Personalized Learning Support Research Articles

Improving students’ programming performance: an integrated mind mapping and generative AI chatbot learning approach

With the development of the times, programming education has become increasingly important for individual development. However, for programming beginners such as primary and secondary school students, learning programming is not a simple task and requires additional learning support. Generative AI (GenAI) chatbots are effective teaching aids that can reduce the learning difficulty of programming by providing real-time guidance and personalized learning support based on students’ abilities. Therefore, it has been a trend to apply GenAI chatbots in teaching. However, technology always has two sides. Over-reliance on these chatbots may weaken students’ ability to think independently and affect their learning effectiveness. Therefore, how to rationally utilize GenAI chatbots in the classroom and improve their application effectiveness has become an important issue for both researchers and frontline teachers. Based on this, the present study proposed a learning method that integrates mind mapping with GenAI chatbots. To assess the effectiveness of this learning method and to investigate whether there are differences in the impact of various types of mind mapping-supported GenAI chatbots on students’ programming academic performance, computational thinking, and programming self-efficacy, the research team conducted a quasi-experimental study. The participants were 111 seventh-grade students at a junior high school in southeastern China. Experimental Group 1 (36 students) used a learning approach that integrated progressive mind maps with a Generative AI chatbot, Experimental Group 2 (36 students) used a learning approach that integrated self-constructed mind maps with a GenAI chatbot, and the control group (39 students) used a traditional AI chatbot-based learning approach. The results showed that participants in both experimental groups had significantly better programming learning performance as well as computational thinking than the control group, and that the learning method integrating progressive mind mapping with GenAI chatbots was more effective.

Developing LLM-Powered Trustworthy Agents for Personalized Learning Support

Developing LLM-Powered Trustworthy Agents for Personalized Learning Support

Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2

Generative artificial intelligence (GAI) technologies are gaining traction in higher education, offering potential benefits such as personalized learning support and enhanced productivity. However, successful integration requires understanding the factors influencing students’ adoption of these emerging tools. This study investigates the determinants shaping higher education students’ adoption of GAI through the lens of the unified theory of acceptance and use of technology 2 framework. Data was collected from Pyatigorsk State University students and analyzed using structural equation modeling. The findings reveal habit (HB) as the most influential predictor of GAI adoption among students, followed by performance expectancy. Hedonic motivation, social influence (SI), and price value positively influenced behavioral intention (BI) to use these technologies. Surprisingly, facilitating conditions (FCs) exhibited a negative effect on BI, suggesting potential gaps in support systems. The study identifies no significant gender differences in the underlying factors driving adoption. Based on the results, recommendations are provided to foster HB formation, communicate benefits, enhance hedonic appeal, leverage SI, address price concerns, and strengthen FCs. Potential limitations include the cross-sectional nature of the data, geographic constraints, reliance on self-reported measures, and the lack of consideration for individual differences as moderators. This research contributes to the growing body of knowledge on GAI adoption in educational contexts, offering insights to guide higher education institutions in responsibly integrating these innovative tools while addressing student needs and promoting improved learning outcomes.

Long-Term Implementation of a Personalized Learning Support System in Fisheries High Schools and Evaluation of Educational Effectiveness

Long-Term Implementation of a Personalized Learning Support System in Fisheries High Schools and Evaluation of Educational Effectiveness

Artificial intelligence chatbots in endodontic education-Concepts and potential applications.

The integration of artificial intelligence (AI) into education is transforming learning across various domains, including dentistry. Endodontic education can significantly benefit from AI chatbots; however, knowledge gaps regarding their potential and limitations hinder their effective utilization. This narrative review aims to: (A) explain the core functionalities of AI chatbots, including their reliance on natural language processing (NLP), machine learning (ML), and deep learning (DL); (B) explore their applications in endodontic education for personalized learning, interactive training, and clinical decision support; (C) discuss the challenges posed by technical limitations, ethical considerations, and the potential for misinformation. The review highlights that AI chatbots provide learners with immediate access to knowledge, personalized educational experiences, and tools for developing clinical reasoning through case-based learning. Educators benefit from streamlined curriculum development, automated assessment creation, and evidence-based resource integration. Despite these advantages, concerns such as chatbot hallucinations, algorithmic biases, potential for plagiarism, and the spread of misinformation require careful consideration. Analysis of current research reveals limited endodontic-specific studies, emphasizing the need for tailored chatbot solutions validated for accuracy and relevance. Successful integration will require collaborative efforts among educators, developers, and professional organizations to address challenges, ensure ethical use, and establish evaluation frameworks.

A scoping review of the integration of artificial intelligence in primary and secondary schools from 2020 to 2024: Policy implications for South Africa

South Africa's education system struggles with challenges highlighted by low student performance in global assessments. Based on Bloom's (1984) personalised instruction model and the Arksey and O'Malley (2005) methodology, this scoping review explores how artificial intelligence (AI) can enhance educational outcomes in South Africa. This analysis of international applications and their implications for South Africa involved a scoping review of SpringerLink, ScienceDirect, Mendeley, and Taylor & Francis databases to identify primary studies on AI in education from 2020 to 2024. Data from 33 articles were charted and summarised according to the research questions. Key findings highlight the potential of AI to provide personalised learning experiences, enhance inclusivity, and support special needs education. However, infrastructure deficiencies pose significant barriers. Effective AI deployment requires educators to develop technical skills and understand AI ethics to address biases and privacy issues. Implications for policy are discussed.

Opportunities and Challenges of Big Data Empowering Clinical Medicine Graduate Education from the Perspective of Ideological and Political Guidance

This study explores the theme of “Big Data Empowering Clinical Medicine Graduate Education from the Perspective of Ideological and Political Guidance.” It examines how big data technology can enhance the quality of graduate education in clinical medicine. By analyzing the integration of ideological and political guidance theory with big data technology, this paper aims to uncover the potential opportunities and challenges of big data in improving clinical medicine graduate education. The research employs literature review and case study methods to delve into the potential applications of big data technology in curriculum design, teaching quality monitoring, student evaluation, and personalized learning support. The results indicate that big data technology provides new tools and methods for clinical medicine graduate education, contributing to the personalization, precision, and efficiency of education. However, the application of big data technology also faces challenges such as data privacy protection and transparency in technology application. This study offers theoretical support and practical guidance for the introduction of big data technology in the field of clinical medicine graduate education, holding significant implications for promoting the innovation and development of educational technology.

AI Integration Blueprint

The document outlines a comprehensive strategy for integrating AI into higher education, emphasizing the need for curriculum restructuring, pedagogical transformation, and strategic implementation to prepare students for an AI-augmented future. AI as an Educational Opportunity: Higher education institutions should reframe AI use from a discipline issue to an educational opportunity, as detection and prohibition approaches are ineffective and worsen equity gaps. Emphasis on Meta-AI Skills: Learning outcomes should be revised to emphasize meta-AI skills like prompt engineering, output evaluation, and AI collaboration, which require higher cognitive engagement than traditional skills. Curriculum Restructuring: Universities need to update general education requirements and program sequences to reflect AI-transformed professional practices, ensuring students develop both domain knowledge and AI collaboration skills. Permanent and Temporary Scaffolding: Pedagogy should distinguish between temporary scaffolding for developing basic meta-AI skills and permanent scaffolding where AI tools are used professionally, focusing on sophisticated collaboration patterns. Personalized Learning Support: RAG-based technology can provide personalized learning support through custom AI tutoring systems that offer targeted assistance while maintaining accuracy through controlled knowledge bases. Enhanced Student Services: AI integration can enhance student services by streamlining administrative processes and providing proactive, accessible support across departments, benefiting underserved students. Operational Efficiency: AI can improve operational efficiency in higher education by enhancing enrollment management, academic administration, and research support workflows. Strategic Implementation: Successful AI integration requires decisive leadership, strategic resource allocation, and active management of organizational resistance, with a focus on high-impact areas.

Bridging Educational Achievement Gaps with Generative AI: Personalized Curriculum for Targeted Learning Support

The inequitable distribution of educational resources plays a major role in widening achievement gaps, placing students from lower socioeconomic status (SES) backgrounds at a disadvantage due to systemic barriers that restrict access to these resources. Recent advancements in the development of artificial intelligence (AI), namely ChatGPT by OpenAI, showcased its ability to generate text responses in natural language format based on input prompts. The accessibility and convenience of ChatGPT hold promise for offering personalized learning support. In pursuit of this goal, the authors built Ligare an AI-powered curriculum builder that integrates, optimizes, and presents generated responses with a user-friendly interface. The design process followed rigorous Human-Computer Interaction (HCI) protocols, including pre-development analysis, low- and high-fidelity prototyping, and subsequent evaluation. Although Ligare currently supports only math learning, the evaluation results demonstrate its potential for broader application, highlighting future directions for providing more accessible personalized education and addressing achievement gaps.

Vocabulary Instruction for Chinese Middle School Students Empowered by GPT

The purpose of this paper is to explore how GPT (Generative Pretraining Transformer) technology can play a role in teaching English vocabulary to Chinese secondary school students. First, with the differences in students' vocabulary mastery levels, it is often difficult for traditional teaching methods to provide personalized learning support for each student, while GPT can provide customized, interactive and scalable vocabulary learning solutions. Second, this paper provides an in-depth analysis of the specific applications of GPT in vocabulary teaching, including how students can interact with GPT to understand the meaning of words, practice in sentences, and participate in simulated conversations. In addition, GPT is able to provide follow-up learning suggestions based on students' progress, helping students to make continuous progress in the process of vocabulary learning. Finally, the paper highlights the potential impact of GPT on vocabulary instruction, particularly in terms of improving students' long-term memory of vocabulary learning and enhancing flexibility and accessibility of learning. By combining with traditional teaching methods, GPT provides teachers with a richer set of pedagogical tools to promote a more personalized and interactive education and to meet the diverse needs of secondary school students in vocabulary acquisition. Overall, GPT technology can effectively improve Chinese secondary school students' English vocabulary acquisition and memorization and promote the transformation of vocabulary teaching mode.

Bibliometric Analysis of Natural Language Processing Technology in Education: Hot Topics, Frontier Evolution, and Future Prospects

The application of natural language processing (NLP) technology in the field of education has attracted considerable attention. This study takes 716 articles from the Web of Science database from 1998 to 2023 as its research sample. Using bibliometrics as the theoretical foundation, and employing methods such as literature review and knowledge mapping analysis, the study utilizes tools like CiteSpace to generate relevant visualizations, analyzing key research themes, frontier developments, and providing future prospects in this domain. The main findings of the study are as follows: First, the number of publications in this field has been increasing annually, forming core publishing journals such as Education and Information Technology, core research teams led by figures like Cucchiarini Catia and Meurers Detmar, and core publishing countries including the United States and China. Second, the field primarily covers five major themes: the educational application of technical tools, the analysis and development of educational content, the application of computational linguistics in education, language acquisition and language learning, and educational assessment and analysis methods. Third, the research in this field exhibits certain developmental phases, progressing through the stages of emergence, exploration, and development. Based on these findings, the following future prospects are proposed: at the theoretical level, deeper application of personalized learning paths, emotional monitoring and learning support, and intelligent generation and optimization of educational content; at the practical level, interdisciplinary collaboration and innovation, educational data mining and analysis, and global perspectives with international cooperation.

Using AI Chatbots in Visual Programming: Effect on Programming Self-Efficacy of Upper Primary School Learners

Programming Self-Efficacy (PSE) is crucial for enhancing learners’ skills, cognitive abilities, and career interests. Despite its significance, existing research often overlooks strategies to boost PSE among upper primary school learners. This study evaluates the impact of an innovative e-learning tool, AI Chatbots, on students’ PSE. Conducted in a primary school in northern mainland China, the experiment integrated AI Chatbots as an intervention within a visual programming curriculum. It involved 98 fifth-grade students divided into experimental and control groups, both instructed by the same teacher. Interviews were conducted with 6 selected participants from the experimental group after completing all intervention procedures. Findings suggest that, compared to traditional teacher-led instruction, AI Chatbots significantly enhance students’ PSE in programming skill, while boost in PSE in programming knowledge remains non-significant. The study also investigates the mechanisms through which AI Chatbots provide students with easily accessible, personalized learning support for self-directed learning and reduce emotional barriers when they seek help.

Learning Strategies and Effects of Distance Education in China

This study investigated the relationship between learn-ing strategies and learning outcomes among distance education learners, aiming to provide insights and rec-ommendations for enhancing teaching quality and im-proving learners’ academic performance. Adopting a descriptive research design, the study collected data through questionnaires and semi-structured interviews. The questionnaire utilized a validated learning strategy and learning outcome scale, while the interviews delved into learners’ experiences and perceptions. The research focused on registered learners from a distance education platform. A total of 309 participants were se-lected using a stratified random sampling method, and the data were analyzed using SPSS-28 software. The learning outcomes were notably lower compared to traditional education methods. Learners often exhibit weak subjective cognition, low interest in learning, limited autonomous learning ability, inadequate coop-erative communication skills, and poor will power. Based on these findings, the study offers the following optimization suggestions: Educational institutions may provide learning strategies to help learners effectively master and apply them. Distance education platforms may enhance functional designs by incorporating tools to support learning strategies, such as time manage-ment features, learning resource recommendations, and online collaboration functionalities. Personalized learning support and guidance tailored to learners’ backgrounds and needs to improve overall learning outcomes.

The Construction of a Blended English Teaching Model for Vocational Education Based on Mobile-Assisted Language Learning (MALL)

With the rapid development of mobile internet technology, mobile learning has emerged as a mainstream learning method for the new generation of students. Based on the “Fishpond Theory,” this paper proposes a blended learning model for vocational English education, utilizing the WeChat platform on smartphones. The objective is to integrate the benefits of traditional classroom teaching with mobile learning to enhance students’ autonomous learning abilities and improve their English learning outcomes. This paper reviews relevant theories and literature, explaining the basic concepts of Fishpond Theory, Mobile-Assisted Language Learning, and blended learning. It further designs a three-stage teaching model construction path, encompassing front-end analysis, activity and resource design, and teaching model evaluation, tailored to the characteristics of vocational English education. Through the WeChat platform’s resource distribution, personalized learning support, and online-offline interactive feedback, teachers can effectively guide students toward autonomous and in-depth learning. The findings indicate that this model significantly increases student motivation and engagement. However, in practice, it is crucial to strengthen the cultivation of students’ learning habits and the guidance and supervision provided by teachers.

Leveraging AI Technologies for Personalized Learning Support in Dyslexic Students

Dyslexia, a prevalent learning difficulty affecting reading, writing, and spelling, requires specialized interventionsthat traditional educational systems often lack. This paper proposes an AI-assisted learning platform aimed at addressing the diverse needs of dyslexic students. The system first tests for dyslexia, classifying students into three zones—low, moderate, and high— based on severity. Using Natural language processing (NLP), Machine learning, speech-to-text (STT), and Text-to-speech (TTS) these are some of the AI technologies, the platform provides personalized learning practices tailored to each student's classification. Continuous assessment and real- time monitoring allow for dynamic adaptation of the learning process to accommodate individual progress, ensuring that interventions are both responsive and effective.

Design of Intelligent Classroom System with Multi-Dimensional Detection using WSN

This paper explores the design of an intelligent classroom system utilizing Wireless Sensor Network (WSN) technology, aiming to enhance teaching efficiency and student learning experience through multi-dimensional detection (environment, behavior analysis, and communication). By integrating Internet of Things (IoT), big data, cloud computing, and other advanced technologies, the system realizes real-time monitoring of classroom environments, precise analysis of student learning behaviors, and efficient teacher-student interaction. The research results indicate that this system can significantly improve classroom environmental quality, provide personalized learning support, and promote effective communication between teachers and students.

Navigating The Digital Shift: A Bibliometric Analysis of Chatbot Applications in Education

In the ever-changing scenario of a technology-driven learning atmosphere, chatbots occupy a considerable role, taking up tasks in personalised learning and administrative support. This study uses a widespread bibliometric analysis of chatbots for educational purposes, engaging essential bibliographic data from Scopus and using the PRISMA flowchart approach for painstaking screening, exclusion and inclusion of studies in this field. Bibliometric tools like CiteSpace, VOSviewer, and Biblioshiny are utilised for this study to provide a multi-dimensional outlook on the effects and development of this field. The major findings of this study include an analysis of yearly scientific production about educational chatbots, identifying the most productive authors and synthesis of the most relevant sources and major cited documents globally. By analysing the influence of co-citation of cited authors and timezone views of cited journals, this study goes deep into the particulars of academic collaborations. This study analyzes the co-occurrence of keywords and those with the highest citation bursts to comprehend the intellectual structure of the topic’s research field, giving an outlook into the focus areas and evolving trends. This study also undertakes to find practical implications and relevant research gaps. These insights identify the features of chatbot research and suggest future directions in practical application and scholarly inquiry. For effective incorporation and evolution of chatbots in the educational scenario, academicians, technologists, and educators can use this bibliometric analysis as an essential resource.

A New Path to Language Education Innovation Based on Networked Smart Language Laboratories

This paper discusses the importance of networked intelligent language laboratory as a new path for language education innovation. It elaborates on its background, technical support (Internet of Things, big data, cloud computing, artificial intelligence technology), advantages (resource sharing and utilization efficiency improvement, personalized learning support, enhanced teaching interaction and feedback, environmental perception and intelligent management), innovative applications (diversified teaching models, intelligent learning assessment, integration of language practice and cultural experience), analyzes the challenges in construction (technical security and privacy protection, teacher role transformation and capacity improvement, education resource integration and quality assurance), and summarizes the research results and looks forward to future development trends, including continuous technological innovation, expanding and deepening applications, and strengthening globalization and cross-cultural communication and cooperation.

Precarious Partnership: Student Perceptions of Generative AI in Post-Secondary Learning

Generative AI (GenAI) technologies offer both potent possibilities and significant risks for learning. As such, post-secondary learners require critical competencies for strategically regulating learning with AI. While student beliefs about GenAI impact their interactions and decisions, research of students’ perspectives is emergent. This study aimed to examine post-secondary students’ perceptions about the effective use of GenAI for learning. Participants were 125 undergraduate students at a university in Canada. Results indicated students were confident in their ability to use GenAI for learning, perceived GenAI to be effective for learning, and reported using GenAI in a wide range of academic tasks, particularly when faced with cognition and time management challenges. Finally, access to information, personalized learning support, new learning approaches, and time were considered benefits of GenAI. However, students expressed concerns about academic integrity, information accuracy, and the impact on personal learning. Implications for scaffolding learners’ development of skills for self-regulating interactions with AI critical for post-secondary and beyond are discussed.

The Role of Artificial Intelligence in Higher Education

The integration of Artificial Intelligence (AI) in higher education has sparked a transformative shift in pedagogical methodologies, student engagement, and academic integrity across the UK and Ireland (Dogru, et.al, 2023). This qualitative study delves into the multifaceted implications of utilising AI in academic assignments from the perspective of higher education students, drawing on semi-structured interviews carried out with higher education students. The findings reveal a variety of perceived benefits, including enhanced study efficiency, personalised learning support (Hanaba et al., 2020; Yang, 2021), and the potential of AI to level the academic playing field for students with diverse needs. However, these advantages are in contrast with students’ perceptions of a wide range of significant concerns and challenges, notably the ethical dilemmas surrounding academic honesty, the potential for student dependency on AI leading to diminished knowledge and skill development, as well as the issue of equity in relation to students’ access to AI resources. Moreover, the study stresses a critical gap in institutional guidance regarding AI use, with students voicing a need for clear, consistent guidance from universities. This paper highlights the complex landscape of AI in academia, advocating for a balanced approach that harnesses AI's potential while addressing ethical, educational, and equity challenges. The study emphasises a need for immediate and more in-depth research into the use of AI in higher education from the perspective of both students and university staff in Ireland/UK.