Pub Date : 2025-05-30DOI: 10.1016/j.iheduc.2025.101022
Huiying Cai , Bing Han , Jiayue Sun , Xin Li , Lung-Hsiang Wong
This study investigates the impact of ChatGPT-supported lesson plan critiques on pre-service teachers' lesson planning skills. A quasi-experimental study involved 48 pre-service teachers from a university in Eastern China, divided into experimental (EC, n = 24) and control (CC, n = 24) condition. Each group with three participants engaged in three tasks of reviewing and revising lesson plans, guided by cognitive, metacognitive and affective questions. The EC supported by ChatGPT, while the CC did not. Epistemic network analysis indicated ChatGPT's positive impact on lesson planning skills in cognitive and affective critiques but not in metacognitive critiques. Affective critiques supported by ChatGPT benefited both low and high prior-knowledge participants, while cognitive critiques primarily benefited high prior-knowledge participants. These findings highlight the potential of design AI-supported scaffolding to enhance pre-service teachers' lesson planning skills and promote equitable learning experiences for diverse learners.
本研究调查了chatgpt支持的课程计划评论对职前教师课程计划技能的影响。本研究以华东地区某高校48名职前教师为研究对象，分为实验组（EC, n = 24）和对照组（CC, n = 24）。在认知、元认知和情感问题的指导下，每组三名参与者分别完成复习和修改教案的任务。执委会得到ChatGPT的支持，而执委会则不支持。认知网络分析表明，ChatGPT在认知和情感批评中对课程计划技能有积极影响，而在元认知批评中没有积极影响。ChatGPT支持的情感批评对低先验知识和高先验知识的参与者都有好处，而认知批评主要对高先验知识的参与者有好处。这些发现强调了设计人工智能支持的脚手架的潜力，可以提高职前教师的课程规划技能，并促进不同学习者的公平学习体验。
{"title":"Harnessing AI for teacher education to promote inclusive education: Investigating the effects of ChatGPT-supported lesson plan critiques on the development of pre-service teachers' lesson planning skills","authors":"Huiying Cai , Bing Han , Jiayue Sun , Xin Li , Lung-Hsiang Wong","doi":"10.1016/j.iheduc.2025.101022","DOIUrl":"10.1016/j.iheduc.2025.101022","url":null,"abstract":"<div><div>This study investigates the impact of ChatGPT-supported lesson plan critiques on pre-service teachers' lesson planning skills. A quasi-experimental study involved 48 pre-service teachers from a university in Eastern China, divided into experimental (EC, <em>n</em> = 24) and control (CC, n = 24) condition. Each group with three participants engaged in three tasks of reviewing and revising lesson plans, guided by cognitive, metacognitive and affective questions. The EC supported by ChatGPT, while the CC did not. Epistemic network analysis indicated ChatGPT's positive impact on lesson planning skills in cognitive and affective critiques but not in metacognitive critiques. Affective critiques supported by ChatGPT benefited both low and high prior-knowledge participants, while cognitive critiques primarily benefited high prior-knowledge participants. These findings highlight the potential of design AI-supported scaffolding to enhance pre-service teachers' lesson planning skills and promote equitable learning experiences for diverse learners.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"67 ","pages":"Article 101022"},"PeriodicalIF":6.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-30DOI: 10.1016/j.iheduc.2025.101023
Abraham E. Flanigan , Anna C. Brady , Mete Akcaoglu , Yan Dai , Sungjun Won , Bridget K. Daleiden , Kendall Hartley
College students' misuse of mobile devices during class for off-task purposes is a global issue that harms learning. While prior studies examined device misuse frequency within individual countries, no known studies have directly compared transnational differences in this behavior, leaving little known about whether crosscultural differences influence this behavior. This study investigates whether the country where students attend college (United States, South Korea, or Turkey) moderates the relationships among self-regulation of learning (SRL) tendencies, motivational factors (basic psychological needs satisfaction and utility value perceptions), and device misuse. Hierarchical moderated regression analyses revealed consistent patterns across cultures: SRL tendencies had little impact on device misuse, whereas basic needs satisfaction and utility value perceptions served as protective factors. Findings suggest that digital distraction in college classrooms transcends cultural influences that commonly lead to differences in student behavior, emphasizing the need for globally relevant strategies to reduce distractions and enhance student motivation. These results also challenge traditional assumptions that self-regulated learners are less susceptible to digital distraction. Even the more self-regulated participants in the present study regularly misused their devices during class. Such findings indicate that the performance phase of SRL that unfolds during class is riddled with disruptions and device misuse, even for the more self-regulated college students. Findings highlight the importance of fostering motivationally supportive learning environments to curb digital distraction and nourish student engagement.
{"title":"The interplay among digital distraction, self-regulation of learning tendencies, and motivational influences: A transnational investigation","authors":"Abraham E. Flanigan , Anna C. Brady , Mete Akcaoglu , Yan Dai , Sungjun Won , Bridget K. Daleiden , Kendall Hartley","doi":"10.1016/j.iheduc.2025.101023","DOIUrl":"10.1016/j.iheduc.2025.101023","url":null,"abstract":"<div><div>College students' misuse of mobile devices during class for off-task purposes is a global issue that harms learning. While prior studies examined device misuse frequency within individual countries, no known studies have directly compared transnational differences in this behavior, leaving little known about whether crosscultural differences influence this behavior. This study investigates whether the country where students attend college (United States, South Korea, or Turkey) moderates the relationships among self-regulation of learning (SRL) tendencies, motivational factors (basic psychological needs satisfaction and utility value perceptions), and device misuse. Hierarchical moderated regression analyses revealed consistent patterns across cultures: SRL tendencies had little impact on device misuse, whereas basic needs satisfaction and utility value perceptions served as protective factors. Findings suggest that digital distraction in college classrooms transcends cultural influences that commonly lead to differences in student behavior, emphasizing the need for globally relevant strategies to reduce distractions and enhance student motivation. These results also challenge traditional assumptions that self-regulated learners are less susceptible to digital distraction. Even the more self-regulated participants in the present study regularly misused their devices during class. Such findings indicate that the performance phase of SRL that unfolds during class is riddled with disruptions and device misuse, even for the more self-regulated college students. Findings highlight the importance of fostering motivationally supportive learning environments to curb digital distraction and nourish student engagement.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"67 ","pages":"Article 101023"},"PeriodicalIF":6.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-10DOI: 10.1016/j.iheduc.2025.101018
Chen-Chen Liu , Hai-Jie Wang , Xiao-Qing Gu
Although music education is considered a fundamental right for all, disparities in access remain widespread. Learners often face unequal opportunities shaped by their family backgrounds and prior experiences. This study explored the potential of AI integration in blended learning to promote inclusive and accessible music theory education. By utilizing AI-driven feedback in blended learning (AF-BL), students benefit from tailored learning experiences that promote equal opportunities for growth and reflection. A total of 43 students from a public university in China participated in a 4-week music theory course. They were divided into two groups: an experimental group (N = 22) utilizing the AF-BL method, and a control group (N = 21) following the conventional blended learning (C-BL) method. The results demonstrated that the AF-BL method significantly improved learners' music theory learning outcome and perceptions, compared to the C-BL method. Interviews with participants further highlighted the inclusivity and accessibility of the AF-BL approach, noting its ability to cater to diverse learning needs and provide equal learning opportunities for all students. The findings highlight the potential of AI in creating equitable and inclusive educational experiences, suggesting promising directions for future research and practical applications in music theory education.
{"title":"From access to mastery: Integrating AI in blended learning for equitable, inclusive, and accessible music theory educations","authors":"Chen-Chen Liu , Hai-Jie Wang , Xiao-Qing Gu","doi":"10.1016/j.iheduc.2025.101018","DOIUrl":"10.1016/j.iheduc.2025.101018","url":null,"abstract":"<div><div>Although music education is considered a fundamental right for all, disparities in access remain widespread. Learners often face unequal opportunities shaped by their family backgrounds and prior experiences. This study explored the potential of AI integration in blended learning to promote inclusive and accessible music theory education. By utilizing AI-driven feedback in blended learning (AF-BL), students benefit from tailored learning experiences that promote equal opportunities for growth and reflection. A total of 43 students from a public university in China participated in a 4-week music theory course. They were divided into two groups: an experimental group (<em>N</em> = 22) utilizing the AF-BL method, and a control group (<em>N</em> = 21) following the conventional blended learning (C-BL) method. The results demonstrated that the AF-BL method significantly improved learners' music theory learning outcome and perceptions, compared to the C-BL method. Interviews with participants further highlighted the inclusivity and accessibility of the AF-BL approach, noting its ability to cater to diverse learning needs and provide equal learning opportunities for all students. The findings highlight the potential of AI in creating equitable and inclusive educational experiences, suggesting promising directions for future research and practical applications in music theory education.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"66 ","pages":"Article 101018"},"PeriodicalIF":6.4,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-10DOI: 10.1016/j.iheduc.2025.101017
Gediminas Lipnickas , Joanne Harris , Bora Qesja , Svetlana De Vos
With the growth of the online higher education sector, educational institutions are increasingly creating asynchronous online courses resembling Massive Open Online Courses (MOOCs), characterised by reduced interpersonal interactions. While these courses offer higher flexibility for students, much remains unknown about how the design of these courses impacts student behaviour and performance. This study combines learning analytics and learning design (via Open University Learning Design Initiative (OULDI) taxonomy) to examine effective online course design elements in a 100 % online environment. Effectiveness is evaluated based on the impact of design elements on student engagement and performance. Student engagement patterns throughout the degree are also explored. Results show that while assimilative activities are those most frequently undertaken by students, they rank as fourth in impact on performance. Experiential, interactive/adaptive, and productive activities, though more impactful, are less common and constitute only a fraction of online course design activities. Students were also more likely to engage with videos as opposed to readings, indicating a preference for this type of content in the online learning environment. Furthermore, an inverse correlation was found between students attempting a range of activities, and the need to communicate with staff (i.e., asking for clarification/guidance). Results also identified six types of student engagement patterns, revealing a transition over time towards an assessment focus, where students self-optimise and prioritise assessment completion (over other content/activities). In an online environment, where introducing sequential/scaffolding activities may prove difficult, findings indicate that activities should be clearly linked to assessments to cater for student engagement patterns.
{"title":"Adaptive online course design: Analysis of changes in student behaviour throughout the degree lifecycle","authors":"Gediminas Lipnickas , Joanne Harris , Bora Qesja , Svetlana De Vos","doi":"10.1016/j.iheduc.2025.101017","DOIUrl":"10.1016/j.iheduc.2025.101017","url":null,"abstract":"<div><div>With the growth of the online higher education sector, educational institutions are increasingly creating asynchronous online courses resembling Massive Open Online Courses (MOOCs), characterised by reduced interpersonal interactions. While these courses offer higher flexibility for students, much remains unknown about how the design of these courses impacts student behaviour and performance. This study combines learning analytics and learning design (via Open University Learning Design Initiative (OULDI) taxonomy) to examine effective online course design elements in a 100 % online environment. Effectiveness is evaluated based on the impact of design elements on student engagement and performance. Student engagement patterns throughout the degree are also explored. Results show that while assimilative activities are those most frequently undertaken by students, they rank as fourth in impact on performance. Experiential, interactive/adaptive, and productive activities, though more impactful, are less common and constitute only a fraction of online course design activities. Students were also more likely to engage with videos as opposed to readings, indicating a preference for this type of content in the online learning environment. Furthermore, an inverse correlation was found between students attempting a range of activities, and the need to communicate with staff (i.e., asking for clarification/guidance). Results also identified six types of student engagement patterns, revealing a transition over time towards an assessment focus, where students self-optimise and prioritise assessment completion (over other content/activities). In an online environment, where introducing sequential/scaffolding activities may prove difficult, findings indicate that activities should be clearly linked to assessments to cater for student engagement patterns.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"66 ","pages":"Article 101017"},"PeriodicalIF":6.4,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although the adoption of AI-generated content, such as ChatGPT, has extensively transformed traditional teaching and learning paradigms, the critical question of the effectiveness of ChatGPT content in lesson preparation remains largely unanswered. Therefore, this research aims to understand the determinants that drive or hinder this effectiveness, which is crucial to realizing the full potential of AI technologies in the academic landscape. Relying on a global sample of academic instructors surveyed, we found that individual-level factors, such as instructor confidence, and frequency of use had a positive effect on ChatGPT-generated content effectiveness in class preparation. However, academic work intensity had a negative association with effectiveness. The study also revealed that institutional-level factors, such as training and support, institutional culture, and course complexity exerted a positive impact on ChatGPT content effectiveness. Additionally, the analysis reported that the course complexity-moderated interactions of instructor confidence and work intensity on the effectiveness of ChatGPT content in lesson preparation were significant. We also revealed that the frequency of ChatGPT use significantly moderated the nexus between institutional-level factors (e.g., training and support, and institutional culture) and individual-level factors (e.g., instructor confidence and work intensity) with ChatGPT content effectiveness. The study also provides actionable insights for a wide range of stakeholders, such as higher educational institutions (HEIs), academic instructors, regulators in higher education, and EdTech developers, to understand how to empower educators to leverage AI tools more effectively, ultimately enhancing teaching efficiency and education outcomes.
{"title":"Beyond hype: Is ChatGPT-generated content effective in class preparation among academic instructors?","authors":"Saeed Awadh Bin-Nashwan , Mohamed Bouteraa , Abderrahim Benlahcene , Mouad Sadallah","doi":"10.1016/j.iheduc.2025.101016","DOIUrl":"10.1016/j.iheduc.2025.101016","url":null,"abstract":"<div><div>Although the adoption of AI-generated content, such as ChatGPT, has extensively transformed traditional teaching and learning paradigms, the critical question of the effectiveness of ChatGPT content in lesson preparation remains largely unanswered. Therefore, this research aims to understand the determinants that drive or hinder this effectiveness, which is crucial to realizing the full potential of AI technologies in the academic landscape. Relying on a global sample of academic instructors surveyed, we found that individual-level factors, such as instructor confidence, and frequency of use had a positive effect on ChatGPT-generated content effectiveness in class preparation. However, academic work intensity had a negative association with effectiveness. The study also revealed that institutional-level factors, such as training and support, institutional culture, and course complexity exerted a positive impact on ChatGPT content effectiveness. Additionally, the analysis reported that the course complexity-moderated interactions of instructor confidence and work intensity on the effectiveness of ChatGPT content in lesson preparation were significant. We also revealed that the frequency of ChatGPT use significantly moderated the nexus between institutional-level factors (e.g., training and support, and institutional culture) and individual-level factors (e.g., instructor confidence and work intensity) with ChatGPT content effectiveness. The study also provides actionable insights for a wide range of stakeholders, such as higher educational institutions (HEIs), academic instructors, regulators in higher education, and EdTech developers, to understand how to empower educators to leverage AI tools more effectively, ultimately enhancing teaching efficiency and education outcomes.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"66 ","pages":"Article 101016"},"PeriodicalIF":6.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-30DOI: 10.1016/j.iheduc.2025.101015
Yaser Hasan Al-Mamary, Aliyu Alhaji Abubakar
The integration of artificial intelligence (AI), particularly ChatGPT, in higher education is rapidly expanding, offering new avenues for enhancing the learning experience. Despite its potential, the adoption of ChatGPT remains in need of further study, especially in regions like Saudi Arabia. Previous studies have focused on general e-learning tools, but more research needs to examine the specific factors influencing university students' adoption of AI technologies. This study aims to investigate the adoption of ChatGPT among university students in Saudi Arabia, focusing on the mediating role of Technology-to-Performance Chain (TPC) theory between Self-Determination Theory (SDT) constructs (autonomy, competence, and relatedness) and students' intentions to adopt ChatGPT. It also seeks to identify which SDT factors most significantly affect the adoption process. Using a quantitative approach, this study collected data from 253 university students in Saudi Arabia. Structural equation modelling was used to analyze the collected data and determine the relationship between self-determination theory (SDT), technology-to-performance chain theory (TPC) and ChatGPT adoption. Findings reveal that the perceived autonomy and relatedness significantly affect TTF and ChatGPT utilisation, whereas perceived competence has no effect. In addition, TTF and utilisation are the main predictors of intention to adopt ChatGPT. These findings can be useful for educational policy makers and researchers because they indicate that to enhance university students' adoption of AI technologies, focus should be given to their psychological needs. The results also show that enhancing students' self-determination and their perceived connection with technology can significantly affect their decision to adopt such technologies. This research also presents a new model wherein SDT is integrated with TPC with regard to AI in higher education, specifically in the context of Saudi Arabia. This work contributes to the current literature on AI in education with emphasis on cultural specificities of adoption processes.
{"title":"Empowering ChatGPT adoption in higher education: A comprehensive analysis of university students' intention to adopt artificial intelligence using self-determination and technology-to-performance chain theories","authors":"Yaser Hasan Al-Mamary, Aliyu Alhaji Abubakar","doi":"10.1016/j.iheduc.2025.101015","DOIUrl":"10.1016/j.iheduc.2025.101015","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI), particularly ChatGPT, in higher education is rapidly expanding, offering new avenues for enhancing the learning experience. Despite its potential, the adoption of ChatGPT remains in need of further study, especially in regions like Saudi Arabia. Previous studies have focused on general e-learning tools, but more research needs to examine the specific factors influencing university students' adoption of AI technologies. This study aims to investigate the adoption of ChatGPT among university students in Saudi Arabia, focusing on the mediating role of Technology-to-Performance Chain (TPC) theory between Self-Determination Theory (SDT) constructs (autonomy, competence, and relatedness) and students' intentions to adopt ChatGPT. It also seeks to identify which SDT factors most significantly affect the adoption process. Using a quantitative approach, this study collected data from 253 university students in Saudi Arabia. Structural equation modelling was used to analyze the collected data and determine the relationship between self-determination theory (SDT), technology-to-performance chain theory (TPC) and ChatGPT adoption. Findings reveal that the perceived autonomy and relatedness significantly affect TTF and ChatGPT utilisation, whereas perceived competence has no effect. In addition, TTF and utilisation are the main predictors of intention to adopt ChatGPT. These findings can be useful for educational policy makers and researchers because they indicate that to enhance university students' adoption of AI technologies, focus should be given to their psychological needs. The results also show that enhancing students' self-determination and their perceived connection with technology can significantly affect their decision to adopt such technologies. This research also presents a new model wherein SDT is integrated with TPC with regard to AI in higher education, specifically in the context of Saudi Arabia. This work contributes to the current literature on AI in education with emphasis on cultural specificities of adoption processes.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"66 ","pages":"Article 101015"},"PeriodicalIF":6.4,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1016/j.iheduc.2025.101014
Xin Zhao , Andrew Cox , Xuanning Chen
The use of generative AI is controversial in education largely because of its potential impact on academic integrity. Yet some scholars have suggested it could be particularly beneficial for students with disabilities. To date there has been no empirical research to discover how these students use generative AI in academic writing. Informed by a prior interview study and AI-literacy model, we surveyed students regarding their use of generative AI, and gained 124 valid responses from students with disabilities. We identified primary conditions affecting writing such as ADHD, dyslexia, dyspraxia, and autism. The main generative AI used were chatbots, particularly ChatGPT, and rewriting applications. They were used in a wide range of academic writing tasks. Key concerns students with disabilities had included the inaccuracy of AI answers, risks to academic integrity, and subscription cost barriers. Students expressed a strong desire to participate in AI policymaking and for universities to provide generative AI training. The paper concludes with recommendations to address educational disparities and foster inclusivity.
{"title":"The use of generative AI by students with disabilities in higher education","authors":"Xin Zhao , Andrew Cox , Xuanning Chen","doi":"10.1016/j.iheduc.2025.101014","DOIUrl":"10.1016/j.iheduc.2025.101014","url":null,"abstract":"<div><div>The use of generative AI is controversial in education largely because of its potential impact on academic integrity. Yet some scholars have suggested it could be particularly beneficial for students with disabilities. To date there has been no empirical research to discover how these students use generative AI in academic writing. Informed by a prior interview study and AI-literacy model, we surveyed students regarding their use of generative AI, and gained 124 valid responses from students with disabilities. We identified primary conditions affecting writing such as ADHD, dyslexia, dyspraxia, and autism. The main generative AI used were chatbots, particularly ChatGPT, and rewriting applications. They were used in a wide range of academic writing tasks. Key concerns students with disabilities had included the inaccuracy of AI answers, risks to academic integrity, and subscription cost barriers. Students expressed a strong desire to participate in AI policymaking and for universities to provide generative AI training. The paper concludes with recommendations to address educational disparities and foster inclusivity.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"66 ","pages":"Article 101014"},"PeriodicalIF":6.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-03-06DOI: 10.1016/j.iheduc.2025.101012
Nuoen Li, Kit-Ling Lau
While the theoretical value of the Community of Inquiry (CoI) framework in comprehending online learning experiences has been acknowledged, the newly introduced CoI element—learning presence—has received insufficient attention in the field of foreign language (FL) education. Drawing on the constructivist stimulus-mediation-response approach, this study investigated the predicting effects of teaching presence and online interaction on learning presence. Data were collected from 460 college-level online Chinese as a Foreign Language (CFL) learners at seven Chinese higher education institutions. Partial least squares structural equation modeling (PLS-SEM) was used to explore the linear relationships, followed by the artificial neural network (ANN) technique to assess the relative importance of predictors based on the nonlinear relationships between variables in the research model. The results support most of the predictive effects of teaching presence and online interaction on learning presence variables (self-efficacy, metacognitive self-regulation, and metacognitive co-regulation). Teaching presence, learner-instructor interaction, and learner-learner interaction were the most influential predictors of self-efficacy, metacognitive self-regulation, and metacognitive co-regulation, respectively. Furthermore, the mediating role of online interaction between teaching presence and learning presence was partially supported. The findings highlight the critical roles of teaching presence and online interaction in fostering online FL learners' active and responsible learning while offering valuable insights into the design of online language courses.
{"title":"A two-staged SEM-ANN approach to predict learning presence in online foreign language education: The role of teaching presence and online interaction","authors":"Nuoen Li, Kit-Ling Lau","doi":"10.1016/j.iheduc.2025.101012","DOIUrl":"10.1016/j.iheduc.2025.101012","url":null,"abstract":"<div><div>While the theoretical value of the Community of Inquiry (CoI) framework in comprehending online learning experiences has been acknowledged, the newly introduced CoI element—learning presence—has received insufficient attention in the field of foreign language (FL) education. Drawing on the constructivist stimulus-mediation-response approach, this study investigated the predicting effects of teaching presence and online interaction on learning presence. Data were collected from 460 college-level online Chinese as a Foreign Language (CFL) learners at seven Chinese higher education institutions. Partial least squares structural equation modeling (PLS-SEM) was used to explore the linear relationships, followed by the artificial neural network (ANN) technique to assess the relative importance of predictors based on the nonlinear relationships between variables in the research model. The results support most of the predictive effects of teaching presence and online interaction on learning presence variables (self-efficacy, metacognitive self-regulation, and metacognitive co-regulation). Teaching presence, learner-instructor interaction, and learner-learner interaction were the most influential predictors of self-efficacy, metacognitive self-regulation, and metacognitive co-regulation, respectively. Furthermore, the mediating role of online interaction between teaching presence and learning presence was partially supported. The findings highlight the critical roles of teaching presence and online interaction in fostering online FL learners' active and responsible learning while offering valuable insights into the design of online language courses.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"66 ","pages":"Article 101012"},"PeriodicalIF":6.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21DOI: 10.1016/j.iheduc.2025.101003
Tingting Li, Zehui Zhan, Yu Ji, Tongde Li
Inclusive STEM teacher training plays a critical role in shaping the future of STEM teaching practices and improving educational outcomes for all students, particularly those from marginalized and underrepresented backgrounds. This study investigates the inclusive collaborative learning framework for enhancing STEM teaching among student teachers, focusing on interpersonal and human-machine (generative artificial intelligence) collaboration. Employing a Self-Determination Theory guided approach, two rounds of exploratory studies were conducted. Study 1 compared the effects of interpersonal collaboration (TSPL: in-Service Teacher-Student Teacher Pair Learning) and human-machine collaboration (CSPL: ChatGPT-Student Teacher Pair Learning). Building on Study 1, Study 2 employed a hybrid inclusive collaborative learning model (iHMCL: integrated Human-Machine Collaborative Learning) with expanded participant demographics, blended course formats, and integrated peer, expert, and AI feedback mechanisms. The two-year iterative empirical research revealed differences in the impact of the three collaborative learning approaches on student teachers' learning. CSPL and iHMCL groups outperformed TSPL in STEM teaching knowledge and cognitive load, while TSPL and iHMCL excelled in STEM teaching ability compared to CSPL. The SDT-based inclusive collaborative learning framework for STEM teacher training proved effective, with noted implications. In the future, the integration of generative artificial intelligence and cross boundary learning in inclusive STEM teacher education will require educators to redefine their roles, emphasizing emotional support, critical thinking, and creativity, ensuring that AI complements rather than replaces hands-on, reality-based learning.
{"title":"Exploring human and AI collaboration in inclusive STEM teacher training: A synergistic approach based on self-determination theory","authors":"Tingting Li, Zehui Zhan, Yu Ji, Tongde Li","doi":"10.1016/j.iheduc.2025.101003","DOIUrl":"10.1016/j.iheduc.2025.101003","url":null,"abstract":"<div><div>Inclusive STEM teacher training plays a critical role in shaping the future of STEM teaching practices and improving educational outcomes for all students, particularly those from marginalized and underrepresented backgrounds. This study investigates the inclusive collaborative learning framework for enhancing STEM teaching among student teachers, focusing on interpersonal and human-machine (generative artificial intelligence) collaboration. Employing a Self-Determination Theory guided approach, two rounds of exploratory studies were conducted. Study 1 compared the effects of interpersonal collaboration (TSPL: in-Service Teacher-Student Teacher Pair Learning) and human-machine collaboration (CSPL: ChatGPT-Student Teacher Pair Learning). Building on Study 1, Study 2 employed a hybrid inclusive collaborative learning model (iHMCL: integrated Human-Machine Collaborative Learning) with expanded participant demographics, blended course formats, and integrated peer, expert, and AI feedback mechanisms. The two-year iterative empirical research revealed differences in the impact of the three collaborative learning approaches on student teachers' learning. CSPL and iHMCL groups outperformed TSPL in STEM teaching knowledge and cognitive load, while TSPL and iHMCL excelled in STEM teaching ability compared to CSPL. The SDT-based inclusive collaborative learning framework for STEM teacher training proved effective, with noted implications. In the future, the integration of generative artificial intelligence and cross boundary learning in inclusive STEM teacher education will require educators to redefine their roles, emphasizing emotional support, critical thinking, and creativity, ensuring that AI complements rather than replaces hands-on, reality-based learning.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 101003"},"PeriodicalIF":6.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21DOI: 10.1016/j.iheduc.2025.101002
Tim Coughlan , Francisco Iniesto
Administrative burden is a recognised cause of inequities for disabled students. Experiences of sharing information about disabilities and arranging adjustments can be demoralising and present barriers to success. To explore how Artificial Intelligence technologies could improve this situation, a virtual assistant (VA) was iteratively developed and deployed to support the initial steps of the process through which students share information. Here we describe findings from an eight-month trial where this was made available for students to use as an alternative to completing a form when declaring disabilities. 544 students tried using the assistant during this period. We analyse 351 questions asked of the VA by students, and a feedback survey with 129 responses. Results indicate the types of support expected while interacting with a VA and provide feedback on aspects of the design, the relationship with wider processes and experience of use. Overall, most participants wanted to continue using a VA in these processes, with positive perceptions across disability categories. We identify 12 themes showing a broad range of questions asked of the assistant. Given recent advances in AI, we discuss the opportunities and challenges to build on this and develop further inclusive innovations. Future work should focus on enabling context-informed answers to questions, enabling students to learn and contribute through the conversation, managing expectations according to VA capabilities, enhancing and monitoring inclusivity and integrating the VA with wider processes.
{"title":"What should I know? Analysing behaviour and feedback from student use of a virtual assistant to share information about disabilities","authors":"Tim Coughlan , Francisco Iniesto","doi":"10.1016/j.iheduc.2025.101002","DOIUrl":"10.1016/j.iheduc.2025.101002","url":null,"abstract":"<div><div>Administrative burden is a recognised cause of inequities for disabled students. Experiences of sharing information about disabilities and arranging adjustments can be demoralising and present barriers to success. To explore how Artificial Intelligence technologies could improve this situation, a virtual assistant (VA) was iteratively developed and deployed to support the initial steps of the process through which students share information. Here we describe findings from an eight-month trial where this was made available for students to use as an alternative to completing a form when declaring disabilities. 544 students tried using the assistant during this period. We analyse 351 questions asked of the VA by students, and a feedback survey with 129 responses. Results indicate the types of support expected while interacting with a VA and provide feedback on aspects of the design, the relationship with wider processes and experience of use. Overall, most participants wanted to continue using a VA in these processes, with positive perceptions across disability categories. We identify 12 themes showing a broad range of questions asked of the assistant. Given recent advances in AI, we discuss the opportunities and challenges to build on this and develop further inclusive innovations. Future work should focus on enabling context-informed answers to questions, enabling students to learn and contribute through the conversation, managing expectations according to VA capabilities, enhancing and monitoring inclusivity and integrating the VA with wider processes.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"66 ","pages":"Article 101002"},"PeriodicalIF":6.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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