Pub Date : 2025-06-01Epub Date: 2025-03-30DOI: 10.1016/j.caeo.2025.100250
Daniel G. Ferguson, Caitlin Campbell, Zachary L. Nolen, Kristy L. Daniel
Social media has become a major force in the internet age and has gained much interest as a potential new educational tool. Currently, many instructors assume that students have positive perceptions of using social media in their courses. However, there is no instrument that can assess student perceptions of social media use in academic courses. We created and tested the perceptions of social media (POSoM) questionnaire and determined its factorability through an exploratory factor analysis. Using Cronbach's alpha, we confirmed the reliability of our four factors. We found that the POSoM questionnaire contained four reliable factors: Academic Perceptions, Academic Usage, Academic Communication, and Personal Usage. Using the POSoM questionnaire, we found that students do not want to communicate in academic settings through social media and are apathetic to use of social media for other academic uses. Our results provide a new reliable instrument that can be used to further explore students’ perceptions of social media in science courses.
{"title":"Considering and measuring student perceptions on the role of using social media as an educational tool in science courses","authors":"Daniel G. Ferguson, Caitlin Campbell, Zachary L. Nolen, Kristy L. Daniel","doi":"10.1016/j.caeo.2025.100250","DOIUrl":"10.1016/j.caeo.2025.100250","url":null,"abstract":"<div><div>Social media has become a major force in the internet age and has gained much interest as a potential new educational tool. Currently, many instructors assume that students have positive perceptions of using social media in their courses. However, there is no instrument that can assess student perceptions of social media use in academic courses. We created and tested the perceptions of social media (POSoM) questionnaire and determined its factorability through an exploratory factor analysis. Using Cronbach's alpha, we confirmed the reliability of our four factors. We found that the POSoM questionnaire contained four reliable factors: Academic Perceptions, Academic Usage, Academic Communication, and Personal Usage. Using the POSoM questionnaire, we found that students do not want to communicate in academic settings through social media and are apathetic to use of social media for other academic uses. Our results provide a new reliable instrument that can be used to further explore students’ perceptions of social media in science courses.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100250"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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: 2025-05-22DOI: 10.1016/j.caeo.2025.100264
Haniye Seyri , Farhad Ghiasvand
The integration of Artificial Intelligence (AI) technologies into various aspects of second/foreign language (L2) education is recently gaining an unprecedented attention. However, teacher emotionality in light of using AI tools for teaching specific language skills has remained unaddressed, so far. To fill this void, the present qualitative study aimed to unveil English as a foreign language (EFL) teachers’ perceived AI-induced emotions and associated regulatory strategies used during their L2 speaking and writing instruction. A cohort of 21 Iranian EFL teachers were non-randomly picked up to attend a semi-structured interview and complete a written narrative frame. The results of thematic analysis through MAXQDA software divulged that the participants frequently experienced seven positive emotions including ‘excitement’, ‘confidence’, ‘joy’, ‘pride’, ‘satisfaction’, ‘passion’, and ‘engagement’. On the negative side, ‘anxiety’, ‘worry’, ‘stress’, ‘apprehension’, and ‘frustration’ were repeatedly induced by AI tools in L2 speaking and writing classes. Moreover, it was found that four ‘up-regulating’ and five ‘down-regulating’ strategies, either antecedent-focused or response-focused had been commonly employed by the teachers to manage their positive and negative AI-induced emotions. A discussion of the findings and practical implications for considering teacher emotionality when integrating AI technologies into L2 productive skills is provided.
{"title":"“Teaching is basically feeling”: Unpacking EFL Teachers’ perceived emotions and regulatory strategies in AI-Powered L2 speaking and writing skills instruction","authors":"Haniye Seyri , Farhad Ghiasvand","doi":"10.1016/j.caeo.2025.100264","DOIUrl":"10.1016/j.caeo.2025.100264","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) technologies into various aspects of second/foreign language (L2) education is recently gaining an unprecedented attention. However, teacher emotionality in light of using AI tools for teaching specific language skills has remained unaddressed, so far. To fill this void, the present qualitative study aimed to unveil English as a foreign language (EFL) teachers’ perceived AI-induced emotions and associated regulatory strategies used during their L2 speaking and writing instruction. A cohort of 21 Iranian EFL teachers were non-randomly picked up to attend a semi-structured interview and complete a written narrative frame. The results of thematic analysis through MAXQDA software divulged that the participants frequently experienced seven positive emotions including ‘excitement’, ‘confidence’, ‘joy’, ‘pride’, ‘satisfaction’, ‘passion’, and ‘engagement’. On the negative side, ‘anxiety’, ‘worry’, ‘stress’, ‘apprehension’, and ‘frustration’ were repeatedly induced by AI tools in L2 speaking and writing classes. Moreover, it was found that four ‘up-regulating’ and five ‘down-regulating’ strategies, either antecedent-focused or response-focused had been commonly employed by the teachers to manage their positive and negative AI-induced emotions. A discussion of the findings and practical implications for considering teacher emotionality when integrating AI technologies into L2 productive skills is provided.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100264"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-20DOI: 10.1016/j.caeo.2025.100255
Héctor Sánchez San Blas, Sergio García González, André F. Sales Mendes, Gabriel Villarrubia González, Juan F. De Paz Santana
This study investigates how integrating immersive virtual reality with a multi-agent system can improve urban cyclist training by adapting learning experiences to individual performance. Addressing the challenge of preparing cyclists for complex urban environments, the research explores whether an adaptive VR-based system can enhance hazard perception, decision-making, and compliance with traffic rules. The proposed system leverages a context-aware multi-agent framework that dynamically adjusts traffic density, environmental conditions, and scenario complexity based on user behaviour. This personalized approach ensures that training remains challenging yet accessible, fostering progressive skill acquisition in a safe, controlled simulation environment. A preliminary evaluation was conducted with eight participants over a month-long training period. Results indicated improvements in reaction times, safety distance compliance, and overall traffic rule adherence. The system’s adaptability and ability to integrate into existing urban training programs suggest its potential as a scalable, data-driven tool for cyclist education.
{"title":"Improving urban cyclist safety and skills: Integrating a multiagent system and virtual reality training simulations","authors":"Héctor Sánchez San Blas, Sergio García González, André F. Sales Mendes, Gabriel Villarrubia González, Juan F. De Paz Santana","doi":"10.1016/j.caeo.2025.100255","DOIUrl":"10.1016/j.caeo.2025.100255","url":null,"abstract":"<div><div>This study investigates how integrating immersive virtual reality with a multi-agent system can improve urban cyclist training by adapting learning experiences to individual performance. Addressing the challenge of preparing cyclists for complex urban environments, the research explores whether an adaptive VR-based system can enhance hazard perception, decision-making, and compliance with traffic rules. The proposed system leverages a context-aware multi-agent framework that dynamically adjusts traffic density, environmental conditions, and scenario complexity based on user behaviour. This personalized approach ensures that training remains challenging yet accessible, fostering progressive skill acquisition in a safe, controlled simulation environment. A preliminary evaluation was conducted with eight participants over a month-long training period. Results indicated improvements in reaction times, safety distance compliance, and overall traffic rule adherence. The system’s adaptability and ability to integrate into existing urban training programs suggest its potential as a scalable, data-driven tool for cyclist education.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100255"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-14DOI: 10.1016/j.caeo.2025.100257
Sandra Drumm
Learning paths (LP), a combination of internet-based, sequenced learning content and self-learning tasks, enable learning to take place according to individual pace and depth and offer automated tests to check one’s own learning process. A broad diversity in learning approaches within LPs can help heterogeneous student groups to acquire the same knowledge base for further instruction and can be useful in developing blended learning courses. It turns out however, that different students show varying degrees of success when working autonomously with digital learning paths. Based on this, the following questions arise: how do students engage with the digital content; which learning opportunities do the students notice, and how do they use them. The study conducted examined student statements on how they worked through a learning path and why they chose a certain approach. Additionally, students answered questions about their process in a stimulated recall setting in order to find out why they processed the path in a certain way. The results show that success in the course is highly dependent on how much students were able to activate their self-regulating learning skills. Being able to apply their own strategies and working habits helped well-performing students, while the same setting offered too much openness and thus confusion to weaker-performing students. This provides key information on fostering engagement and self-regulated student learning, and how access to lectures for independent study can be implemented in a flipped classroom scenario.
{"title":"Applying individual strategies enhances learning in asynchronous learning paths","authors":"Sandra Drumm","doi":"10.1016/j.caeo.2025.100257","DOIUrl":"10.1016/j.caeo.2025.100257","url":null,"abstract":"<div><div>Learning paths (LP), a combination of internet-based, sequenced learning content and self-learning tasks, enable learning to take place according to individual pace and depth and offer automated tests to check one’s own learning process. A broad diversity in learning approaches within LPs can help heterogeneous student groups to acquire the same knowledge base for further instruction and can be useful in developing blended learning courses. It turns out however, that different students show varying degrees of success when working autonomously with digital learning paths. Based on this, the following questions arise: how do students engage with the digital content; which learning opportunities do the students notice, and how do they use them. The study conducted examined student statements on how they worked through a learning path and why they chose a certain approach. Additionally, students answered questions about their process in a stimulated recall setting in order to find out why they processed the path in a certain way. The results show that success in the course is highly dependent on how much students were able to activate their self-regulating learning skills. Being able to apply their own strategies and working habits helped well-performing students, while the same setting offered too much openness and thus confusion to weaker-performing students. This provides key information on fostering engagement and self-regulated student learning, and how access to lectures for independent study can be implemented in a flipped classroom scenario.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100257"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-02DOI: 10.1016/j.caeo.2025.100245
Solomon Sunday Oyelere , Kehinde Aruleba
In today's era of technological evolution, programming education is crucial for shaping the future workforce and fostering innovation. However, access to quality computer science education remains a significant challenge with Sub-Saharan Africa nations experiencing a pronounced digital divide. Despite growing interest in technology, these countries struggle with unequal access to educational resources. AI-driven tools like ChatGPT, Codey, and GitHub Copilot offer personalized learning experiences that could democratize access to knowledge and reshape programming education. This quantitative study examines the impact of these AI tools on fostering inclusive education in Kenya, Nigeria, and South Africa. It involves 322 university students, using purposive sampling and online questionnaires. Various quantitative analyzes, including descriptive statistics, country-wise comparisons, one-way ANOVA, Kruskal–Wallis tests, and correlation analysis, were conducted. The study reveals students’ motivations for programming, their attitudes towards AI-driven educational tools, and the perceived impact on equity, diversity, and inclusion. Significant variations were found in attitudes based on educational level and country of residence, highlighting the need for tailored strategies to enhance the inclusivity and effectiveness of AI-driven programming education tools.
{"title":"A comparative study of student perceptions on generative AI in programming education across Sub-Saharan Africa","authors":"Solomon Sunday Oyelere , Kehinde Aruleba","doi":"10.1016/j.caeo.2025.100245","DOIUrl":"10.1016/j.caeo.2025.100245","url":null,"abstract":"<div><div>In today's era of technological evolution, programming education is crucial for shaping the future workforce and fostering innovation. However, access to quality computer science education remains a significant challenge with Sub-Saharan Africa nations experiencing a pronounced digital divide. Despite growing interest in technology, these countries struggle with unequal access to educational resources. AI-driven tools like ChatGPT, Codey, and GitHub Copilot offer personalized learning experiences that could democratize access to knowledge and reshape programming education. This quantitative study examines the impact of these AI tools on fostering inclusive education in Kenya, Nigeria, and South Africa. It involves 322 university students, using purposive sampling and online questionnaires. Various quantitative analyzes, including descriptive statistics, country-wise comparisons, one-way ANOVA, Kruskal–Wallis tests, and correlation analysis, were conducted. The study reveals students’ motivations for programming, their attitudes towards AI-driven educational tools, and the perceived impact on equity, diversity, and inclusion. Significant variations were found in attitudes based on educational level and country of residence, highlighting the need for tailored strategies to enhance the inclusivity and effectiveness of AI-driven programming education tools.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100245"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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: 2025-05-02DOI: 10.1016/j.caeo.2025.100260
Frank Reinhold , Priska Sprenger , Gunnar Staniczek
Computational Thinking (CT) has been considered one of the 21st century skills that already primary school students should develop. In this study we compared different methods for guiding a real object through a training maze task to teach N = 70 second and third graders basic computational thinking concepts, i.e., basic directions and sequences. Students in the experimental condition used the LEGO® Education SPIKE™ Essential-Set and Scratch-based icon blocks software to navigate a maze, while the control group used regular LEGO®-Sets and verbal commands. Results supported our hypotheses: Students in the experimental condition outperformed those in the control condition, produced longer sequences and more sequence commands to direct their object through the maze, and a positive relationship was observed between sequence length and posttest performance. Furthermore, mediation analysis revealed that the effect of the intervention on posttest achievement was influenced by the use of longer sequences. Our study underpins that Scratch-based block programming combined with robot-like real-world devices is a promising way to utilize CT in (early) primary education—and deepens our knowledge about how students engage in relevant learning activities.
{"title":"Introducing computational thinking to second and third graders. Programming whole paths outperforms step-by-step navigation in maze tasks","authors":"Frank Reinhold , Priska Sprenger , Gunnar Staniczek","doi":"10.1016/j.caeo.2025.100260","DOIUrl":"10.1016/j.caeo.2025.100260","url":null,"abstract":"<div><div>Computational Thinking (CT) has been considered one of the 21st century skills that already primary school students should develop. In this study we compared different methods for guiding a real object through a training maze task to teach <em>N</em> = 70 second and third graders basic computational thinking concepts, i.e., basic directions and sequences. Students in the experimental condition used the LEGO® Education SPIKE™ Essential-Set and Scratch-based icon blocks software to navigate a maze, while the control group used regular LEGO®-Sets and verbal commands. Results supported our hypotheses: Students in the experimental condition outperformed those in the control condition, produced longer sequences and more sequence commands to direct their object through the maze, and a positive relationship was observed between sequence length and posttest performance. Furthermore, mediation analysis revealed that the effect of the intervention on posttest achievement was influenced by the use of longer sequences. Our study underpins that Scratch-based block programming combined with robot-like real-world devices is a promising way to utilize CT in (early) primary education—and deepens our knowledge about how students engage in relevant learning activities.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100260"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-12DOI: 10.1016/j.caeo.2025.100248
Nadia Catenazzi , Lorenzo Sommaruga , Kylene De Angelis , Sara Caboni
This paper proposes a comprehensive methodology to implement learning outcomes-based training and assessment, starting with the course planning, followed by the training delivery, and ending with the assessment and recognition of the achieved learning outcomes with the release of digital badges. The methodology consists of a number of steps organized in three main phases: curriculum development, micro-credentials and digital badge setting, training delivery and badge awarding. The methodology has been applied and refined in the context of Vocational Education and Training projects, enabling the creation of a learning outcomes-based modular curriculum, the creation of a constellation of digital badges based on the Open Badges standard, and the creation of an infrastructure for the release of digital badges. In the learning outcome journey, it represents a showcase of how intended and achieved learning outcomes can be aligned.
{"title":"A comprehensive methodology for curriculum development, training delivery and certification using learning outcomes and digital badges","authors":"Nadia Catenazzi , Lorenzo Sommaruga , Kylene De Angelis , Sara Caboni","doi":"10.1016/j.caeo.2025.100248","DOIUrl":"10.1016/j.caeo.2025.100248","url":null,"abstract":"<div><div>This paper proposes a comprehensive methodology to implement learning outcomes-based training and assessment, starting with the course planning, followed by the training delivery, and ending with the assessment and recognition of the achieved learning outcomes with the release of digital badges. The methodology consists of a number of steps organized in three main phases: curriculum development, micro-credentials and digital badge setting, training delivery and badge awarding. The methodology has been applied and refined in the context of Vocational Education and Training projects, enabling the creation of a learning outcomes-based modular curriculum, the creation of a constellation of digital badges based on the Open Badges standard, and the creation of an infrastructure for the release of digital badges. In the learning outcome journey, it represents a showcase of how intended and achieved learning outcomes can be aligned.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100248"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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: 2025-03-01DOI: 10.1016/j.caeo.2025.100247
Andreas Schulz, Johannes Voermanek
Students' help-seeking behavior plays a central role in successful learning with interactive learning environments (ILEs), such as intelligent tutoring systems that provide on-demand help, including step-by-step hints or strategic help for solving mathematics problems. However, learners can also abuse the help offered when trying to successfully complete an ILE by using the hints provided primarily to find the required solution with as little effort as possible, rather than using the hints to support their learning efforts. This type of help abuse by learners undermines the purpose of an ILE. The present study investigated the extent to which self-reported help-abuse of 322 student teachers mediates the effect of observed help-seeking on learning number conversion in an ILE. Further, we examined the moderating effects of prior knowledge and academic self-concept in mathematics (MSC) on the effects of help-seeking and help-abuse on learning. The results showed that increased help-seeking had a significant negative impact on learning achievement. However, this could only be observed for the use of step-by-step hints, but not for the use of strategic help. The extent of self-reported help-abuse largely mediated the negative influence of observed help-seeking on learning achievement. The study indicates that step-by-step hints in ILEs could be faded out in the learning process and that more emphasis should be placed on strategic help that encourages self-explanations.
{"title":"How do help-seeking and help-abuse affect learning achievement in an interactive learning environment?","authors":"Andreas Schulz, Johannes Voermanek","doi":"10.1016/j.caeo.2025.100247","DOIUrl":"10.1016/j.caeo.2025.100247","url":null,"abstract":"<div><div>Students' help-seeking behavior plays a central role in successful learning with interactive learning environments (ILEs), such as intelligent tutoring systems that provide on-demand help, including step-by-step hints or strategic help for solving mathematics problems. However, learners can also abuse the help offered when trying to successfully complete an ILE by using the hints provided primarily to find the required solution with as little effort as possible, rather than using the hints to support their learning efforts. This type of help abuse by learners undermines the purpose of an ILE. The present study investigated the extent to which self-reported help-abuse of 322 student teachers mediates the effect of observed help-seeking on learning number conversion in an ILE. Further, we examined the moderating effects of prior knowledge and academic self-concept in mathematics (MSC) on the effects of help-seeking and help-abuse on learning. The results showed that increased help-seeking had a significant negative impact on learning achievement. However, this could only be observed for the use of step-by-step hints, but not for the use of strategic help. The extent of self-reported help-abuse largely mediated the negative influence of observed help-seeking on learning achievement. The study indicates that step-by-step hints in ILEs could be faded out in the learning process and that more emphasis should be placed on strategic help that encourages self-explanations.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100247"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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: 2025-05-02DOI: 10.1016/j.caeo.2025.100258
Sung-Hee Jin
As Artificial Intelligence (AI) becomes increasingly integrated into educational environments, understanding the relationship between learners and AI systems is crucial for optimizing learning outcomes. This study introduces and validates the Learner-Generative AI Relationship Scale, a novel instrument designed to measure the multifaceted nature of learner-AI relationship in educational settings. The scale was developed through a rigorous process involving literature review, expert reviews, and cognitive pre-testing. An exploratory factor analysis with 95 undergraduate students confirmed a three-factor structure: Affective Intimacy, Cognitive Competence, and Social Flow, each comprising three sub-factors. The scale demonstrated good internal consistency and construct validity. To establish concurrent and predictive validity, 75 participants completed an argumentative essay writing task using ChatGPT. Concurrent validity was established through significant correlations with measures of attitude toward AI and AI self-efficacy. Predictive validity was confirmed through regression analyses, which showed that the learner-generative AI relationship significantly predicted learning engagement, perceived cognitive effects, and perceived motivational effects in a ChatGPT-assisted argumentative writing task. This study addresses a critical gap in the literature by providing a comprehensive tool for measuring learner-AI relationships beyond mere interactions and attitudes. The learner-generative AI relationship scale offers researchers and educators a valuable instrument for understanding and improving AI-driven educational systems, potentially informing the design of more effective AI-enhanced learning experiences.
{"title":"Measures of learner-generative ai relationships","authors":"Sung-Hee Jin","doi":"10.1016/j.caeo.2025.100258","DOIUrl":"10.1016/j.caeo.2025.100258","url":null,"abstract":"<div><div>As Artificial Intelligence (AI) becomes increasingly integrated into educational environments, understanding the relationship between learners and AI systems is crucial for optimizing learning outcomes. This study introduces and validates the Learner-Generative AI Relationship Scale, a novel instrument designed to measure the multifaceted nature of learner-AI relationship in educational settings. The scale was developed through a rigorous process involving literature review, expert reviews, and cognitive pre-testing. An exploratory factor analysis with 95 undergraduate students confirmed a three-factor structure: Affective Intimacy, Cognitive Competence, and Social Flow, each comprising three sub-factors. The scale demonstrated good internal consistency and construct validity. To establish concurrent and predictive validity, 75 participants completed an argumentative essay writing task using ChatGPT. Concurrent validity was established through significant correlations with measures of attitude toward AI and AI self-efficacy. Predictive validity was confirmed through regression analyses, which showed that the learner-generative AI relationship significantly predicted learning engagement, perceived cognitive effects, and perceived motivational effects in a ChatGPT-assisted argumentative writing task. This study addresses a critical gap in the literature by providing a comprehensive tool for measuring learner-AI relationships beyond mere interactions and attitudes. The learner-generative AI relationship scale offers researchers and educators a valuable instrument for understanding and improving AI-driven educational systems, potentially informing the design of more effective AI-enhanced learning experiences.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100258"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-01-22DOI: 10.1016/j.caeo.2025.100244
Dominik Petko , Punya Mishra , Matthew J Koehler
The Technological Pedagogical and Content Knowledge (TPACK) framework has evolved significantly since its introduction, particularly in its treatment of context. The original model acknowledged contexts through a dotted circle surrounding the framework's core components; however, understanding and operationalizing these contexts has remained challenging for researchers and practitioners. We address this challenge by proposing a new conceptual synthesis that bridges two prominent approaches to context in TPACK: contexts as external influences and Contextual Knowledge (XK) as a distinct knowledge domain. We argue that both perspectives are essential and complementary. Drawing on research from cognitive psychology, teacher expertise, and situated learning, we argue that TPACK exists simultaneously as teacher knowledge that is shaped by external contexts (contextualized knowledge) and as knowledge about educational environments (Contextual Knowledge). This dual nature is reflected in our proposed model, which maintains the original framework's dotted circle representing external contexts while incorporating XK as an additional knowledge domain. This reconceptualization provides theoretical clarity and practical utility for understanding how teachers develop and apply their technology integration knowledge across different educational settings.
{"title":"TPACK in context: An updated model","authors":"Dominik Petko , Punya Mishra , Matthew J Koehler","doi":"10.1016/j.caeo.2025.100244","DOIUrl":"10.1016/j.caeo.2025.100244","url":null,"abstract":"<div><div>The Technological Pedagogical and Content Knowledge (TPACK) framework has evolved significantly since its introduction, particularly in its treatment of context. The original model acknowledged contexts through a dotted circle surrounding the framework's core components; however, understanding and operationalizing these contexts has remained challenging for researchers and practitioners. We address this challenge by proposing a new conceptual synthesis that bridges two prominent approaches to context in TPACK: contexts as external influences and Contextual Knowledge (XK) as a distinct knowledge domain. We argue that both perspectives are essential and complementary. Drawing on research from cognitive psychology, teacher expertise, and situated learning, we argue that TPACK exists simultaneously as teacher knowledge that is shaped by external contexts (contextualized knowledge) and as knowledge about educational environments (Contextual Knowledge). This dual nature is reflected in our proposed model, which maintains the original framework's dotted circle representing external contexts while incorporating XK as an additional knowledge domain. This reconceptualization provides theoretical clarity and practical utility for understanding how teachers develop and apply their technology integration knowledge across different educational settings.</div></div>","PeriodicalId":100322,"journal":{"name":"Computers and Education Open","volume":"8 ","pages":"Article 100244"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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