Intelligent Tutoring Systems Research Articles

PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming

Recent advancements in intelligent tutoring systems (ITS) driven by artificial intelligence (AI) have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners’ backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to support the effective attainment of learning objectives has become a critical challenge. This paper introduces personalized fuzzy logic strategies for intelligent programming tutoring (PerFuSIT), an innovative fuzzy logic-based module designed to select the most appropriate tutoring strategy from five available options, based on individual learner characteristics. The available strategies include revisiting previous content, progressing to the next topic, providing supplementary materials, assigning additional exercises, or advising the learner to take a break. PerFuSIT’s decision-making process incorporates a range of learner-specific parameters, such as performance metrics, error types, indicators of carelessness, frequency of help requests, and the time required to complete tasks. Embedded within the traditional ITS framework, PerFuSIT introduces a sophisticated reasoning mechanism for dynamically determining the optimal instructional approach. Experimental evaluations demonstrate that PerFuSIT significantly enhances learner performance and improves the overall efficacy of interactions with the ITS. The findings highlight the potential of fuzzy logic to optimize adaptive tutoring strategies by customizing instruction to individual learners’ strengths and weaknesses, thereby providing more effective and personalized educational support in programming instruction.

AI, Personalized Education, and Challenges

Artificial Intelligence (AI) is gaining traction in education, with potential applications that range from personalized learning to automated administrative tasks. However, the integration of AI into educational systems is not without its challenges. This paper explores both the opportunities and obstacles that AI presents in the field of education, particularly through the use of Intelligent Tutoring Systems (ITS) and Adaptive Learning Management Systems (ALMS). These technologies aim to tailor learning experiences to individual students by analyzing data and adjusting content to suit their needs. While this personalized approach could enhance student engagement and comprehension, it relies on vast amounts of data, raising concerns about privacy and the potential misuse of personal information. Moreover, AI’s impact on education extends to supporting educators by providing insights into student performance and automating routine tasks. However, the effectiveness of AI systems in this regard remains questionable, particularly when considering the limitations in current AI models and the challenges of integrating them into existing educational frameworks. The risk of algorithmic bias is also a critical issue, as AI systems can inadvertently reinforce inequalities present in the data they are trained on, leading to unfair or discriminatory outcomes. Additionally, while AI promises to streamline certain aspects of education, there are concerns that over-reliance on technology could depersonalize the learning process. Human educators play a crucial role in fostering not only intellectual growth but also emotional and social development—elements that AI systems are currently unable to replicate. This paper argues that while AI holds significant promise in education, its deployment must be carefully managed, with attention to ethical considerations, equity in access, and the preservation of the human elements essential to effective learning. Addressing these challenges is key to ensuring that AI contributes meaningfully to education rather than exacerbating existing issues.

From shy to fly: Facilitating EFL learners’ willingness to communicate with an AI chatbot and an intelligent tutoring system

Willingness to communicate (WTC) has been identified as a crucial factor in improving EFL learners' speaking. However, enhancing WTC often remains a major challenge. The recent emergence of artificial intelligence (AI) has been heralded for its transformative potential, yet there is a lack of empirical evidence regarding how this development influences WTC. To address this, our pioneering study investigates the impact of two major AI tools on EFL learners from China, one involving a chatbot and the other an intelligent tutoring system (ITS). 82 first-year undergraduates participated in the study and were assigned to two groups: the AI chatbot group and the ITS group. Pre- and post-session WTC questionnaires, reflection journals and a teacher interview were conducted to collect data. Results revealed significant disparities in WTC changes among the groups. Specifically, the chatbot group displayed significant improvements, while the WTC of the ITS group surprisingly declined. The study delves into the reasons behind these contrasting performances. It concludes by proposing practical implications based on the findings.

Artificial Intelligence in Engineering Education: A Review of Pedagogical Innovations

This study critically evaluates the integration of artificial intelligence (AI) into engineering education, with a specific focus on its transformative role within the Digital-Intelligent Era. Drawing on General Systems Theory (GST), this research synthesizes insights from 82 empirical studies spanning 2011 to 2024, exploring AI's impact on the evolution of educational frameworks. The study identifies six key application domains of AI within engineering education, including intelligent tutoring systems, adaptive learning platforms, virtual laboratories, and personalized curriculum design. It highlights the synergy between AI and other emerging technologies, such as 5G, Cloud Computing, and Big Data, driving pedagogical innovation and enhancing the learning experience. Additionally, the paper addresses challenges related to the implementation of AI-based educational strategies, including infrastructure limitations, resistance to change, and equity concerns. Finally, it offers strategic solutions to overcome these challenges, fostering a more inclusive and effective educational environment.

The dual role of AI in personalized learning: Enhancements and hindrances for diverse learners

This study examines the dual role of Artificial Intelligence (AI) in personalized learning, exploring how AI can both advance and hinder individualized educational experiences for diverse learners. AI technologies—such as adaptive learning platforms, intelligent tutoring systems, and data analytics tools—offer tailored pathways that can increase engagement, accommodate learning differences, and improve academic outcomes. However, challenges also emerge, including algorithmic bias, over-reliance on technology, and potential compromises in data privacy and human interaction. Findings indicate that while AI can enhance personalized learning, it requires careful integration to avoid exacerbating disparities and to support critical thinking and social skills. The role of educators remains essential, with recommendations for professional development to equip teachers with the skills to leverage AI effectively and ethically. This study highlights the importance of a balanced approach in AI integration, combining technological tools with human-centered teaching practices to create inclusive, equitable, and effective learning environments.

Idiographic learning analytics: Mapping of the ethical issues

This paper examines the ethical implications of idiographic learning analytics (LA), a personalized approach to learning where individual student data serves as the primary reference frame for analysis. By focusing on the unique characteristics of each learner, idiographic LA offers tailored educational experiences that could potentially enhance learning outcomes. This personalized approach offers new possibilities to mitigate common ethical challenges in nomothetic LA. This study explores the ethical dimensions of idiographic LA by integrating theories and lessons learned from implementing idiographic approaches in the fields of quantified self, recommender systems, intelligent tutoring systems, and artificial intelligence personalization. We employ a consequentialist ethical perspective to assess the implications of idiographic practices in education, highlighting potential benefits and risks and mapping the main ethical issues differentiating idiographic LA from nomothetic LA. The paper provides several points of consideration to ensure ethical implementations of idiographic LA.

Analyzing Learning Persistence Determinants in Virtual Learning Environments Using Mixed Methodology

This research analyzes the impact of various factors and their relatable aspects, such as contextual (academic), external (environmental), and internal (personal) of students' learning persistence in virtual environments as critical aspects of online learning advancement and academic success. Utilizing a mixed methodology approach that combines qualitative and quantitative methods, this study leverages an educational dataset from Harvard and MIT, employing a multinomial logistic regression (MLR) model to detect the correlation among diverse learning factors and online persistence classes with the triple class label (positive persistence, negative persistence, and absence of persistence). The classification accuracy achieved in the MLR model reached 96%. Supporting evidence from online surveys and expert interviews further underscores the combined influence of academic, demographic, and environmental factors on learning persistence. The quantitative analysis highlights the significant influence of contextual factors, particularly achievements and activities, on learning persistence, while internal factors like gender and birth year have minimal impact. Qualitative analysis reveals a mix of internal, external, and contextual factors affecting persistence, including academic support, peer communication, and family support. Expert interviews confirm the importance of multidimensional features and highlight challenges ranging from technological access to varied learning styles. Suggestions for enhancing persistence include implementing supportive online tools and intelligent tutoring systems. This research's contribution lies in its holistic examination of online persistence-influencing factors through a mixed methodology lens, facilitating the identification of learning persistence; it aims to enhance support mechanisms for students in virtual learning environments, potentially improving educational outcomes and online learning experience. This innovative approach underscores the importance of understanding and addressing the various influencers of learning persistence to foster more successful outcomes in digital education contexts.

Parent Teaching Using the Enhanced Moved by Reading to Accelerate Comprehension in English Intelligent Tutoring System to Teach Question-Asking During Shared Book Reading in Latino Families.

The Parent-Enhanced Moved by Reading to Accelerate Comprehension in English (Parent EMBRACE) program offers a bilingual parent-training literacy intervention for Latino families. Within the context of shared book reading, the application leverages both the home language and technology to increase parent question-asking during shared reading. Research goals were to (a) examine the potential of the Parent EMBRACE tutoring system at teaching parents to increase the quantity and variety of their question-asking during shared book reading, (b) examine changes to parents' reading attitudes or motivation, and (c) examine whether children's reading attitude is correlated with parent interactions. Twenty-one participants were randomized into three conditions: a digital storybook (DS) group (n = 7), an interactive storybook (EMBRACE) group (n = 6), and a parent-teaching interactive storybook (Parent EMBRACE) group (n = 8). Participants received iPads with digital storybooks for use during the intervention (in which the parent-teaching group received prompts from the app to ask questions while reading). Shared book reading assessments before and after the intervention involved hard-copy books, and behaviors were analyzed using video-recorded reading sessions before and after the intervention. Group differences were explored using descriptive analysis. Reading attitude and motivation were measured through pre- and post-intervention surveys. The relationship between parent interactions and reading attitudes was explored through regression. Results indicate that after the intervention, four out of seven parents in the parent-teaching interactive storybook group asked more questions to their children. Parents' reading attitudes and motivations did not significantly change. There was a nonlinear relationship with parent interactions and children's reading attitude. Overall, the Parent EMBRACE tool shows feasibility and warrants further study on its efficacy as a linguistically responsive literacy-based language intervention for Latino parents to develop shared book reading strategies.

Exploring Filipino Medical Students’ Attitudes and Perceptions of Artificial Intelligence in Medical Education: A Mixed-Methods Study

Background Artificial intelligence (AI) is emerging as one of the most revolutionary technologies shaping the educational system utilized by this generation of learners globally. AI enables opportunities for innovative learning experiences, while helping teachers devise teaching strategies through automation and intelligent tutoring systems. The integration of AI into medical education has potential for advancing health management frameworks and elevating the quality of patient care. However, developing countries, including the Philippines, face issues on equitable AI use. Furthermore, medical educators struggle in learning AI which imposes a challenge in teaching its use. To address this, the current study aims to investigate the current perceptions of medical students on the role of AI in medical education and practice of medicine. Methods The study utilized a mixed-methods approach to quantitatively and qualitatively assess the current attitudes and perceptions of medicine students of AI. Quantitative assessment was done via survey and qualitative analysis via focus group discussion. Participants were composed of 20 medical students from the College of Medicine, University of the Philippines Manila. Results Analysis of the attitudes and perceptions of Filipino medical students on AI showed that participants had a baseline understanding and awareness, but lack opportunities in studying medicine and clinical practice. Majority of participants recognize the advantages in medical education but have reservations on its overall application in a clinical setting. Conclusions The results of this investigation can direct future studies that aim to guide educators on the emerging role of AI in medical practice and the healthcare system, on its effect on physicians-in-training under contemporary medical educational practices. Findings from our study revealed key focal points which need to be sufficiently addressed in order to better equip medical students with knowledge, tools, and skills needed to utilize and integrate AI into their education and eventual practice as healthcare professionals.

PERSONALIZATION OF LEARNING WITH ARTIFICIAL INTELLIGENCE: HOW AI IS TRANSFORMING EDUCATION AND CURRICULUM

This research analyzed the impact of Artificial Intelligence (AI) on the personalization of learning and its influence on the transformation of teaching and curriculum. The central problem investigated was to identify the main ways in which AI is changing pedagogical practices and curricular structures. The overall objective was to analyze the applications of AI in the personalization of learning in the educational context, highlighting its implications for teaching and curriculum development. The methodology used was a literature review, with a qualitative approach, including the analysis of published materials such as books, scientific articles, theses and official documents. The results indicated that AI is providing significant opportunities for the personalization of learning, allowing the adaptation of content and teaching pace to the individual needs of students. AI applications range from intelligent tutoring systems to platforms for predictive analysis of student performance. The research highlighted the importance of a balanced approach that considers both the benefits and the ethical challenges of implementing AI in education. The concluding remarks pointed out that, despite promising advances, the effective integration of AI in education requires a reformulation of traditional pedagogical and curricular models. Investments in teacher training, development of technological infrastructure and the development of appropriate educational policies are essential to maximize the benefits of AI in personalizing learning. The need for future studies was highlighted to explore the long-term impacts of AI in education and develop best practices for its implementation.

Unpacking help-seeking process through multimodal learning analytics: A comparative study of ChatGPT vs Human expert

Help-seeking is an active learning strategy tied to self-regulated learning (SRL), where learners seek assistance when facing challenges. They may seek help from teachers, peers, intelligent tu-tor systems, and more recently, generative artificial intelligence (AI). However, there is limited empirical research on how learners’ help-seeking process differs between generative AI and hu-man experts. To address this, we conducted a lab experiment with 38 university students tasked with essay writing and revising. The students were randomly divided into two groups: one seeking help from ChatGPT (AI Group) and the other from an experienced teacher (HE Group). To examine their help-seeking processes, we used a combination of statistical testing and process mining methods, analyzing multimodal data (e.g., trace data, eye-tracking data, and conversa-tional data). Our results indicated that the AI Group exhibited a nonlinear help-seeking process, such as skipping evaluation, differing significantly from the linear model observed in the HE Group which also aligned with classic help-seeking theory. Detailed analysis revealed that the AI Group asked more operational questions, showing pragmatic help-seeking activities, whereas the HE Group was more proactive in evaluating and processing received feedback. We discussed factors such as social pressure, metacognitive off-loading, and over-reliance on AI in these different help-seeking scenarios. More importantly, this study offers innovative insights and evidence, based on multimodal data, to better understand and scaffold learners learning with generative AI.

Learning Languages in the 21st Century: the Digital and Ai Transformation

The article deals with the problem of the learning languages in the 21st century: the digital and AI transformation. The aim of this article is to explore and clarify the transformative impact of digital technologies on foreign language teaching. This article explores the evolution of language learning from classroom-based methods to the incorporation of digital instruments, Artificial Intelligence (AI), and Information and Communication Technologies (ICT). Digital tools such as language learning applications, online dictionaries, multimedia resources, and language exchange platforms have made language acquisition more accessible and engaging. AI technologies introduce intelligent tutoring systems, personalized learning experiences, and advanced language processing capabilities, while ICT tools like Learning Management Systems (LMS) and virtual classrooms facilitate effective communication and information processing. The benefits of these advancements are multifaceted, including increased accessibility and flexibility, enhanced engagement and motivation, personalized learning paths, and improved collaboration and communication. However, challenges such as the digital divide, variability in resource quality, data privacy concerns, and the need for continuous teacher training must be addressed. Looking ahead, future trends such as augmented and virtual reality, blockchain technology, AI-driven content creation, IoT integration, and multimodal learning experiences promise to further transform the landscape of language education. These innovations will pave the way for more immersive and interconnected language learning experiences, contributing to a more multilingual global society.

Action research plan: a methodology to examine the impact of artificial intelligence (AI) on the cognitive abilities of university students

This research suggests a methodology to examine the effectiveness Artificial Intelligence (AI) on the cognitive abilities of college students so that future researchers can utilize this experimental project to focus on how AI-powered Intelligent Tutoring Systems (ITSs) affect learning outcomes. As AI continues to revolutionize all walks of life, so has its integration with education. ITS is a key application of AI in education, providing a personalized learning experience by analyzing student data and tailoring instructional materials accordingly. This provides a research program to study the effectiveness of ITS in improving cognitive skills such as memory, critical thinking, and problem solving abilities in college students. The research project combined quantitative and qualitative research methods including surveys, pre-tests, post-tests, and in-depth interviews to assess the cognitive differences between students using ITS and those using traditional learning methods. The research project also recognized potential challenges, such as dependence on technology and the risk of increased educational inequality. The action research program concluded by advocating for a balanced integration of AI into education, highlighting the need for ongoing research to optimize its use across different stages of education and to ensure equitable access to AI-powered tools.

Application of Academic Analytical Models in Education Management

Objective: The primary objective of the current study is to provide an analytical systematic review on the application of academic analytical models in educational management and how the related techniques can be used to enhance quality outcomes and learning processes. Methodology: The study adopted the methodological framework outlined by the Joanna Briggs Institute (2015) and was informed by Arksey and O’Malley’s (2005) approach of summary and dissemination of research findings and, in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Findings: Learning analytics has emerged as the most relevant educational concept with the potential of collecting, analyzing and reporting data on learners, educators and administrators for efficient educational management processes. Analytics in education enhances the capabilities of the educators of determining the learners’ performances and identification of areas for improvement to make data-driven learning decisions. Conclusions: The application of analytical models in educational management is based on different domains such as customized learning, intelligent tutoring systems, predictive modelling, automated grading and assessments, and natural language processing. Received: 26 July 2024 / Accepted: 25 October 2024 / Published: 05 November 2024

Effectiveness of gamified intelligent tutoring in physical education through the lens of self-determination theory

Scholars have recommended the application of an intelligent tutoring and instant feedback system (ITIFS) to enhance students’ motor skills performance by automatically evaluating their learning performance and providing personalized guidance and feedback. However, solely providing personalized evaluation and feedback may not necessarily attract students' active and sustained engagement in practice. In particular, it is difficult to arouse students' enthusiasm to participate in sports that require repetitive practice to improve their physical abilities and which involve less interaction with the environment and their opponents. To address this issue, grounded in self-determination theory (SDT), the present study integrated a gamification mechanism that aligned with students’ psychological needs into an ITIFS. The gamification features included avatars, achievements (personal ratings and rankings), badges, levels, and social networks (group ratings and rankings). It aimed to attract students to engage continuously in practice, and to address the issue of students lacking motivation to engage in repeated practice. To investigate the effectiveness of the proposed method, a quasi-experimental research design was adopted, and the collected data were analyzed with analysis of covariance (ANCOVA), independent samples t tests and qualitative coding. Two classes of university students participated in the experiment. One class (N = 80) was the experimental group adopting the SDT-based gamified ITIFS (G-ITIFS), and the other class (N = 76) was the control group adopting the conventional ITIFS (C-ITIFS). The findings indicated that the experimental group showed significantly better yoga skills performance and learning engagement compared to the control group. Feedback from students also revealed that the gamification mechanism provided more excitement and had positive impacts, satisfying students’ psychological needs and reinforcing the learning benefits. The findings of the present study revealed that, from the perspective of SDT, incorporating gamification elements into the development of ITIFS could be a promising approach for physical education. Therefore, it is strongly encouraged that educators promote such a gamified intelligent tutoring mode in physical education curriculums as it is crucial to the development of students' physical and mental health, as well as to their enthusiasm to participate in sports.

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.

Investigating Teachers’ Use of an AI-Enabled System and Their Perceptions of AI Integration in Science Classrooms: A Case Study

Recent research indicates the significant potential of artificial intelligence (AI) in enhancing teachers’ instructional practices in areas such as lesson planning, personalized teacher intervention and feedback, and performance assessment. To fully realize the potential of AI in teaching, it is crucial to understand how teachers innovatively apply and critically evaluate AI applications in their teaching practices. However, there is a research gap in investigating how teachers use various features of an AI-enabled system and their perceptions of AI integration in teaching to promote teachers’ effective AI integration practices. Employing an exploratory case study design, we investigated how six science teachers utilized an AI-enabled inquiry intelligent tutoring system (Inq-ITS) within their teaching and examined their perceptions of AI integration. Classroom observations and teacher interview data were collected. When using Inq-ITS functionalities, two teachers with a pedagogical orientation of teacher-guided scientific inquiry mainly engaged with its virtual tutor and teacher report summary features. Conversely, four teachers, practicing the pedagogical orientation of AI-guided scientific inquiry, relied on the AI system to guide student learning, interacting intensively with its features, particularly real-time teacher alerts and teacher inquiry practice support. Regardless of the differences in using Inq-ITS features, all teachers recognized the potential benefits of pedagogical change and encountered various challenges. This analysis also revealed that teachers exhibited distinct perceptions regarding the role of Inq-ITS integration in their teaching. Teachers who adopted a teacher-guided pedagogical orientation perceived the Inq-ITS as a supporting tool that enhanced traditional teaching methods. In contrast, those with an AI-guided pedagogical orientation viewed the Inq-ITS as akin to a teaching assistant and a pedagogical collaborator. The findings underscored the importance of enhancing teachers’ realization of the pedagogical affordance of AI in teaching through their use of AI functionalities. It is essential to consider teachers’ diverse perceptions of AI integration when promoting their integration of AI into teaching practices.

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.