Pub Date : 2025-03-05DOI: 10.1109/TLT.2025.3547321
Bei Cai;Ziyu He;Hong Fu;Yang Zheng;Yanjie Song
Much research has applied automated writing evaluation (AWE) systems to English writing instruction; however, understanding how students internalize and apply this feedback to reduce writing errors is difficult, largely due to the personal and private nature of this process. Therefore, this research utilized eye-tracking technology to explore the AWE system's effectiveness in reducing the writing errors of English major students. A total of 118 higher vocational college students majoring in English in China participated in this eight-week study. The experimental group studied with and received feedback from both the AWE system (Pigai) and the teacher, whereas the control group studied without the AWE system and only received teacher feedback. Eye-tracking experiments were conducted before and after the writing instruction. Participants’ responses during the eye-tracking experiment, first-person eye movement video data, and corresponding gaze data were collected. Leveraging the application of neural network technology in optical character recognition (OCR), combined with data from an eye-tracking device, we developed a system that can transform first-person eye movement video data and gaze data into heatmaps and eye-tracking indices conducive to analysis. Various data analysis methods were employed, including neural network algorithms, heatmap analysis, Mann–Whitney U test, independent-samples t-test, and Welch's t-test. The results for the post-eye-tracking experiment responses, heatmaps, and eye-tracking indices indicate the advantages of using the AWE system, which effectively enhances students’ ability to recognize writing errors while reducing processing time by facilitating the internalization of writing errors through continuous feedback on such errors, and enabling them to apply this knowledge to new materials, thereby recognizing writing errors more quickly and accurately, and thus helping them to reduce writing errors. The pedagogical implications are fully discussed.
{"title":"Reducing English Major Students’ Writing Errors With an Automated Writing Evaluation System: Evidence From Eye-Tracking Technology","authors":"Bei Cai;Ziyu He;Hong Fu;Yang Zheng;Yanjie Song","doi":"10.1109/TLT.2025.3547321","DOIUrl":"https://doi.org/10.1109/TLT.2025.3547321","url":null,"abstract":"Much research has applied automated writing evaluation (AWE) systems to English writing instruction; however, understanding how students internalize and apply this feedback to reduce writing errors is difficult, largely due to the personal and private nature of this process. Therefore, this research utilized eye-tracking technology to explore the AWE system's effectiveness in reducing the writing errors of English major students. A total of 118 higher vocational college students majoring in English in China participated in this eight-week study. The experimental group studied with and received feedback from both the AWE system (Pigai) and the teacher, whereas the control group studied without the AWE system and only received teacher feedback. Eye-tracking experiments were conducted before and after the writing instruction. Participants’ responses during the eye-tracking experiment, first-person eye movement video data, and corresponding gaze data were collected. Leveraging the application of neural network technology in optical character recognition (OCR), combined with data from an eye-tracking device, we developed a system that can transform first-person eye movement video data and gaze data into heatmaps and eye-tracking indices conducive to analysis. Various data analysis methods were employed, including neural network algorithms, heatmap analysis, Mann–Whitney U test, independent-samples <italic>t</i>-test, and Welch's <italic>t</i>-test. The results for the post-eye-tracking experiment responses, heatmaps, and eye-tracking indices indicate the advantages of using the AWE system, which effectively enhances students’ ability to recognize writing errors while reducing processing time by facilitating the internalization of writing errors through continuous feedback on such errors, and enabling them to apply this knowledge to new materials, thereby recognizing writing errors more quickly and accurately, and thus helping them to reduce writing errors. The pedagogical implications are fully discussed.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"304-320"},"PeriodicalIF":2.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.1109/TLT.2025.3545084
Sirinda Palahan
The rise of online programming education has necessitated more effective personalized interactions, a gap that PythonPal aims to fill through its innovative learning system integrated with a chatbot. This research delves into PythonPal's potential to enhance the online learning experience, especially in contexts with high student-to-teacher ratios where there is a need for personalized feedback. PythonPal's design, featuring modules for conversation, tutorials, and exercises, was evaluated through student interactions and feedback. Key findings reveal PythonPal's proficiency in syntax error recognition and user query comprehension, with its intent classification model showing high accuracy. The system's performance in error feedback, though varied, demonstrates both strengths and areas for enhancement. Student feedback indicated satisfactory query understanding and feedback accuracy but also pointed out the need for faster responses and improved interaction quality. PythonPal's deployment promises to significantly enhance online programming education by providing immediate personalized feedback and interactive learning experiences, fostering a deeper understanding of programming concepts among students. These benefits mark a step forward in addressing the challenges of distance learning, making programming education more accessible and effective.
{"title":"PythonPal: Enhancing Online Programming Education Through Chatbot-Driven Personalized Feedback","authors":"Sirinda Palahan","doi":"10.1109/TLT.2025.3545084","DOIUrl":"https://doi.org/10.1109/TLT.2025.3545084","url":null,"abstract":"The rise of online programming education has necessitated more effective personalized interactions, a gap that PythonPal aims to fill through its innovative learning system integrated with a chatbot. This research delves into PythonPal's potential to enhance the online learning experience, especially in contexts with high student-to-teacher ratios where there is a need for personalized feedback. PythonPal's design, featuring modules for conversation, tutorials, and exercises, was evaluated through student interactions and feedback. Key findings reveal PythonPal's proficiency in syntax error recognition and user query comprehension, with its intent classification model showing high accuracy. The system's performance in error feedback, though varied, demonstrates both strengths and areas for enhancement. Student feedback indicated satisfactory query understanding and feedback accuracy but also pointed out the need for faster responses and improved interaction quality. PythonPal's deployment promises to significantly enhance online programming education by providing immediate personalized feedback and interactive learning experiences, fostering a deeper understanding of programming concepts among students. These benefits mark a step forward in addressing the challenges of distance learning, making programming education more accessible and effective.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"335-350"},"PeriodicalIF":2.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-25DOI: 10.1109/TLT.2025.3545641
Nikola M. Luburić;Luka Ž. Dorić;Jelena J. Slivka;Dragan Lj. Vidaković;Katarina-Glorija G. Grujić;Aleksandar D. Kovačević;Simona B. Prokić
Software engineers are tasked with writing functionally correct code of high quality. Maintainability is a crucial code quality attribute that determines the ease of analyzing, modifying, reusing, and testing a software component. This quality attribute significantly affects the software's lifetime cost, contributing to developer productivity and other quality attributes. Consequently, academia and industry emphasize the need to train software engineers to build maintainable software code. Unfortunately, code maintainability is an ill-defined domain and is challenging to teach and learn. This problem is aggravated by a rising number of software engineering students and a lack of capable instructors. Existing instructors rely on scalable one-size-fits-all teaching methods that are ineffective. Advances in e-learning technologies can alleviate these issues. Our primary contribution is the design of a novel assessment item type, the maintainability challenge. It integrates into the standard intelligent tutoring system (ITS) architecture to develop skills for analyzing and refactoring high-level code maintainability issues. Our secondary contributions include the code maintainability knowledge component model and the implementation of an ITS that supports the maintainability challenge for the C# programming language. We designed, developed, and evaluated the ITS over two years of working with undergraduate students using a mixed-method approach anchored in design science. The empirical evaluations culminated with a field study with 59 undergraduate students. We report on the evaluation results that showcase the utility of our contributions. Our contributions support software engineering instructors in developing the code maintainability skills of their students at scale.
{"title":"An Intelligent Tutoring System to Support Code Maintainability Skill Development","authors":"Nikola M. Luburić;Luka Ž. Dorić;Jelena J. Slivka;Dragan Lj. Vidaković;Katarina-Glorija G. Grujić;Aleksandar D. Kovačević;Simona B. Prokić","doi":"10.1109/TLT.2025.3545641","DOIUrl":"https://doi.org/10.1109/TLT.2025.3545641","url":null,"abstract":"Software engineers are tasked with writing functionally correct code of high quality. Maintainability is a crucial code quality attribute that determines the ease of analyzing, modifying, reusing, and testing a software component. This quality attribute significantly affects the software's lifetime cost, contributing to developer productivity and other quality attributes. Consequently, academia and industry emphasize the need to train software engineers to build maintainable software code. Unfortunately, code maintainability is an ill-defined domain and is challenging to teach and learn. This problem is aggravated by a rising number of software engineering students and a lack of capable instructors. Existing instructors rely on scalable one-size-fits-all teaching methods that are ineffective. Advances in e-learning technologies can alleviate these issues. Our primary contribution is the design of a novel assessment item type, the maintainability challenge. It integrates into the standard intelligent tutoring system (ITS) architecture to develop skills for analyzing and refactoring high-level code maintainability issues. Our secondary contributions include the code maintainability knowledge component model and the implementation of an ITS that supports the maintainability challenge for the C# programming language. We designed, developed, and evaluated the ITS over two years of working with undergraduate students using a mixed-method approach anchored in design science. The empirical evaluations culminated with a field study with 59 undergraduate students. We report on the evaluation results that showcase the utility of our contributions. Our contributions support software engineering instructors in developing the code maintainability skills of their students at scale.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"289-303"},"PeriodicalIF":2.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1109/TLT.2025.3545436
Lorenzo Valente;Federico De Lorenzis;Davide Calandra;Fabrizio Lamberti
In recent years, first responders have faced increasing challenges in their operations, highlighting a growing need for specialized and comprehensive training. In particular, the firefighting incident commanders (ICs) are playing a pivotal role, providing directions to field operators and making critical decisions in emergency situations. Over time, traditional training tools in this field have evolved, reaching their pinnacle with augmented sand tables (ASTs). ASTs build on spatial augmented reality (SAR), a form of extended reality (XR) that utilizes projections. Although ASTs enable large-scale visualization of the morphological features of the terrain, by relying solely on SAR, it is not possible to fully leverage the potential of XR, which is increasingly recognized as a powerful tool for training. This work introduces a novel approach to training ICs by integrating ASTs with XR, incorporating a learning-by-doing methodology alongside an objective measurement of trainees' performance. To this end, an XR training system (XRTS) has been developed, combining the capabilities of an AST with personal mixed reality devices and integrating a physically accurate interactive fire simulator. This system was deployed within a forest firefighting IC training course. All the system components were designed based on the theoretical foundations of decision making to effectively develop the necessary skills. The proposed approach was compared with traditional AST-based training methods for these roles, focusing on the analysis of learning outcomes, user experience, usability, and cognitive load. The study demonstrated several advantages associated with the use of the XRTS, including improvements in training effectiveness and a notable reduction in overall cognitive load.
{"title":"Enhancing Sand-Table-Based Incident Command Training With Extended Reality and Interactive Simulations: A Use Case in Forest Firefighting","authors":"Lorenzo Valente;Federico De Lorenzis;Davide Calandra;Fabrizio Lamberti","doi":"10.1109/TLT.2025.3545436","DOIUrl":"https://doi.org/10.1109/TLT.2025.3545436","url":null,"abstract":"In recent years, first responders have faced increasing challenges in their operations, highlighting a growing need for specialized and comprehensive training. In particular, the firefighting incident commanders (ICs) are playing a pivotal role, providing directions to field operators and making critical decisions in emergency situations. Over time, traditional training tools in this field have evolved, reaching their pinnacle with augmented sand tables (ASTs). ASTs build on spatial augmented reality (SAR), a form of extended reality (XR) that utilizes projections. Although ASTs enable large-scale visualization of the morphological features of the terrain, by relying solely on SAR, it is not possible to fully leverage the potential of XR, which is increasingly recognized as a powerful tool for training. This work introduces a novel approach to training ICs by integrating ASTs with XR, incorporating a learning-by-doing methodology alongside an objective measurement of trainees' performance. To this end, an XR training system (XRTS) has been developed, combining the capabilities of an AST with personal mixed reality devices and integrating a physically accurate interactive fire simulator. This system was deployed within a forest firefighting IC training course. All the system components were designed based on the theoretical foundations of decision making to effectively develop the necessary skills. The proposed approach was compared with traditional AST-based training methods for these roles, focusing on the analysis of learning outcomes, user experience, usability, and cognitive load. The study demonstrated several advantages associated with the use of the XRTS, including improvements in training effectiveness and a notable reduction in overall cognitive load.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"273-288"},"PeriodicalIF":2.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-20DOI: 10.1109/TLT.2025.3544454
Sheng Bi;Zeyi Miao;Qizhi Min
The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder–decoder architectures have advanced traditional KGQG approaches. However, these approaches encounter challenges in achieving question diversity and grammatical accuracy. They often suffer from a disconnect between the phrasing of the question and the type of the answer entity, a phenomenon known as semantic drift. To address these challenges, we introduce LEMON, a knowledge-enhanced, type-constrained, and grammar-guided model for KGQG. LEMON enhances the input by integrating entity-related knowledge using heuristic rules, which fosters diversity in question generation. It employs a hierarchical global relation embedding with translation loss to align questions with entity types. In addition, it utilizes a graph-based module to aggregate type information from neighboring nodes. The LEMON model incorporates a type-constrained decoder to generate diverse expressions and improves grammatical accuracy through a syntactic and semantic reward function via reinforcement learning. Evaluations on benchmark datasets demonstrate LEMON's strong competitiveness. The study also examines the impact of question generation quality on question-answering systems, providing guidance for future research endeavors in this domain.
{"title":"LEMON: A Knowledge-Enhanced, Type-Constrained, and Grammar-Guided Model for Question Generation Over Knowledge Graphs","authors":"Sheng Bi;Zeyi Miao;Qizhi Min","doi":"10.1109/TLT.2025.3544454","DOIUrl":"https://doi.org/10.1109/TLT.2025.3544454","url":null,"abstract":"The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder–decoder architectures have advanced traditional KGQG approaches. However, these approaches encounter challenges in achieving question diversity and grammatical accuracy. They often suffer from a disconnect between the phrasing of the question and the type of the answer entity, a phenomenon known as semantic drift. To address these challenges, we introduce LEMON, a knowledge-enhanced, type-constrained, and grammar-guided model for KGQG. LEMON enhances the input by integrating entity-related knowledge using heuristic rules, which fosters diversity in question generation. It employs a hierarchical global relation embedding with translation loss to align questions with entity types. In addition, it utilizes a graph-based module to aggregate type information from neighboring nodes. The LEMON model incorporates a type-constrained decoder to generate diverse expressions and improves grammatical accuracy through a syntactic and semantic reward function via reinforcement learning. Evaluations on benchmark datasets demonstrate LEMON's strong competitiveness. The study also examines the impact of question generation quality on question-answering systems, providing guidance for future research endeavors in this domain.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"256-272"},"PeriodicalIF":2.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid advancement of generative artificial intelligence (GAI), its application in educational settings has increasingly become a focal point, particularly in enhancing students’ analytical capabilities. This study examines the effectiveness of the ChatGPT prompt framework in improving text analysis skills among students, specifically targeting readability, accuracy, completeness, logicality, and critical thinking. Conducted among high school students in Canada, the research assesses how GAI prompt frameworks significantly affect the quality of students’ analytical responses. Results showed significant improvements in all five aspects of readability, accuracy, completeness, logicality, and critical thinking, especially for students with no prior knowledge of the topic. However, enhancements in completeness and critical thinking were less pronounced, suggesting that while the ChatGPT framework substantially supports basic analytical skills, its effectiveness varies depending on the complexity of cognitive tasks and the extent of students’ existing knowledge. The study underscores the significant role that advanced GAI tools can play in modern educational environments, promoting deeper engagement with learning materials and enhancing students’ analytical abilities. It highlights the necessity of integrating these technologies to cater to diverse learning needs and cognitive challenges.
{"title":"Navigating the Textual Maze: Enhancing Textual Analytical Skills Through an Innovative GAI Prompt Framework","authors":"Xuefan Li;Tingsong Li;Minjuan Wang;Sining Tao;Xiaoxu Zhou;Xiaoqing Wei;Naiqing Guan","doi":"10.1109/TLT.2025.3539104","DOIUrl":"https://doi.org/10.1109/TLT.2025.3539104","url":null,"abstract":"With the rapid advancement of generative artificial intelligence (GAI), its application in educational settings has increasingly become a focal point, particularly in enhancing students’ analytical capabilities. This study examines the effectiveness of the ChatGPT prompt framework in improving text analysis skills among students, specifically targeting readability, accuracy, completeness, logicality, and critical thinking. Conducted among high school students in Canada, the research assesses how GAI prompt frameworks significantly affect the quality of students’ analytical responses. Results showed significant improvements in all five aspects of readability, accuracy, completeness, logicality, and critical thinking, especially for students with no prior knowledge of the topic. However, enhancements in completeness and critical thinking were less pronounced, suggesting that while the ChatGPT framework substantially supports basic analytical skills, its effectiveness varies depending on the complexity of cognitive tasks and the extent of students’ existing knowledge. The study underscores the significant role that advanced GAI tools can play in modern educational environments, promoting deeper engagement with learning materials and enhancing students’ analytical abilities. It highlights the necessity of integrating these technologies to cater to diverse learning needs and cognitive challenges.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"206-215"},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-05DOI: 10.1109/TLT.2025.3539179
Kaitlyn Tracy;Ourania Spantidi
Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as generative pretrained transformer can further enhance these experiences by reducing cognitive load and improving learning outcomes. This study examines the impact of an AI-driven instructor assistant in VR classrooms on student engagement, cognitive load, knowledge retention, and performance. A total of 52 participants were divided into two groups experiencing a VR lesson on the bubble sort algorithm, one with only a prescripted virtual instructor (control group), and the other with the addition of an AI instructor assistant (experimental group). Statistical analysis of postlesson quizzes and cognitive load assessments was conducted using independent t-tests and analysis of variance (ANOVA), with the cognitive load being measured through a postexperiment questionnaire. The study results indicate that the experimental group reported significantly higher engagement compared to the control group. While the AI assistant did not significantly improve postlesson assessment scores, it enhanced conceptual knowledge transfer. The experimental group also demonstrated lower intrinsic cognitive load, suggesting the assistant reduced the perceived complexity of the material. Higher germane and general cognitive loads indicated that students were more invested in meaningful learning without feeling overwhelmed.
{"title":"Impact of GPT-Driven Teaching Assistants in VR Learning Environments","authors":"Kaitlyn Tracy;Ourania Spantidi","doi":"10.1109/TLT.2025.3539179","DOIUrl":"https://doi.org/10.1109/TLT.2025.3539179","url":null,"abstract":"Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as generative pretrained transformer can further enhance these experiences by reducing cognitive load and improving learning outcomes. This study examines the impact of an AI-driven instructor assistant in VR classrooms on student engagement, cognitive load, knowledge retention, and performance. A total of 52 participants were divided into two groups experiencing a VR lesson on the bubble sort algorithm, one with only a prescripted virtual instructor (control group), and the other with the addition of an AI instructor assistant (experimental group). Statistical analysis of postlesson quizzes and cognitive load assessments was conducted using independent t-tests and analysis of variance (ANOVA), with the cognitive load being measured through a postexperiment questionnaire. The study results indicate that the experimental group reported significantly higher engagement compared to the control group. While the AI assistant did not significantly improve postlesson assessment scores, it enhanced conceptual knowledge transfer. The experimental group also demonstrated lower intrinsic cognitive load, suggesting the assistant reduced the perceived complexity of the material. Higher germane and general cognitive loads indicated that students were more invested in meaningful learning without feeling overwhelmed.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"192-205"},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1109/TLT.2025.3537618
Qi Lang;Minjuan Wang;Minghao Yin;Shuang Liang;Wenzhuo Song
Generative artificial intelligence (GAI) has demonstrated remarkable potential in both educational practice and research, particularly in areas, such as personalized learning, adaptive assessment, innovative teaching methods, and cross-cultural communication. However, it faces several significant challenges, including the comprehension of complex domain knowledge, technological accessibility, and the delineation of AI's role in education. Addressing these challenges necessitates collaborative efforts from educators and researchers. This article summarizes the state-of-the-art large language models (LLMs) developed by various technology companies, exploring their diverse applications and unique contributions to primary, higher, and vocational education. Furthermore, it reviews recent research from the past three years, focusing on the challenges and solutions associated with GAI in educational practice and research. The aim of the review is to provide novel insights for enhancing human–computer interaction in educational settings through the utilization of GAI. Statistical analysis reveals that the current application of LLMs in the education sector is predominantly centered on the ChatGPT series. A key focus for future research lies in effectively integrating a broader range of LLMs into educational tasks, with particular emphasis on the interaction between multimodal LLMs and educational scenarios.
{"title":"Transforming Education With Generative AI (GAI): Key Insights and Future Prospects","authors":"Qi Lang;Minjuan Wang;Minghao Yin;Shuang Liang;Wenzhuo Song","doi":"10.1109/TLT.2025.3537618","DOIUrl":"https://doi.org/10.1109/TLT.2025.3537618","url":null,"abstract":"Generative artificial intelligence (GAI) has demonstrated remarkable potential in both educational practice and research, particularly in areas, such as personalized learning, adaptive assessment, innovative teaching methods, and cross-cultural communication. However, it faces several significant challenges, including the comprehension of complex domain knowledge, technological accessibility, and the delineation of AI's role in education. Addressing these challenges necessitates collaborative efforts from educators and researchers. This article summarizes the state-of-the-art large language models (LLMs) developed by various technology companies, exploring their diverse applications and unique contributions to primary, higher, and vocational education. Furthermore, it reviews recent research from the past three years, focusing on the challenges and solutions associated with GAI in educational practice and research. The aim of the review is to provide novel insights for enhancing human–computer interaction in educational settings through the utilization of GAI. Statistical analysis reveals that the current application of LLMs in the education sector is predominantly centered on the ChatGPT series. A key focus for future research lies in effectively integrating a broader range of LLMs into educational tasks, with particular emphasis on the interaction between multimodal LLMs and educational scenarios.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"230-242"},"PeriodicalIF":2.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1109/TLT.2025.3537802
Ahmad Chaddad;Yuchen Jiang
The concept of the Metaverse, viewed as the ultimate manifestation of the Internet, has gained significant attention due to rapid advances in technologies such as the Internet of Things (IoT) and blockchain. Acting as a bridge between the physical and virtual worlds, the Metaverse has the potential to offer remarkable experiences to its users. This study presents a comprehensive survey of Metaverse techniques, including artificial intelligence, blockchain, IoT, augmented reality, virtual reality, 5G, natural language processing, and digital twins. These Metaverse techniques lead to improved health outcomes and patient care, offering innovative treatments for complex conditions, and advancing medical education. We explore the benefits of the Metaverse by examining its effectiveness in supporting various medical applications and highlight potential research challenges and future trends for the medical Metaverse and education. Although the Metaverse is currently in its early stages, more efforts are required to enable its widespread adoption in the future.
{"title":"Integrating Technologies in the Metaverse for Enhanced Healthcare and Medical Education","authors":"Ahmad Chaddad;Yuchen Jiang","doi":"10.1109/TLT.2025.3537802","DOIUrl":"https://doi.org/10.1109/TLT.2025.3537802","url":null,"abstract":"The concept of the Metaverse, viewed as the ultimate manifestation of the Internet, has gained significant attention due to rapid advances in technologies such as the Internet of Things (IoT) and blockchain. Acting as a bridge between the physical and virtual worlds, the Metaverse has the potential to offer remarkable experiences to its users. This study presents a comprehensive survey of Metaverse techniques, including artificial intelligence, blockchain, IoT, augmented reality, virtual reality, 5G, natural language processing, and digital twins. These Metaverse techniques lead to improved health outcomes and patient care, offering innovative treatments for complex conditions, and advancing medical education. We explore the benefits of the Metaverse by examining its effectiveness in supporting various medical applications and highlight potential research challenges and future trends for the medical Metaverse and education. Although the Metaverse is currently in its early stages, more efforts are required to enable its widespread adoption in the future.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"216-229"},"PeriodicalIF":2.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid changes in technology available for teaching and learning have led to a wide variety of potential tools that can be deployed to support a student's education experience. This article examines the learning interfaces for pedagogical virtual reality (VR) environments, including immersive VR (iVR). It also looks at how microlearning (ML) can be employed for instructional design at the sticking points of these interfaces. ML is an approach in which learning materials are provided in small bite-sized quantities and has been embraced as an ideal learning format for the modern learner. This study explores the research gap in ML literature regarding the ideal length of materials and modality when ML is employed for iVR. It does so through two experiments: in the first, students gave feedback on different interfaces for content and in the second, different lengths of text, video, and presentation style were tested for optimal user preference and comprehension. The findings show that preferences must be balanced against expected learning outcomes or desired level of engagement, but that fixed-point interfaces and longer texts may best be avoided. The study can be used to inform technology-enhanced learning delivery and can be used to guide policy regarding effective digital content, particularly within a VR environment.
{"title":"Microlearning in Immersive Virtual Reality: A User-Centered Analysis of Learning Interfaces","authors":"Amarpreet Gill;Derek Irwin;Linjing Sun;Dave Towey;Gege Zhang;Yanhui Zhang","doi":"10.1109/TLT.2025.3533360","DOIUrl":"https://doi.org/10.1109/TLT.2025.3533360","url":null,"abstract":"The rapid changes in technology available for teaching and learning have led to a wide variety of potential tools that can be deployed to support a student's education experience. This article examines the learning interfaces for pedagogical virtual reality (VR) environments, including immersive VR (iVR). It also looks at how microlearning (ML) can be employed for instructional design at the sticking points of these interfaces. ML is an approach in which learning materials are provided in small bite-sized quantities and has been embraced as an ideal learning format for the modern learner. This study explores the research gap in ML literature regarding the ideal length of materials and modality when ML is employed for iVR. It does so through two experiments: in the first, students gave feedback on different interfaces for content and in the second, different lengths of text, video, and presentation style were tested for optimal user preference and comprehension. The findings show that preferences must be balanced against expected learning outcomes or desired level of engagement, but that fixed-point interfaces and longer texts may best be avoided. The study can be used to inform technology-enhanced learning delivery and can be used to guide policy regarding effective digital content, particularly within a VR environment.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"165-178"},"PeriodicalIF":2.9,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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