers opportunities to enhance learning, diagnostics, treatment planning, and patient care. However, ethical implications must be addressed to ensure responsible and ethical integration of these technologies. This review explores AI and robotics in dental education and highlights the associated ethical considerations. These technologies provide improved learning experiences and simulations. Intelligent tutoring systems offer personalized feedback, virtual reality simulations enable practice in a safe environment, and AI algorithms aid in analysing radiographic images. Despite their potential, ethical challenges arise, including data privacy, autonomy, equity, and professional integrity. Addressing these challenges requires transparency, informed consent, bias detection, and accountability. Dental curricula should in-corporate ethics, fostering collaborations between educators and AI/robotics experts. Professional development programs should prioritize ethics training, considering emerging technologies such as AI-powered learning and diagnostic assistance. By embracing ethical considerations, AI and robotics can be integrated in dental education guided by transparency, accountability, privacy, and patient-centric care. A comprehensive understanding of the ethical dimensions is essential to harness the transformative potential of AI and robotics while upholding ethical standards in dental education. Bangladesh Journal of Medical Science Vol. 23 No. 04 October’24 Page : 999-1007

THE ROLE OF ARTIFICIAL INTELLIGENCE IN EDUCATION

The integration of Artificial Intelligence (AI) into education is transforming the landscape of teaching and learning, offering innovative solutions and personalized learning experiences. This article explores the multifaceted role of AI in education, beginning with its historical evolution and current applications, including personalized learning, intelligent tutoring systems, and administrative automation. The benefits of AI, such as enhanced learning opportunities and improved student engagement, are weighed against challenges like equity issues and ethical concerns.

Leveraging AI to Transform Online Higher Education: Focusing on Personalized Learning, Assessment, and Student Engagement

The proliferation of online higher education has underscored the need for innovative approaches to enhance student learning, engagement, and success. This paper explores the transformative potential of artificial intelligence (AI) in revolutionizing online education. By focusing on personalized learning, AI-driven assessment, and student engagement, this research investigates how AI technologies can create tailored educational experiences, optimize learning outcomes, and foster a dynamic online learning environment. The study delves into the implementation of AI-powered tools, such as intelligent tutoring systems, adaptive learning platforms, and predictive analytics, to address individual student needs, provide timely feedback, and promote active participation. Through a comprehensive analysis of the existing literature and emerging trends, this paper aims to identify key challenges, opportunities, and best practices for leveraging AI to optimize online higher education, ultimately contributing to improved student satisfaction, retention, and academic achievement.

Revolutionizing Education: The Impact of AI-Enhanced Teaching Strategies

In recent years, the integration of Artificial Intelligence (AI) into educational settings has revolutionized traditional teaching methodologies, leading to the development of innovative teaching strategies. This paper explores how AI-enhanced approaches are transforming education by personalizing learning experiences, improving student engagement, and optimizing instructional methods. Through adaptive learning platforms, AI tailors educational content to meet individual student needs, fostering a more inclusive and effective learning environment. Intelligent tutoring systems provide real-time feedback and support, enabling students to progress at their own pace and achieve mastery in various subjects. Additionally, AI-driven analytics offer educators valuable insights into student performance and learning patterns, facilitating data-informed decisionmaking and targeted interventions. By leveraging AI technologies, educators can create dynamic, interactive, and studentcentered classrooms that cater to diverse learning styles and preferences. This paper discusses the potential benefits and challenges of AI-enhanced teaching strategies and highlights the importance of integrating AI thoughtfully and ethically to maximize its positive impact on education.

Online Learning Strategy Induction through Partially Observable Markov Decision Process-Based Cognitive Experience Model

Inducing learning strategies is a crucial component of intelligent tutoring systems. Previous research has predominantly focused on the induction of offline learning strategies. Although the existing offline learning strategy induction methods can also be used for real-time updates of learning strategies, their update efficiency is not high, making it difficult to capture the characteristics exhibited by learners during the learning process in a timely manner. With the superior performance of the Partially Observable Markov Decision Process (POMDP), this paper proposes a POMDP-based cognitive experience model, which can be quickly updated during interactions and enables the real-time induction of learning strategies by weighting the learning experiences of different learners. Experimental results demonstrate that the learning strategies induced by PCEM are more personalized and exhibit superior performance.

The Impact of AI-Assisted Teaching on Students' Learning and Psychology

The integration of artificial intelligence (AI) in education has garnered significant attention, yet its implications on student outcomes require further exploration. This paper analyzes the impact of AI-assisted teaching on students' learning and psychological well-being. Through a comprehensive review of existing AI applications in education, including intelligent tutoring systems and educational chatbots, the study examines how these technologies influence academic performance and psychological health. The findings indicate that AI-assisted teaching enhances learning outcomes by providing personalized educational experiences and fostering student engagement. Furthermore, AI systems contribute positively to students' psychological well-being by offering timely emotional support and personalized stress management strategies. However, challenges such as technological limitations, resistance to change, and privacy concerns persist. Based on these insights, the paper suggests actionable recommendations for the effective integration of AI in education, emphasizing the need for robust infrastructure, continuous educator training, and ethical considerations to maximize benefits while mitigating potential risks.

Enhancing python learning with PyTutor: Efficacy of a ChatGPT-Based intelligent tutoring system in programming education

Programming is regarded as a focal point in the current rapidly evolving educational landscape. To aid learning in this domain, we developed PyTutor, an innovative intelligent tutoring system (ITS) that is designed to assist beginners in Python programming. PyTutor utilizes the ChatGPT model to offer continuous guidance, problem-solving hints, and detailed code explanations. It features a structured hint system for each question, covering pseudocode, cloze, basic, and advanced coding solutions. In our 11-week experiment, we compared 35 students who used PyTutor with 36 students who did not. The results indicated the effectiveness of PyTutor, particularly for students with weak foundations in programming. Those with lower initial knowledge exhibited higher engagement, completion rates, and success rates in in-class and after-class programming exercises. Nevertheless, we observed a potential risk of overreliance on PyTutor among students, which may impede the development of independent problem-solving skills. Thus, we recommend the balanced usage of PyTutor. In conclusion, PyTutor is a valuable ITS in programming education that considerably improves the learning outcomes of beginners. Its tailored approach renders it a promising tool for bridging knowledge gaps and enhancing overall educational experiences in the field of programming.

The Fourth Educational Revolution and the Impact of AI on Pedagogy

This study explores the capacity of Artificial Intelligence (AI) to revolutionize educational pedagogy, aiming to foster a more personalized and effective learning experience. Methodology: Through a bibliometric analysis of publications in Scopus and Web of Science, the research examines AI's impact on adaptive learning, personalized instruction, and effective teaching methods. It also evaluates AI's role in assessment, creation of simulated learning environments, and widening access to education, while addressing the integration challenges. Conclusions: The investigation demonstrates that AI has considerable potential to improve teaching and learning. AI-driven tools like intelligent tutoring systems, machine learning algorithms, and virtual simulations can facilitate a learner-centric approach. Nonetheless, it also highlights the necessity to confront issues such as technological overdependence, privacy concerns, and the potential displacement of teachers. A thoughtful adoption of AI in education could lead to a more equitable and engaging learning system. The advent of this educational revolution demands a focus on augmenting human capabilities with AI and transforming pedagogical strategies to enhance student outcomes. The research advocates for further development of ethical standards and informed policies to guide AI integration in the educational sector.

The influence of e-learning on exam performance and the role of achievement goals in shaping learning patterns

Digital learning environments provide opportunities to support learning in higher education. However, it is yet unclear why and how learners use these opportunities. We propose that learners’ achievement goals and their beliefs regarding the instrumentality of e-learning tools to achieve those goals are predictive for learning behavior within digital learning environments. Furthermore, we assume learning behavior characterized by longer overall learning time, more distributed learning, and less learning delay predicts higher exam performance. To test these hypotheses, we analyzed log-file data of 91 university students who had used an intelligent tutoring system as exam preparation in a pre-registered study. Beyond the overall predictive validity of the intelligent tutoring system, we found a negative association between learning delay and exam performance. Achievement goals predicted learning time and time distribution, an association that was partly moderated by perceived instrumentality. This suggests that goals and beliefs are important puzzle pieces for understanding e-learning (behavior).

Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research Literature

The integration of Artificial Intelligence (AI) into teacher education has been transformative, offering personalized learning experiences, enhanced professional development, and improved teaching methodologies. AI technologies such as Intelligent Tutoring Systems (ITS), AI-driven analytics, and automated assessment tools have become central to modern educational practices, significantly improving engagement, adaptability, and effectiveness. This study employs a qualitative thematic analysis of current literature on AI in teacher education, examining peer-reviewed articles and reports using thematic coding to identify key patterns, opportunities, and challenges. The findings reveal that AI enhances teacher education by providing personalized learning pathways, fostering critical thinking, and supporting ongoing professional growth. Technologies like ITS, Virtual Reality (VR), and AI-driven analytics have proven effective in promoting motivation and engagement among teachers. However, ethical challenges such as biases in AI systems and concerns regarding data privacy require continuous attention. Furthermore, a gap in teacher preparedness, particularly in developing AI literacy and integrating AI tools into classroom practices, is evident. Despite these challenges, AI offers substantial benefits, transforming teaching practices and enabling personalized, adaptive instruction that supports both teachers and students. The study emphasizes the need for comprehensive teacher training programs focusing on digital literacy and ethical AI use, ensuring educators can navigate an AI-enhanced educational environment effectively. This research contributes to the ongoing discourse by highlighting the need for ethical guidelines and robust teacher training programs, offering actionable insights for educators, policymakers, and institutions aiming to integrate AI into teacher education

Intelligent Tutoring Systems, Generative Artificial Intelligence (AI), and Healthcare Agents: A Proof of Concept and Dual-Layer Approach.

This study introduces a novel methodology for enhancing intelligent tutoring systems (ITS)through the integration of generative artificial intelligence (GenAI) and specialized AIagents. We present a proof of concept (PoC) demo that implements a dual-layer GenAI validation approach that utilizes multiple large language models to ensure the reliability and pedagogical integrity of the AI-generated content. The system features role-specific AI agents, a GenAI-powered scoring mechanism, and an AI mentor that provides periodic guidance. This approach demonstrates capabilities in dynamic scenario generation and real-time adaptability while addressing key challenges in AI-driven education, such as personalization, scalability, and domain-specific knowledge integration. Although exemplified here through a case study in healthcare root cause analysis, the methodology is designed for broad applicability across various fields. Our findings suggest that this approach has significant potential for advancing adaptive learning and personalized instruction while raising important considerations regarding ethical AI application in education. This work provides a foundation for further research into the efficacy and impact of GenAI-enhanced ITS on learning outcomes and instructional design across diverse educational domains.