Pub Date : 2025-10-01Epub Date: 2025-06-01DOI: 10.1016/j.iheduc.2025.101021
Ahmed Lachheb , Javier Leung , Victoria Abramenka-Lachheb , Rajagopal Sankaranarayanan
To better characterize and understand AI in higher education and its role in relation to educational disparities and inclusivity, this paper presents a comprehensive bibliometric assessment of research on AI in higher education. Using quantitative topic modeling and qualitative analysis methods, this study describes: (1) the research landscape of AI in higher education and (2) the common topics of AI in higher education research, including topics related to inclusive education. Based on these descriptions, this study offers a synthesis and critique of research on AI in higher education on the following issues: (a) the use of AI to address educational disparities and foster inclusivity, (b) the ethics of AI-powered large language learning models and translation tools, and (c) AI literacy. The findings of this study call on higher education scholars/researchers to reaffirm higher education research and educational mission, and the standards of rigorous research to lead the knowledge on AI.
{"title":"AI in higher education: A bibliometric analysis, synthesis, and a critique of research","authors":"Ahmed Lachheb , Javier Leung , Victoria Abramenka-Lachheb , Rajagopal Sankaranarayanan","doi":"10.1016/j.iheduc.2025.101021","DOIUrl":"10.1016/j.iheduc.2025.101021","url":null,"abstract":"<div><div>To better characterize and understand AI in higher education and its role in relation to educational disparities and inclusivity, this paper presents a comprehensive bibliometric assessment of research on AI in higher education. Using quantitative topic modeling and qualitative analysis methods, this study describes: (1) the research landscape of AI in higher education and (2) the common topics of AI in higher education research, including topics related to inclusive education. Based on these descriptions, this study offers a synthesis and critique of research on AI in higher education on the following issues: (a) the use of AI to address educational disparities and foster inclusivity, (b) the ethics of AI-powered large language learning models and translation tools, and (c) AI literacy. The findings of this study call on higher education scholars/researchers to reaffirm higher education research and educational mission, and the standards of rigorous research to lead the knowledge on AI.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"67 ","pages":"Article 101021"},"PeriodicalIF":6.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313229","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 : 2025-10-01Epub Date: 2025-06-15DOI: 10.1016/j.iheduc.2025.101032
Arita L. Liu , Philip H. Winne , John C. Nesbit
Asynchronous online discussions (AOD) offer pedagogical advantages as a social learning tool, but their success largely depends on students' motivated participation and sustained engagement. Recent research highlights the potential of leveraging temporal data to understand discussion dynamics and inform instructional strategies. However, the role of contextual factors in analyzing temporal data has not been systematically investigated. To address this gap, this study examines the interplay between temporal patterns and contextual factors including discussion format, group configuration, and course sessions. We analyzed logged timestamp data from 22 online discussions in a university course offered across two semesters to examine temporal patterns of participation in various contexts. Data visualization and linear mixed-effects modeling revealed a dominant trend of deadline-oriented posting behaviors. Cluster analysis results further indicated timely engagement, consistent responsiveness, and ongoing participation are key to academic success. Our findings suggest that discussion design often overlooks temporal aspects, which may contribute to suboptimal engagement. To address this, we propose a temporal structuring approach that combines explicit instructor-imposed schedules with implicit socially constructed temporal structure, supplemented by soft nudges to promote autonomy and sustain discussion engagement. The study concludes with theoretical and practical implications for optimizing online discussion design.
{"title":"Temporal structuring in asynchronous discussions: Designing for collaborative learning in online university courses","authors":"Arita L. Liu , Philip H. Winne , John C. Nesbit","doi":"10.1016/j.iheduc.2025.101032","DOIUrl":"10.1016/j.iheduc.2025.101032","url":null,"abstract":"<div><div>Asynchronous online discussions (AOD) offer pedagogical advantages as a social learning tool, but their success largely depends on students' motivated participation and sustained engagement. Recent research highlights the potential of leveraging temporal data to understand discussion dynamics and inform instructional strategies. However, the role of contextual factors in analyzing temporal data has not been systematically investigated. To address this gap, this study examines the interplay between temporal patterns and contextual factors including discussion format, group configuration, and course sessions. We analyzed logged timestamp data from 22 online discussions in a university course offered across two semesters to examine temporal patterns of participation in various contexts. Data visualization and linear mixed-effects modeling revealed a dominant trend of deadline-oriented posting behaviors. Cluster analysis results further indicated timely engagement, consistent responsiveness, and ongoing participation are key to academic success. Our findings suggest that discussion design often overlooks temporal aspects, which may contribute to suboptimal engagement. To address this, we propose a temporal structuring approach that combines explicit instructor-imposed schedules with implicit socially constructed temporal structure, supplemented by soft nudges to promote autonomy and sustain discussion engagement. The study concludes with theoretical and practical implications for optimizing online discussion design.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"67 ","pages":"Article 101032"},"PeriodicalIF":6.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321374","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 : 2025-10-01Epub Date: 2025-07-01DOI: 10.1016/j.iheduc.2025.101035
Yanqing Wang , Shaoying Gong , Yang Cao , Ying Liu
To increase human-computer interaction and motivate university students' online learning, this study investigated the effects of affective pedagogical agent (PA) providing parallel empathy and reactive empathy in an online learning scenario. In a 2 (Parallel empathy: yes vs. no) × 2 (Reactive empathy: yes vs. no) between-subjects design, 122 university students learned 10 psychological concepts about judgment and decision-making while their eye movements were tracked and physiological arousals (i.e., heart rate and electrodermal activity) were detected. The results found that (a) affective PA that provides learners with reactive empathy could enhance learners' physiological arousal, guide learners to devote more attention to key learning content, and improve retention and transfer performances; (b) affective PA providing both parallel empathy and reactive empathy was more likely to promote transfer performance than those only providing parallel empathy or reactive empathy; (c) affective PA neither increased learners' extraneous cognitive load nor distracted learners' attention. In summary, this study confirms that reactive empathy may be the most important empathy provided by affective PA, and demonstrates that affective PA providing both parallel empathy and reactive empathy were most effective in promoting deep learning. Research findings help enrich prior theories and provide a reference for future researchers to design affective PA.
{"title":"Parallel empathy or reactive empathy? The role of emotional support provided by affective pedagogical agent in online learning","authors":"Yanqing Wang , Shaoying Gong , Yang Cao , Ying Liu","doi":"10.1016/j.iheduc.2025.101035","DOIUrl":"10.1016/j.iheduc.2025.101035","url":null,"abstract":"<div><div>To increase human-computer interaction and motivate university students' online learning, this study investigated the effects of affective pedagogical agent (PA) providing parallel empathy and reactive empathy in an online learning scenario. In a 2 (Parallel empathy: yes vs. no) × 2 (Reactive empathy: yes vs. no) between-subjects design, 122 university students learned 10 psychological concepts about judgment and decision-making while their eye movements were tracked and physiological arousals (i.e., heart rate and electrodermal activity) were detected. The results found that (a) affective PA that provides learners with reactive empathy could enhance learners' physiological arousal, guide learners to devote more attention to key learning content, and improve retention and transfer performances; (b) affective PA providing both parallel empathy and reactive empathy was more likely to promote transfer performance than those only providing parallel empathy or reactive empathy; (c) affective PA neither increased learners' extraneous cognitive load nor distracted learners' attention. In summary, this study confirms that reactive empathy may be the most important empathy provided by affective PA, and demonstrates that affective PA providing both parallel empathy and reactive empathy were most effective in promoting deep learning. Research findings help enrich prior theories and provide a reference for future researchers to design affective PA.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"67 ","pages":"Article 101035"},"PeriodicalIF":6.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565724","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 : 2025-06-01Epub 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":"2025-06-01","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 : 2025-06-01Epub 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":"2025-06-01","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 : 2025-06-01Epub 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":"2025-06-01","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 : 2025-06-01Epub 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":"2025-06-01","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 : 2025-06-01Epub 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":"2025-06-01","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":"2025-06-01","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 : 2025-06-01Epub 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.
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