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}
Pub Date : 2025-02-14DOI: 10.1016/j.iheduc.2025.101000
Yukyeong Song , Chenglu Li , Wanli Xing , Bailing Lyu , Wangda Zhu
Entities such as governments and universities have begun using AI for algorithmic decision-making that impacts people's lives. Despite their known benefits, such as efficiency, the public has raised concerns about the fairness of AI's decision-making. Here, the concept of perceived fairness, defined as people's emotional, cognitive, and behavioral responses toward the justice of the AI system, has been widely discussed as one of the important factors in determining technology acceptance. In the field of AI in education, students are among the biggest stakeholders; thus, it is important to consider students' perceived fairness of AI decision-making systems to gauge technology acceptance. This study adopted an explanatory sequential mixed-method research design involving 428 college students to investigate the factors that impact students' perceived fairness of AI's pass-or-fail prediction decisions in the context of math learning and suggest ways to improve the perceived fairness based on students' voices. The findings suggest that students who received a favorable prediction outcome (i.e., pass), who were presented with a system that had a lower algorithmic bias and higher transparency, who major(ed) in STEM (vs. non-STEM), who have higher math anxiety, and who received the outcome that matches their math knowledge level (i.e., accurate) tend to report a higher level of perceived fairness for the AI's prediction decisions. Interesting interaction effects were also found regarding decision-making, students' math anxiety and knowledge, and the outcome's favorability on students' perceived fairness. Qualitative thematic analysis revealed students' strong desire for transparency with guidance, explainability, and interactive communication with the AI system, as well as constructive feedback and emotional support. This study contributes to the development of a justice theory in the era of AI and suggests practical design implications for AI systems and communication strategies with AI systems in education.
{"title":"Investigating perceived fairness of AI prediction system for math learning: A mixed-methods study with college students","authors":"Yukyeong Song , Chenglu Li , Wanli Xing , Bailing Lyu , Wangda Zhu","doi":"10.1016/j.iheduc.2025.101000","DOIUrl":"10.1016/j.iheduc.2025.101000","url":null,"abstract":"<div><div>Entities such as governments and universities have begun using AI for algorithmic decision-making that impacts people's lives. Despite their known benefits, such as efficiency, the public has raised concerns about the fairness of AI's decision-making. Here, the concept of perceived fairness, defined as people's emotional, cognitive, and behavioral responses toward the justice of the AI system, has been widely discussed as one of the important factors in determining technology acceptance. In the field of AI in education, students are among the biggest stakeholders; thus, it is important to consider students' perceived fairness of AI decision-making systems to gauge technology acceptance. This study adopted an explanatory sequential mixed-method research design involving 428 college students to investigate the factors that impact students' perceived fairness of AI's pass-or-fail prediction decisions in the context of math learning and suggest ways to improve the perceived fairness based on students' voices. The findings suggest that students who received a favorable prediction outcome (i.e., pass), who were presented with a system that had a lower algorithmic bias and higher transparency, who major(ed) in STEM (vs. non-STEM), who have higher math anxiety, and who received the outcome that matches their math knowledge level (i.e., accurate) tend to report a higher level of perceived fairness for the AI's prediction decisions. Interesting interaction effects were also found regarding decision-making, students' math anxiety and knowledge, and the outcome's favorability on students' perceived fairness. Qualitative thematic analysis revealed students' strong desire for transparency with guidance, explainability, and interactive communication with the AI system, as well as constructive feedback and emotional support. This study contributes to the development of a justice theory in the era of AI and suggests practical design implications for AI systems and communication strategies with AI systems in education.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 101000"},"PeriodicalIF":6.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444742","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}
The last two decades of online learning research vastly flourished by examining discussion board text data through content analysis based on constructs like cognitive presence (CP) with the Practical Inquiry Model (PIM). The PIM sets a footprint for how cognitive development unfolds in collaborative inquiry in online learning experiences. Ironically, content analysis is a resource-intensive endeavor in terms of time and expertise, making researchers look for ways to automate text classification through ensemble machine-learning algorithms. We leveraged large language models (LLMs) through OpenAI's Generative Pre-Trained Transformer (GPT) models in the public API to automate the content analysis of students' text data based on PIM indicators and assess the reliability and efficiency of automated content analysis compared to human analysis. Using the seven steps of the Large Language Model Content Analysis (LACA) approach, we proposed an AI-adapted CP codebook leveraging prompt engineering techniques (i.e., role, chain-of-thought, one-shot, few-shot) for the automated content analysis of CP. We found that a fine-tuned model with a one-shot prompt achieved moderate interrater reliability with researchers. The models were more reliable when classifying students' discussion board text in the Integration phase of the PIM. A cost comparison showed an obvious cost advantage of LACA approaches in online learning research in terms of efficiency. Nevertheless, practitioners still need considerable data literacy skills to deploy LACA at a scale. We offer theoretical suggestions for simplifying the CP codebook and improving the IRR with LLM. Implications for practice are discussed, and future research that includes instructional advice is recommended.
{"title":"Transforming online learning research: Leveraging GPT large language models for automated content analysis of cognitive presence","authors":"Daniela Castellanos-Reyes , Larisa Olesova , Ayesha Sadaf","doi":"10.1016/j.iheduc.2025.101001","DOIUrl":"10.1016/j.iheduc.2025.101001","url":null,"abstract":"<div><div>The last two decades of online learning research vastly flourished by examining discussion board text data through content analysis based on constructs like cognitive presence (CP) with the Practical Inquiry Model (PIM). The PIM sets a footprint for how cognitive development unfolds in collaborative inquiry in online learning experiences. Ironically, content analysis is a resource-intensive endeavor in terms of time and expertise, making researchers look for ways to automate text classification through ensemble machine-learning algorithms. We leveraged large language models (LLMs) through OpenAI's Generative Pre-Trained Transformer (GPT) models in the public API to automate the content analysis of students' text data based on PIM indicators and assess the reliability and efficiency of automated content analysis compared to human analysis. Using the seven steps of the Large Language Model Content Analysis (LACA) approach, we proposed an AI-adapted CP codebook leveraging prompt engineering techniques (i.e., role, chain-of-thought, one-shot, few-shot) for the automated content analysis of CP. We found that a fine-tuned model with a one-shot prompt achieved moderate interrater reliability with researchers. The models were more reliable when classifying students' discussion board text in the Integration phase of the PIM. A cost comparison showed an obvious cost advantage of LACA approaches in online learning research in terms of efficiency. Nevertheless, practitioners still need considerable data literacy skills to deploy LACA at a scale. We offer theoretical suggestions for simplifying the CP codebook and improving the IRR with LLM. Implications for practice are discussed, and future research that includes instructional advice is recommended.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 101001"},"PeriodicalIF":6.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429722","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}
The adoption of artificial intelligence (AI), particularly Large Language Models like ChatGPT, has gained significant traction in the education sector, offering numerous benefits for students and educators alike. This study focuses on the triggers and drivers of ChatGPT adoption within African higher education institutions (HEIs). Utilizing the Technology Acceptance Model (TAM) and the Diffusion of Innovations Theory (DIT) as theoretical frameworks, the research proposes a conceptual model to explore the impact of ChatGPT on perceived usefulness and awareness. Additionally, the study examines how ChatGPT awareness influences perceived usefulness through the intermediary role of ChatGPT knowledge. A quantitative methodology was employed, with data collected from higher education institutions in Morocco, Nigeria, and Tanzania. The Partial Least Squares Structural Equation Modelling (PLS-SEM) technique was used to analyze the data. The findings emphasize the positive impact of triggers on both the perceived usefulness and awareness of ChatGPT. Furthermore, the study highlights the significant effect of ChatGPT awareness on perceived usefulness, mediated by knowledge. The research contributes to the existing literature by providing empirical insights into the adoption of ChatGPT in African HEIs and underscores the importance of awareness and knowledge in enhancing perceived usefulness. The study also offers practical recommendations for educators and policymakers to facilitate the effective integration of AI tools in education, considering regional and demographic variations.
{"title":"Awareness, perception, and adoption of ChatGPT in African HEIs: A multi-dimensional analysis","authors":"Olugbenga Ayo Ojubanire , Sunday Adewale Olaleye , Mohamed Amine Marhraoui , Mesiet William Kamihanda , Oluwatosin Ifedayo Oke , Oluwaseun Abigail Ojubanire","doi":"10.1016/j.iheduc.2025.100999","DOIUrl":"10.1016/j.iheduc.2025.100999","url":null,"abstract":"<div><div>The adoption of artificial intelligence (AI), particularly Large Language Models like ChatGPT, has gained significant traction in the education sector, offering numerous benefits for students and educators alike. This study focuses on the triggers and drivers of ChatGPT adoption within African higher education institutions (HEIs). Utilizing the Technology Acceptance Model (TAM) and the Diffusion of Innovations Theory (DIT) as theoretical frameworks, the research proposes a conceptual model to explore the impact of ChatGPT on perceived usefulness and awareness. Additionally, the study examines how ChatGPT awareness influences perceived usefulness through the intermediary role of ChatGPT knowledge. A quantitative methodology was employed, with data collected from higher education institutions in Morocco, Nigeria, and Tanzania. The Partial Least Squares Structural Equation Modelling (PLS-SEM) technique was used to analyze the data. The findings emphasize the positive impact of triggers on both the perceived usefulness and awareness of ChatGPT. Furthermore, the study highlights the significant effect of ChatGPT awareness on perceived usefulness, mediated by knowledge. The research contributes to the existing literature by providing empirical insights into the adoption of ChatGPT in African HEIs and underscores the importance of awareness and knowledge in enhancing perceived usefulness. The study also offers practical recommendations for educators and policymakers to facilitate the effective integration of AI tools in education, considering regional and demographic variations.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 100999"},"PeriodicalIF":6.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378926","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 : 2025-02-05DOI: 10.1016/j.iheduc.2025.100998
Di Xu , Yujia Liu , Zhiling Meng Shea , Kimberly Vincent-Layton , Jeffrey White , Michelle Pacansky-Brock
The rapid growth of online learning has raised concerns about quality and equity in virtual education. This study introduces the Humanizing Online STEM Academy, a six-week professional development program designed specifically to promote humanizing and inclusive teaching within STEM college online courses. We document in detail the Academy's design and instructional approach, and examine its impact on the perceptions and instructional practices of 79 faculty participants from eight California institutions, using pre- and post-Academy surveys and in-depth interviews. Results indicate that participants found the humanizing elements covered in the Academy highly beneficial for building trust with students. Post-Academy, instructors reported increased confidence in online teaching, stronger belief in their ability to address equity gaps, and enhanced support for diverse student backgrounds. Their instructional approaches also evolved to prioritize interpersonal interactions and individual student needs. Interviews revealed heightened awareness of student diversity and intentional efforts to accommodate it.
{"title":"Humanizing college online instruction: The effects of professional development on faculty perceptions and instructional practices","authors":"Di Xu , Yujia Liu , Zhiling Meng Shea , Kimberly Vincent-Layton , Jeffrey White , Michelle Pacansky-Brock","doi":"10.1016/j.iheduc.2025.100998","DOIUrl":"10.1016/j.iheduc.2025.100998","url":null,"abstract":"<div><div>The rapid growth of online learning has raised concerns about quality and equity in virtual education. This study introduces the Humanizing Online STEM Academy, a six-week professional development program designed specifically to promote humanizing and inclusive teaching within STEM college online courses. We document in detail the Academy's design and instructional approach, and examine its impact on the perceptions and instructional practices of 79 faculty participants from eight California institutions, using pre- and post-Academy surveys and in-depth interviews. Results indicate that participants found the humanizing elements covered in the Academy highly beneficial for building trust with students. Post-Academy, instructors reported increased confidence in online teaching, stronger belief in their ability to address equity gaps, and enhanced support for diverse student backgrounds. Their instructional approaches also evolved to prioritize interpersonal interactions and individual student needs. Interviews revealed heightened awareness of student diversity and intentional efforts to accommodate it.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 100998"},"PeriodicalIF":6.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421787","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 : 2025-02-01DOI: 10.1016/j.iheduc.2025.100997
Vasiliki Papageorgiou , Edgar Meyer , Iro Ntonia
The global expansion of university-level online programmes has heightened the demand for educators to design and facilitate meaningful learning experiences. However, many educators lack the necessary expertise and experience, highlighting the urgency for contextually relevant professional development opportunities. This paper investigates the collaborative design processes of novice online educators and digital learning professionals when designing online learning and the conditions promoting educators’ development. A multiple case study methodology was employed, recruiting six interdisciplinary design teams from five UK-based universities. Data collection involved two phases of semi-structured interviews and design meeting observations. Findings evidence three key processes: (1) framing the design inquiry, (2) sharing and integrating insider knowledge and expertise, and (3) anticipating the future. Emotional support, skilled facilitation and valuing diverse perspectives acted as enabling conditions. We propose network-enabled and boundary-crossing capabilities as novel dimensions of educators’ development. This paper emphasises the need for purposeful collaborative design initiatives for integrated professional development.
{"title":"Investigating the relationship between collaborative design, online learning and educator integrated professional development","authors":"Vasiliki Papageorgiou , Edgar Meyer , Iro Ntonia","doi":"10.1016/j.iheduc.2025.100997","DOIUrl":"10.1016/j.iheduc.2025.100997","url":null,"abstract":"<div><div>The global expansion of university-level online programmes has heightened the demand for educators to design and facilitate meaningful learning experiences. However, many educators lack the necessary expertise and experience, highlighting the urgency for contextually relevant professional development opportunities. This paper investigates the collaborative design processes of novice online educators and digital learning professionals when designing online learning and the conditions promoting educators’ development. A multiple case study methodology was employed, recruiting six interdisciplinary design teams from five UK-based universities. Data collection involved two phases of semi-structured interviews and design meeting observations. Findings evidence three key processes: (1) framing the design inquiry, (2) sharing and integrating insider knowledge and expertise, and (3) anticipating the future. Emotional support, skilled facilitation and valuing diverse perspectives acted as enabling conditions. We propose network-enabled and boundary-crossing capabilities as novel dimensions of educators’ development. This paper emphasises the need for purposeful collaborative design initiatives for integrated professional development.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 100997"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388210","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 : 2025-01-30DOI: 10.1016/j.iheduc.2025.100996
Peijun Wang , Yuhui Jing , Shusheng Shen
Represented by ChatGPT, Generative Artificial Intelligence (GAI) is revolutionizing the field of education. Despite a series of related studies and reviews around GAI, existing reviews predominantly focus on macro-level discussions covering overall development trends, core issues, opportunities and risks. There has been a lack of systematic reviews from a meso-level perspective examining the application of GAI in classroom teaching within higher education. This study employs a systematic literature review method, examining 139 articles from Web of Science, EBSCO, and Scopus databases. Findings include: (1)In terms of disciplines and types of GAI applications, engineering, health and medicine, and language are the most popular, while humanities, social sciences, basic sciences, mathematics, sports sciences, and interdisciplinary fields have fewer applications. Based on Strobel's classification of GAI(2024), it is found that Generators, Reimaginators, and Assistants are the most widely applied types of GAI. In contrast, Synthesizers and Enablers are less commonly utilized. Regarding the adoption trends across disciplines, engineering and language have a diverse range of GAI product types applied, whereas health and medicine has fewer types of GAI products in use. Due to smaller sample sizes, the analysis of GAI product types in the remaining six disciplines is also relatively limited. (2)In terms of the application of GAI across different disciplines, a small portion of GAI applications reflect distinctive disciplinary characteristics. Regarding the roles mapped out by the application of GAI, based on Xu and Ouyang's classification(2022), instructors or students predominantly perceive GAI as “New Subject” or “Direct Mediator", with less emphasis on the role of “Supplement Assistant”. Regarding the integration into the classroom, as assessed through the SAMR framework, most GAI applications are in the Augmentation level. There are also some in the Substitution and Modification levels, while applications in the Redefinition level are relatively rare. (3)In terms of the selection of instructional strategies under GAI applications, there are 18 types of strategies across four orientations, primarily emphasizing constructive and reflective orientations. Strategies focusing on didactic and authentic orientiations are less frequently utilized. Regarding the roles GAI plays as reflected in instructional strategies, it predominantly assumes roles as “New Subject” and “Direct Mediator", with the role of “Supplementary Assistant” yet to be explored. Finally, this study evaluated the instructional application research of GAI from three dimensions: GAI product type and applied discipline, discipline-specific application and integration level, instructional strategies and GAI role, and put forward relevant research suggestions.
以ChatGPT为代表的生成式人工智能（GAI）正在彻底改变教育领域。尽管围绕GAI进行了一系列相关的研究和评论，但现有的评论主要集中在宏观层面的讨论，涵盖了总体发展趋势、核心问题、机遇和风险。从中观角度考察GAI在高等教育课堂教学中的应用，一直缺乏系统的综述。本研究采用系统文献回顾的方法，分析了来自Web of Science、EBSCO和Scopus数据库的139篇文章。结果表明：(1)在GAI应用的学科和类型上，工程、健康医学和语言是最受欢迎的，而人文、社会科学、基础科学、数学、体育科学和跨学科领域的应用较少。根据Strobel对GAI的分类（2024），我们发现generator、Reimaginators和Assistants是GAI中应用最广泛的类型。相比之下，合成器和使能器很少被使用。就跨学科的采用趋势而言，工程和语言领域应用了多种GAI产品类型，而卫生和医学领域使用的GAI产品类型较少。由于样本量较小，其余六个学科对GAI产品类型的分析也相对有限。(2)在GAI跨学科应用方面，少部分GAI应用体现出鲜明的学科特征。对于GAI应用所描绘的角色，根据Xu和Ouyang（2022）的分类，教师或学生主要将GAI视为“新主体”或“直接调解人”，对“补充助手”的重视程度较低。关于与课堂的整合，通过SAMR框架进行评估，大多数GAI应用程序处于增强级别。在替换和修改级别中也有一些，而在重新定义级别中的应用程序相对较少。(3)在GAI应用下的教学策略选择方面，有四个取向的18种策略，主要强调建设性和反思性取向。侧重于说教和真实导向的策略较少使用。从GAI在教学策略中所扮演的角色来看，它主要承担着“新主体”和“直接调解人”的角色，“辅助助手”的角色有待探索。最后，本研究从GAI产品类型与应用学科、特定学科应用与整合水平、教学策略与GAI作用三个维度对GAI教学应用研究进行了评价，并提出了相关研究建议。
{"title":"A systematic literature review on the application of generative artificial intelligence (GAI) in teaching within higher education: Instructional contexts, process, and strategies","authors":"Peijun Wang , Yuhui Jing , Shusheng Shen","doi":"10.1016/j.iheduc.2025.100996","DOIUrl":"10.1016/j.iheduc.2025.100996","url":null,"abstract":"<div><div>Represented by ChatGPT, Generative Artificial Intelligence (GAI) is revolutionizing the field of education. Despite a series of related studies and reviews around GAI, existing reviews predominantly focus on macro-level discussions covering overall development trends, core issues, opportunities and risks. There has been a lack of systematic reviews from a meso-level perspective examining the application of GAI in classroom teaching within higher education. This study employs a systematic literature review method, examining 139 articles from Web of Science, EBSCO, and Scopus databases. Findings include: (1)In terms of disciplines and types of GAI applications, engineering, health and medicine, and language are the most popular, while humanities, social sciences, basic sciences, mathematics, sports sciences, and interdisciplinary fields have fewer applications. Based on Strobel's classification of GAI(2024), it is found that Generators, Reimaginators, and Assistants are the most widely applied types of GAI. In contrast, Synthesizers and Enablers are less commonly utilized. Regarding the adoption trends across disciplines, engineering and language have a diverse range of GAI product types applied, whereas health and medicine has fewer types of GAI products in use. Due to smaller sample sizes, the analysis of GAI product types in the remaining six disciplines is also relatively limited. (2)In terms of the application of GAI across different disciplines, a small portion of GAI applications reflect distinctive disciplinary characteristics. Regarding the roles mapped out by the application of GAI, based on Xu and Ouyang's classification(2022), instructors or students predominantly perceive GAI as “New Subject” or “Direct Mediator\", with less emphasis on the role of “Supplement Assistant”. Regarding the integration into the classroom, as assessed through the SAMR framework, most GAI applications are in the Augmentation level. There are also some in the Substitution and Modification levels, while applications in the Redefinition level are relatively rare. (3)In terms of the selection of instructional strategies under GAI applications, there are 18 types of strategies across four orientations, primarily emphasizing constructive and reflective orientations. Strategies focusing on didactic and authentic orientiations are less frequently utilized. Regarding the roles GAI plays as reflected in instructional strategies, it predominantly assumes roles as “New Subject” and “Direct Mediator\", with the role of “Supplementary Assistant” yet to be explored. Finally, this study evaluated the instructional application research of GAI from three dimensions: GAI product type and applied discipline, discipline-specific application and integration level, instructional strategies and GAI role, and put forward relevant research suggestions.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 100996"},"PeriodicalIF":6.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143300098","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 : 2025-01-28DOI: 10.1016/j.iheduc.2025.100995
Miranda M. McIntyre , Geoffrey Cui , Yunfei Hou
Online education is growing in popularity, yet online courses in science, technology, engineering, and mathematics (STEM) experience lower student satisfaction and higher attrition compared to non-STEM courses. This study explores differences in online course perceptions between STEM and non-STEM students and how these perceptions influence their intentions to enroll in future online courses. A sample of 1245 students was recruited from a diverse range of majors, with 44 % of students majoring in STEM. Relative to non-STEM majors, STEM students felt that online classes offer lower performance outcomes, are less enjoyable, and offer less flexibility while requiring more effort. These perceptions explain 74 % of the variance in intentions to enroll in online courses. Differences among STEM sub-categories were also examined to provide a nuanced picture of students' experiences in online courses. The findings identify strategic improvements for online STEM courses by targeting learning-based performance, flexibility in course modality, and students' enjoyment.
{"title":"Virtual avoidance: Examining STEM students' lower interest in online courses","authors":"Miranda M. McIntyre , Geoffrey Cui , Yunfei Hou","doi":"10.1016/j.iheduc.2025.100995","DOIUrl":"10.1016/j.iheduc.2025.100995","url":null,"abstract":"<div><div>Online education is growing in popularity, yet online courses in science, technology, engineering, and mathematics (STEM) experience lower student satisfaction and higher attrition compared to non-STEM courses. This study explores differences in online course perceptions between STEM and non-STEM students and how these perceptions influence their intentions to enroll in future online courses. A sample of 1245 students was recruited from a diverse range of majors, with 44 % of students majoring in STEM. Relative to non-STEM majors, STEM students felt that online classes offer lower performance outcomes, are less enjoyable, and offer less flexibility while requiring more effort. These perceptions explain 74 % of the variance in intentions to enroll in online courses. Differences among STEM sub-categories were also examined to provide a nuanced picture of students' experiences in online courses. The findings identify strategic improvements for online STEM courses by targeting learning-based performance, flexibility in course modality, and students' enjoyment.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 100995"},"PeriodicalIF":6.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077774","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}
The transition between online and in-person learning has become a recurring challenge in contemporary education, highlighting the need for effective strategies to support students' self-regulated learning (SRL) during these shifts. This study examines the impact of digital diaries on undergraduate students' perceived SRL skills during a critical transition from in-person to online learning. Using a quasi-experimental design, the study compares SRL skills over an 18-week technology integration course, with one group utilizing digital diaries and a control group that did not. The findings reveal that students using digital diaries experienced significant improvements in their SRL skills, particularly in goal setting, environment structuring, help-seeking, and self-evaluation, during the transition to online learning. These enhancements persisted even after returning to in-person learning. In contrast, the control group showed more modest improvements, with SRL skills remaining stable in online settings and only increasing significantly during in-person instruction. This research underscores the potential of digital diaries as a targeted intervention to enhance SRL, particularly in online learning environments, offering educators actionable insights to better support student adaptation during these educational transitions.
{"title":"Digital diaries supporting self-regulated learning during in-person and online transitions","authors":"Zui Cheng , Danyang Zhang , Shan Peng , Xinyi Xiong , Qixiu Xiong","doi":"10.1016/j.iheduc.2025.100994","DOIUrl":"10.1016/j.iheduc.2025.100994","url":null,"abstract":"<div><div>The transition between online and in-person learning has become a recurring challenge in contemporary education, highlighting the need for effective strategies to support students' self-regulated learning (SRL) during these shifts. This study examines the impact of digital diaries on undergraduate students' perceived SRL skills during a critical transition from in-person to online learning. Using a quasi-experimental design, the study compares SRL skills over an 18-week technology integration course, with one group utilizing digital diaries and a control group that did not. The findings reveal that students using digital diaries experienced significant improvements in their SRL skills, particularly in goal setting, environment structuring, help-seeking, and self-evaluation, during the transition to online learning. These enhancements persisted even after returning to in-person learning. In contrast, the control group showed more modest improvements, with SRL skills remaining stable in online settings and only increasing significantly during in-person instruction. This research underscores the potential of digital diaries as a targeted intervention to enhance SRL, particularly in online learning environments, offering educators actionable insights to better support student adaptation during these educational transitions.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 100994"},"PeriodicalIF":6.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049708","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 : 2025-01-19DOI: 10.1016/j.iheduc.2025.100993
Xuehan Zhou , Qiujie Li , Di Xu
With the fast expansion of online learning in higher education, institutions have increasingly offered and mandated faculty professional development (PD) programs focused on online instruction. However, the extent to which these PD programs indeed lead to improved students' online course performance remains largely unknown. This paper used a rigorous quasi-experimental approach to estimate the impact of a PD program on student online course performance at a large community college using a dataset that includes more than 370,000 online course enrollments taught by close to 900 instructors. The analyses yielded robust, nonsignificant estimates for the PD program on both online course persistence and course grades. Further qualitative analysis of the courses taught by PD participants indicated that instructors' integration of elements covered by the PD training into their subsequent teaching was fairly limited, highlighting the need for ongoing support to help instructors incorporate recommended practices into instruction.
{"title":"Does professional development for online instruction improve student course outcomes?","authors":"Xuehan Zhou , Qiujie Li , Di Xu","doi":"10.1016/j.iheduc.2025.100993","DOIUrl":"10.1016/j.iheduc.2025.100993","url":null,"abstract":"<div><div>With the fast expansion of online learning in higher education, institutions have increasingly offered and mandated faculty professional development (PD) programs focused on online instruction. However, the extent to which these PD programs indeed lead to improved students' online course performance remains largely unknown. This paper used a rigorous quasi-experimental approach to estimate the impact of a PD program on student online course performance at a large community college using a dataset that includes more than 370,000 online course enrollments taught by close to 900 instructors. The analyses yielded robust, nonsignificant estimates for the PD program on both online course persistence and course grades. Further qualitative analysis of the courses taught by PD participants indicated that instructors' integration of elements covered by the PD training into their subsequent teaching was fairly limited, highlighting the need for ongoing support to help instructors incorporate recommended practices into instruction.</div></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":"65 ","pages":"Article 100993"},"PeriodicalIF":6.4,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049706","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号