As artificial intelligence (AI) and generative AI (GenAI) technologies become increasingly integrated into everyday life, the need for validated tools that measure people's knowledge about AI grows. Here, we present the development and validation of a theoretically driven, performance-based scale for assessing AI and GenAI knowledge. The scale is grounded in a two-axial framework. One axis captures three knowledge types: content knowledge (what AI is and where it is encountered), procedural knowledge (how AI systems operate and are used), and epistemic knowledge (what features and construction processes characterize AI outputs). The other axis encompasses three knowledge domains: technology-related knowledge (AI systems), user-related knowledge (users' interaction with AI), and society-related knowledge (the social and ethical implications of AI). Based on an online survey of 800 internet-using adults from Israel, the 26-item scale was evaluated using confirmatory factor analysis, which demonstrated an acceptable model fit. It was further validated through two-stage structural equation modeling and group comparisons. Overall, the scale was found to be both valid and practically insightful: while it reproduces the expected relationships with additional constructs (e.g., trust in GenAI, attitudes toward AI) and expected differences between demographic groups, it also provides nuanced insights on the intricacies of AI knowledge. For example, the scale indicates that the relationship between trust in GenAI and knowledge about AI is grounded in both epistemic and societal knowledge. Thus, this novel tool affords more precise investigations into how different types and domains of AI knowledge relate to perceptions, behaviors, and decision-making in an AI-mediated world.
{"title":"Measuring different types and domains of AI knowledge: Developing and validating a performance-based scale","authors":"Inbal Klein-Avraham , Rut Ston , Osnat Atias , Ido Roll , Ayelet Baram-Tsabari","doi":"10.1016/j.compedu.2026.105573","DOIUrl":"10.1016/j.compedu.2026.105573","url":null,"abstract":"<div><div>As artificial intelligence (AI) and generative AI (GenAI) technologies become increasingly integrated into everyday life, the need for validated tools that measure people's knowledge about AI grows. Here, we present the development and validation of a theoretically driven, performance-based scale for assessing AI and GenAI knowledge. The scale is grounded in a two-axial framework. One axis captures three knowledge types: content knowledge (what AI is and where it is encountered), procedural knowledge (how AI systems operate and are used), and epistemic knowledge (what features and construction processes characterize AI outputs). The other axis encompasses three knowledge domains: technology-related knowledge (AI systems), user-related knowledge (users' interaction with AI), and society-related knowledge (the social and ethical implications of AI). Based on an online survey of 800 internet-using adults from Israel, the 26-item scale was evaluated using confirmatory factor analysis, which demonstrated an acceptable model fit. It was further validated through two-stage structural equation modeling and group comparisons. Overall, the scale was found to be both valid and practically insightful: while it reproduces the expected relationships with additional constructs (e.g., trust in GenAI, attitudes toward AI) and expected differences between demographic groups, it also provides nuanced insights on the intricacies of AI knowledge. For example, the scale indicates that the relationship between trust in GenAI and knowledge about AI is grounded in both epistemic and societal knowledge. Thus, this novel tool affords more precise investigations into how different types and domains of AI knowledge relate to perceptions, behaviors, and decision-making in an AI-mediated world.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105573"},"PeriodicalIF":10.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957052","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 : 2026-01-12DOI: 10.1016/j.compedu.2025.105556
Chia-Mei Lu
Scientific reasoning in simulation-based learning environments (SBLEs) is a time-structured process, not a terminal outcome. We advance and test a trait → mechanism → behavior model explaining how self-regulated learning (SRL) and critical thinking disposition (CTD) become consequential via a translation layer: dispositions first shape a reasoning-aligned configuration mechanism (SIM; planned contrasts, disciplined retesting), which enables process-level behavior (SIB; semantic precision, revision cadence). Eleventh graders (N = 168) completed a closed-loop aquatic simulation under Guided→Open or Open→Open sequences. Consistent Partial Least Squares for Reflective Constructs(PLSc)estimated reflective blocks; SIB was formative (Mode B). Measurement quality and cross-condition invariance were established; Cluster-Robust Variance Estimator, Type 2 (CR2), and PLSpredict supported stability and utility. Process analytics (K-means profiles; three-state HMM) complemented the SEM. Findings: SRL and CTD had moderate positive effects on SIM; SIM had a vast, robust effect on SIB. Mediation showed trait effects reach behavior primarily through SIM. Format moderation was small/uncertain; temporally, lower-SRL learners dwelled longer in low-efficiency states, and editing cadence marked transitions to efficiency; early scaffolds modestly shortened dwell. Design principles: instrument platforms to monitor SIM/SIB as live control points; route support by profiles; and time-minimal, load-aware prompts to the revision window to restore Control of Variables Strategy (CVS) discipline and semantic alignment. The contribution is a validated, mechanism-aware account that yields diagnostic, feedback-ready, and scalable specifications for precision scaffolding and evaluation in SBLEs.
{"title":"Tracing scientific reasoning as process: A trait-behavior-performance model with learning analytics in simulated environments","authors":"Chia-Mei Lu","doi":"10.1016/j.compedu.2025.105556","DOIUrl":"10.1016/j.compedu.2025.105556","url":null,"abstract":"<div><div>Scientific reasoning in simulation-based learning environments (SBLEs) is a time-structured process, not a terminal outcome. We advance and test a trait → mechanism → behavior model explaining how self-regulated learning (SRL) and critical thinking disposition (CTD) become consequential via a translation layer: dispositions first shape a reasoning-aligned configuration mechanism (SIM; planned contrasts, disciplined retesting), which enables process-level behavior (SIB; semantic precision, revision cadence). Eleventh graders (N = 168) completed a closed-loop aquatic simulation under Guided→Open or Open→Open sequences. Consistent Partial Least Squares for Reflective Constructs(PLSc)estimated reflective blocks; SIB was formative (Mode B). Measurement quality and cross-condition invariance were established; Cluster-Robust Variance Estimator, Type 2 (CR2), and PLSpredict supported stability and utility. Process analytics (K-means profiles; three-state HMM) complemented the SEM. Findings: SRL and CTD had moderate positive effects on SIM; SIM had a vast, robust effect on SIB. Mediation showed trait effects reach behavior primarily through SIM. Format moderation was small/uncertain; temporally, lower-SRL learners dwelled longer in low-efficiency states, and editing cadence marked transitions to efficiency; early scaffolds modestly shortened dwell. Design principles: instrument platforms to monitor SIM/SIB as live control points; route support by profiles; and time-minimal, load-aware prompts to the revision window to restore Control of Variables Strategy (CVS) discipline and semantic alignment. The contribution is a validated, mechanism-aware account that yields diagnostic, feedback-ready, and scalable specifications for precision scaffolding and evaluation in SBLEs.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"246 ","pages":"Article 105556"},"PeriodicalIF":10.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957053","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 : 2026-01-08DOI: 10.1016/j.compedu.2026.105564
Yi-Chen Juan , Yuan-Hsuan Lee , Jiun-Yu Wu
Generative Artificial Intelligence (GenAI) functions not merely as a tool but an active collaborator in human knowledge construction; however, the Human-GenAI interaction dynamics is still underexplored. This study investigates Human-GenAI interaction profiles, the network interactivity and profile differences within a statistics learning community, as well as the underlying mechanisms linking Human-GenAI interaction to learning performance. We designed the Human–GenAI Inquiry and Problem-Solving Scaffold to foster shared agency between twenty-eight graduate students and GenAI across seven homework assignments in a sixteen-week advanced statistics course. Analytical approaches included k-modes clustering, social network analysis, and Partial Least Squares Structural Equation Modeling, complemented by case studies of interaction profiles. Three distinct Human-GenAI interaction profiles were identified: Human-GenAI collaborators, Peer collaborators with GenAI assistance, and Individual learners with late GenAI adoption. The network interactivity becomes cohesive with GenAI occupying the central hub role within the learning community. The models then demonstrate unique pathways through which Human-GenAI interaction influences learning performance, via degree centrality (number of direct connections) and peer nomination as helpers. The case studies highlight GenAI’s capability to augment human roles, encouraging deeper inquiry, expanding the depth of peer discussion, or promoting the exploration of diverse problem-solving strategies. These findings add value to theory and practice by providing empirical evidence for the framework of a scaffolded Human-GenAI shared agency, offering pedagogical implications to foster active student participation and cultivate learner agency within the symbiotic Human–GenAI partnership.
{"title":"Generative artificial intelligence augments social interactivity and learning outcomes: Advancing the framework of a scaffolded human–GenAI shared agency","authors":"Yi-Chen Juan , Yuan-Hsuan Lee , Jiun-Yu Wu","doi":"10.1016/j.compedu.2026.105564","DOIUrl":"10.1016/j.compedu.2026.105564","url":null,"abstract":"<div><div>Generative Artificial Intelligence (GenAI) functions not merely as a tool but an active collaborator in human knowledge construction; however, the Human-GenAI interaction dynamics is still underexplored. This study investigates Human-GenAI interaction profiles, the network interactivity and profile differences within a statistics learning community, as well as the underlying mechanisms linking Human-GenAI interaction to learning performance. We designed the Human–GenAI Inquiry and Problem-Solving Scaffold to foster shared agency between twenty-eight graduate students and GenAI across seven homework assignments in a sixteen-week advanced statistics course. Analytical approaches included <em>k</em>-modes clustering, social network analysis, and Partial Least Squares Structural Equation Modeling, complemented by case studies of interaction profiles. Three distinct Human-GenAI interaction profiles were identified: Human-GenAI collaborators, Peer collaborators with GenAI assistance, and Individual learners with late GenAI adoption. The network interactivity becomes cohesive with GenAI occupying the central hub role within the learning community. The models then demonstrate unique pathways through which Human-GenAI interaction influences learning performance, via degree centrality (number of direct connections) and peer nomination as helpers. The case studies highlight GenAI’s capability to augment human roles, encouraging deeper inquiry, expanding the depth of peer discussion, or promoting the exploration of diverse problem-solving strategies. These findings add value to theory and practice by providing empirical evidence for the framework of a scaffolded Human-GenAI shared agency, offering pedagogical implications to foster active student participation and cultivate learner agency within the symbiotic Human–GenAI partnership.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105564"},"PeriodicalIF":10.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957068","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}
As AI technologies permeate daily life, adolescents’ distinctive cognitive profiles make their decision-making highly sensitive to AI explanation features. The study aimed to examine the underlying mechanisms by which AI’s explanatory features and time frame impact adolescents’ decision-making. We created an online platform where adolescents interacted with an explainable AI. A preliminary survey identified 10 mathematics-related factors. Experiment 1 involved 158 students (Mage = 13.7) and used a 3 (explanation type: prediction, causal, counterfactual) × 2 (perceived control: high, low) × 2 (perceived reliability: reliable, unreliable) mixed design. Experiment 2 recruited 225 students (Mage = 13.7) and employed a 3 (explanation type) × 2 (time frame: short-term, long-term) mixed design. Decision-making and expectation (expected impact of each factor on math achievement) were the outcomes in both experiments. In Experiment 1, perceived unreliable counterfactual explanations for low-control factors produced the lowest expectation and decision-making probability, whereas predictions and causal explanations did not differ. For high-control factors, perceived reliable counterfactual explanations similarly reduced decision-making probability, although expectation remained constant across explanations. In Experiment 2, predictions and causal explanations led to higher decision-making probability for short-term events than long-term ones, while counterfactuals reversed this pattern. While counterfactual explanations help restore trust and motivate change in distant, uncertain contexts, they can trigger reactance and reduce action when events feel controllable or imminent. Although adolescents cognitively understand causality and time frames, they still struggle to effectively regulate their decisions. AI model explanations should therefore account for the developmental characteristics of adolescents and recognize the dual effects inherent in counterfactual explanations.
{"title":"How explanatory features of AI and time frame reshape adolescents’ decision-making","authors":"Zhuo Shen, Yinghe Chen, Jingyi Zhang, Hengrun Chen","doi":"10.1016/j.compedu.2026.105563","DOIUrl":"https://doi.org/10.1016/j.compedu.2026.105563","url":null,"abstract":"As AI technologies permeate daily life, adolescents’ distinctive cognitive profiles make their decision-making highly sensitive to AI explanation features. The study aimed to examine the underlying mechanisms by which AI’s explanatory features and time frame impact adolescents’ decision-making. We created an online platform where adolescents interacted with an explainable AI. A preliminary survey identified 10 mathematics-related factors. Experiment 1 involved 158 students (<ce:italic>M</ce:italic><ce:inf loc=\"post\">age</ce:inf> = 13.7) and used a 3 (explanation type: prediction, causal, counterfactual) × 2 (perceived control: high, low) × 2 (perceived reliability: reliable, unreliable) mixed design. Experiment 2 recruited 225 students (<ce:italic>M</ce:italic><ce:inf loc=\"post\">age</ce:inf> = 13.7) and employed a 3 (explanation type) × 2 (time frame: short-term, long-term) mixed design. Decision-making and expectation (expected impact of each factor on math achievement) were the outcomes in both experiments. In Experiment 1, perceived unreliable counterfactual explanations for low-control factors produced the lowest expectation and decision-making probability, whereas predictions and causal explanations did not differ. For high-control factors, perceived reliable counterfactual explanations similarly reduced decision-making probability, although expectation remained constant across explanations. In Experiment 2, predictions and causal explanations led to higher decision-making probability for short-term events than long-term ones, while counterfactuals reversed this pattern. While counterfactual explanations help restore trust and motivate change in distant, uncertain contexts, they can trigger reactance and reduce action when events feel controllable or imminent. Although adolescents cognitively understand causality and time frames, they still struggle to effectively regulate their decisions. AI model explanations should therefore account for the developmental characteristics of adolescents and recognize the dual effects inherent in counterfactual explanations.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"7 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957069","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 : 2025-12-30DOI: 10.1016/j.compedu.2025.105555
Tinghui Wu , Xuesong Zhai , Yanjie Song
This study examines the effects of generative artificial intelligence (GAI)-enhanced pedagogical agents in the metaverse (GPAiM) on elementary school students' conceptual understanding of traditional festival customs and on the students' cognitive engagement patterns. The participants included 116 students from three intact classes. These classes were randomly assigned to two experimental groups (with 2D-GPAiM and 3D-GPAiM, respectively) and one control group (without GPAiM but with a real-person teacher). All the participants learned in the metaverse, and students in different groups were allowed to interact with 2D-GPAiM, 3D-GPAiM, and the real-person teacher during their learning, respectively. This study was conducted under a three-week AI literacy project with the learning topic of traditional festival customs. The results showed that the experimental groups (both 2D-GPAiM and 3D-GPAiM) had a positive impact on the students' conceptual understanding of traditional festivals, while the control group did not. More importantly, the 2D-GPAiM group showed a significantly positive difference in the participants’ conceptual understanding compared with the control group. In addition, regarding cognitive engagement, the 2D-GPAiM group showed a highly interactive, low-fluctuating, and high-level cognitive engagement pattern; The 3D-GPAiM group demonstrated a highly interactive, highly fluctuating, medium-level cognitive engagement pattern, while the control group exhibited a low-interactive, low-fluctuating, low-level cognitive engagement pattern. These findings provide valuable insights into future GAI-assisted pedagogical designs.
{"title":"The effects of GAI-enhanced pedagogical agents in the metaverse (GPAiM) on elementary school students’ conceptual understanding and cognitive engagement patterns","authors":"Tinghui Wu , Xuesong Zhai , Yanjie Song","doi":"10.1016/j.compedu.2025.105555","DOIUrl":"10.1016/j.compedu.2025.105555","url":null,"abstract":"<div><div>This study examines the effects of generative artificial intelligence (GAI)-enhanced pedagogical agents in the metaverse (GPAiM) on elementary school students' conceptual understanding of traditional festival customs and on the students' cognitive engagement patterns. The participants included 116 students from three intact classes. These classes were randomly assigned to two experimental groups (with 2D-GPAiM and 3D-GPAiM, respectively) and one control group (without GPAiM but with a real-person teacher). All the participants learned in the metaverse, and students in different groups were allowed to interact with 2D-GPAiM, 3D-GPAiM, and the real-person teacher during their learning, respectively. This study was conducted under a three-week AI literacy project with the learning topic of traditional festival customs. The results showed that the experimental groups (both 2D-GPAiM and 3D-GPAiM) had a positive impact on the students' conceptual understanding of traditional festivals, while the control group did not. More importantly, the 2D-GPAiM group showed a significantly positive difference in the participants’ conceptual understanding compared with the control group. In addition, regarding cognitive engagement, the 2D-GPAiM group showed a <em>highly interactive, low-fluctuating, and high-level cognitive engagement</em> pattern; The 3D-GPAiM group demonstrated a <em>highly interactive, highly fluctuating, medium-level cognitive engagement</em> pattern, while the control group exhibited a <em>low-interactive, low-fluctuating, low-level cognitive engagement pattern</em>. These findings provide valuable insights into future GAI-assisted pedagogical designs.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"245 ","pages":"Article 105555"},"PeriodicalIF":10.5,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882238","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 : 2025-12-27DOI: 10.1016/j.compedu.2025.105552
Siyu Wu , Xiaodan Yu , Wei Wei
Parents can mitigate screens’ negative effects on school-aged children and adolescents by monitoring their screen use and improving their screen use skills. However, many preschoolers spend more time on screens than recommended. It remains unclear whether parental mediation is associated with preschoolers’ development through children’s screen time, and whether the first part of this pathway is moderated by problematic parental screen use. This analysis utilized parent-reported data collected in June 2019 about 57,827 children aged 4–5 years. Data included children’s developmental outcomes, children’s screen time, parental mediation (restrictive mediation, instructive mediation, and co-use) frequency, problematic parental screen use level, family income, and parental education. A significant negative correlation was found between children’s screen time and developmental outcomes (r = −0.07, 95 % confidence interval (CI) = [−0.08, −0.06]). Children’s screen time mediated the relations between parental mediation strategies and developmental outcomes. Restrictive mediation frequency was positively associated with developmental outcomes through children’s screen time (β = 0.016, 95 % CI = [0.013, 0.018]). Instructive mediation (β = −0.005, 95 % CI = [−0.006, −0.005]) and co-use (β = −0.004, 95 % CI = [−0.005, −0.003]) were indirectly, negatively associated with developmental outcomes through children’s screen time. Problematic parental screen use moderated the relations between parental mediation and children’s screen time. Higher problematic parental screen use strengthened restrictive mediation’s negative (β = −0.023, 95 % CI = [−0.032, −0.011]) and instructive mediation’s positive (β = 0.047, 95 % CI = [0.037, 0.057]) effects. Despite the modest effect sizes, the statistically robust results suggest that population-level adoption of combined parental strategies—reducing problematic parental screen use alongside implementing restrictive mediation—could translate into public health benefits for early childhood development.
家长可以通过监测学龄儿童和青少年的屏幕使用情况和提高他们的屏幕使用技能来减轻屏幕对他们的负面影响。然而，许多学龄前儿童花在屏幕上的时间超过了建议的时间。目前尚不清楚父母的调解是否通过儿童的屏幕时间与学龄前儿童的发展有关，以及这一途径的第一部分是否被有问题的父母屏幕使用所缓和。该分析利用了2019年6月收集的约57,827名4-5岁儿童的家长报告数据。数据包括儿童发育结果、儿童屏幕时间、父母干预（限制性干预、指导性干预和共同使用）频率、父母有问题的屏幕使用水平、家庭收入和父母受教育程度。儿童屏幕时间与发育结果呈显著负相关（r = - 0.07, 95%可信区间(CI) =[- 0.08, - 0.06]）。儿童屏幕时间在父母调解策略与发展结果之间起中介作用。限制性中介频率通过儿童屏幕时间与发育结果呈正相关（β = 0.016, 95% CI =[0.013, 0.018]）。指导性中介（β = - 0.005, 95% CI =[- 0.006, - 0.005]）和共同使用（β = - 0.004, 95% CI =[- 0.005, - 0.003]）与儿童屏幕时间的发展结果呈间接负相关。有问题的父母屏幕使用调节了父母调解与儿童屏幕时间之间的关系。较高的问题父母筛选率强化了限制性中介的负作用（β = - 0.023, 95% CI =[- 0.032, - 0.011]）和指导性中介的正作用（β = 0.047, 95% CI =[0.037, 0.057]）。尽管效果不大，但统计结果表明，在人口水平上采用联合父母策略——减少有问题的父母屏幕使用，同时实施限制性调解——可以转化为儿童早期发展的公共卫生效益。
{"title":"The indirect role of children’s screen time and the moderating role of problematic parental screen use on the relationships between different parental mediation strategies and preschoolers’ developmental outcomes","authors":"Siyu Wu , Xiaodan Yu , Wei Wei","doi":"10.1016/j.compedu.2025.105552","DOIUrl":"10.1016/j.compedu.2025.105552","url":null,"abstract":"<div><div>Parents can mitigate screens’ negative effects on school-aged children and adolescents by monitoring their screen use and improving their screen use skills. However, many preschoolers spend more time on screens than recommended. It remains unclear whether parental mediation is associated with preschoolers’ development through children’s screen time, and whether the first part of this pathway is moderated by problematic parental screen use. This analysis utilized parent-reported data collected in June 2019 about 57,827 children aged 4–5 years. Data included children’s developmental outcomes, children’s screen time, parental mediation (restrictive mediation, instructive mediation, and co-use) frequency, problematic parental screen use level, family income, and parental education. A significant negative correlation was found between children’s screen time and developmental outcomes (<em>r</em> = −0.07, 95 % confidence interval (CI) = [−0.08, −0.06]). Children’s screen time mediated the relations between parental mediation strategies and developmental outcomes. Restrictive mediation frequency was positively associated with developmental outcomes through children’s screen time (β = 0.016, 95 % CI = [0.013, 0.018]). Instructive mediation (β = −0.005, 95 % CI = [−0.006, −0.005]) and co-use (β = −0.004, 95 % CI = [−0.005, −0.003]) were indirectly, negatively associated with developmental outcomes through children’s screen time. Problematic parental screen use moderated the relations between parental mediation and children’s screen time. Higher problematic parental screen use strengthened restrictive mediation’s negative (β = −0.023, 95 % CI = [−0.032, −0.011]) and instructive mediation’s positive (β = 0.047, 95 % CI = [0.037, 0.057]) effects. Despite the modest effect sizes, the statistically robust results suggest that population-level adoption of combined parental strategies—reducing problematic parental screen use alongside implementing restrictive mediation—could translate into public health benefits for early childhood development.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"245 ","pages":"Article 105552"},"PeriodicalIF":10.5,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845117","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 : 2025-12-26DOI: 10.1016/j.compedu.2025.105554
Elly Cheng Wang, Xin Guan, Xiaolong Chen, Tai Kai Ng
This study investigates the use of machine learning to detect mindset states — concentration, motivation, perseverance, engagement, and self-initiative among secondary school students in Hong Kong during two real-life online courses. Prior research has explored AI for mindset detection in controlled settings. Addressing real-life challenges such as low data quality, lighting variability, movement, and privacy concerns, this study explores the feasibility of detecting mindset states in a real-life environment with a combination of inputs, including quiz scores, facial expression, and categorized learning behavior logs. Using a Recurrent Neural Network (RNN), we achieved a modest yet significant prediction accuracy, even with a small dataset of approximately one hundred students. In particular, our results demonstrate the potential of logging data as a scalable and privacy-preserving approach for understanding students’ psychological states. We caution that while our results are encouraging, the modest accuracy highlights the need for further optimization before the approach can be reliably applied in real-world educational settings.
From a pedagogical perspective, our findings suggest that real-time feedback from well-trained AI tool may provide useful information about students’ mindset states which educators can use to create better student-centered adaptive teaching practices that promote personalized learning while addressing ethical considerations such as data privacy and accessibility.
{"title":"Assessing mindset states of Hong Kong secondary students using machine learning in real-world online learning environment","authors":"Elly Cheng Wang, Xin Guan, Xiaolong Chen, Tai Kai Ng","doi":"10.1016/j.compedu.2025.105554","DOIUrl":"10.1016/j.compedu.2025.105554","url":null,"abstract":"<div><div>This study investigates the use of machine learning to detect mindset states — concentration, motivation, perseverance, engagement, and self-initiative among secondary school students in Hong Kong during two real-life online courses. Prior research has explored AI for mindset detection in controlled settings. Addressing real-life challenges such as low data quality, lighting variability, movement, and privacy concerns, this study explores the feasibility of detecting mindset states in a real-life environment with a combination of inputs, including quiz scores, facial expression, and categorized learning behavior logs. Using a Recurrent Neural Network (RNN), we achieved a modest yet significant prediction accuracy, even with a small dataset of approximately one hundred students. In particular, our results demonstrate the potential of logging data as a scalable and privacy-preserving approach for understanding students’ psychological states. We caution that while our results are encouraging, the modest accuracy highlights the need for further optimization before the approach can be reliably applied in real-world educational settings.</div><div>From a pedagogical perspective, our findings suggest that real-time feedback from well-trained AI tool may provide useful information about students’ mindset states which educators can use to create better student-centered adaptive teaching practices that promote personalized learning while addressing ethical considerations such as data privacy and accessibility.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"245 ","pages":"Article 105554"},"PeriodicalIF":10.5,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839428","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 : 2025-12-24DOI: 10.1016/j.compedu.2025.105551
Jiabin Liu , Ru-De Liu , Wei Hong , Jingmin Lin
Academic procrastination is a prevalent and varies considerably among adolescents. However, little is known about the specific patterns of academic procrastination development and how the family's digital environment influences these developmental patterns. We conducted a three-wave longitudinal survey with 1130 Chinese adolescents (47.10 % males, Mage = 13.59 years, SD = 2.14 at T1) to identify distinct procrastination trajectories and examine how student and parental mobile phone use (MPU) predicts trajectories. The latent class growth analysis revealed four distinct trajectories: high-stable (47.40 %), low-increasing (14.10 %), moderate-stable (22.20 %), and low-stable groups (16.30 %). Machine learning analysis demonstrated that students' mobile phone dependency and escape motivation predicted membership in less adaptive trajectories (i.e., high-stable, moderate-stable, and low-increasing groups). For the specific purposes of MPU, using for seeking life info, online courses learning, and playing games predicted membership in the low-stable group; while chatting with net friends predicted membership in the low-increasing group. Notably, parental phubbing also predicted membership in these less adaptive groups, whereas active parental mediation predicted membership in the low-stable group. These findings provide the first empirical evidence for the heterogeneous development of academic procrastination and highlight the important role of the family digital ecosystem in shaping these trajectories. Practically, the study supports the development of targeted and family-based interventions tailored to specific procrastination patterns.
{"title":"Differential effects of student and parental mobile phone use on academic procrastination trajectories: Machine learning evidence","authors":"Jiabin Liu , Ru-De Liu , Wei Hong , Jingmin Lin","doi":"10.1016/j.compedu.2025.105551","DOIUrl":"10.1016/j.compedu.2025.105551","url":null,"abstract":"<div><div>Academic procrastination is a prevalent and varies considerably among adolescents. However, little is known about the specific patterns of academic procrastination development and how the family's digital environment influences these developmental patterns. We conducted a three-wave longitudinal survey with 1130 Chinese adolescents (47.10 % males, <em>M</em><sub>age</sub> = 13.59 years, <em>SD</em> = 2.14 at T1) to identify distinct procrastination trajectories and examine how student and parental mobile phone use (MPU) predicts trajectories. The latent class growth analysis revealed four distinct trajectories: high-stable (47.40 %), low-increasing (14.10 %), moderate-stable (22.20 %), and low-stable groups (16.30 %). Machine learning analysis demonstrated that students' mobile phone dependency and escape motivation predicted membership in less adaptive trajectories (i.e., high-stable, moderate-stable, and low-increasing groups). For the specific purposes of MPU, using for seeking life info, online courses learning, and playing games predicted membership in the low-stable group; while chatting with net friends predicted membership in the low-increasing group. Notably, parental phubbing also predicted membership in these less adaptive groups, whereas active parental mediation predicted membership in the low-stable group. These findings provide the first empirical evidence for the heterogeneous development of academic procrastination and highlight the important role of the family digital ecosystem in shaping these trajectories. Practically, the study supports the development of targeted and family-based interventions tailored to specific procrastination patterns.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"245 ","pages":"Article 105551"},"PeriodicalIF":10.5,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823138","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 : 2025-12-19DOI: 10.1016/j.compedu.2025.105540
Mehmet Avcı
The swift advancement of technology in educational contexts introduces a range of challenges alongside significant benefits. Technostress and techno-eustress have been identified as significant determinants of academic achievement in higher education, influencing students’ well-being, productivity, and levels of burnout. However, there is limited research on how psychological mechanisms play a mediating role in the relationship between technostress, techno-eustress, and academic burnout. To fill this gap, two serial mediation models with three mediators were proposed to explore the associations among technostress, techno-eustress, digital literacy, internet self-efficacy, cognitive flexibility, and academic burnout in a random sample of university students from different education faculties in Türkiye (N = 677). The total effect of technostress on academic burnout was significant. This first model, with an additional three mediators, accounted for 16 % of the explained variance in academic burnout, and then 5 % without mediators. In the second model, the total effect of techno-eustress on academic burnout was nonsignificant (p = 0.602), while the direct effect had a small impact (p = 0.029). However, the total indirect effect of techno-eustress on academic burnout was significant and serially mediated by digital literacy, internet self-efficacy, and cognitive flexibility, accounting for 13 % of the explained variance. Triple serial mediation analyses indicated that digital literacy enhances internet self-efficacy, which in turn improves cognitive flexibility. This sequence ultimately promotes techno-eustress and mitigates technostress, resulting in a reduction of academic burnout. Focusing on these mediators as protective resources against technostress may enhance psychological and behavioral outcomes in higher education students.
{"title":"The double-edged sword of technology: Investigating technostress and techno-eustress in academic burnout through digital literacy, internet self-efficacy, and cognitive flexibility","authors":"Mehmet Avcı","doi":"10.1016/j.compedu.2025.105540","DOIUrl":"10.1016/j.compedu.2025.105540","url":null,"abstract":"<div><div>The swift advancement of technology in educational contexts introduces a range of challenges alongside significant benefits. Technostress and techno-eustress have been identified as significant determinants of academic achievement in higher education, influencing students’ well-being, productivity, and levels of burnout. However, there is limited research on how psychological mechanisms play a mediating role in the relationship between technostress, techno-eustress, and academic burnout. To fill this gap, two serial mediation models with three mediators were proposed to explore the associations among technostress, techno-eustress, digital literacy, internet self-efficacy, cognitive flexibility, and academic burnout in a random sample of university students from different education faculties in Türkiye (<em>N</em> = 677). The total effect of technostress on academic burnout was significant. This first model, with an additional three mediators, accounted for 16 % of the explained variance in academic burnout, and then 5 % without mediators. In the second model, the total effect of techno-eustress on academic burnout was nonsignificant (p = 0.602), while the direct effect had a small impact (p = 0.029). However, the total indirect effect of techno-eustress on academic burnout was significant and serially mediated by digital literacy, internet self-efficacy, and cognitive flexibility, accounting for 13 % of the explained variance. Triple serial mediation analyses indicated that digital literacy enhances internet self-efficacy, which in turn improves cognitive flexibility. This sequence ultimately promotes techno-eustress and mitigates technostress, resulting in a reduction of academic burnout. Focusing on these mediators as protective resources against technostress may enhance psychological and behavioral outcomes in higher education students.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"245 ","pages":"Article 105540"},"PeriodicalIF":10.5,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784992","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 : 2025-12-19DOI: 10.1016/j.compedu.2025.105550
Yang An, Steven W. Su
Current educational practices often fall short in delivering personalized instruction, accurately assessing teaching effectiveness, and fostering interactivity in educational settings. Integrating Brain–Computer Interface (BCI) technology with the BOPPPS (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, and Summary) teaching model may offer a promising solution to these challenges, yet remains largely unexplored. This study employed a quasi-experimental design involving 24 undergraduate students. The experimental group utilized a BCI-enhanced Scripted Performance-based BOPPPS model (SP-BOPPPS), while the control group followed the traditional BOPPPS model. The instructional approach was examined using electroencephalogram (EEG) data, classroom tests, and project completion evaluations. Preliminary results suggested that students using the SP-BOPPPS model appeared to show higher test scores and project completion outcomes than those using the traditional BOPPPS model, although these observations were made across different but closely related course topics. EEG analysis from this small exploratory sub-sample of four participants suggested preliminary trends toward higher attention levels and more positive emotional states under the SP-BOPPPS model. These qualitative observations may indicate that BCI technology helps provide real-time information about students’ cognitive and emotional states, potentially supporting more personalized instructional adjustments. Taken together, these tentative findings suggest that integrating BCI into traditional teaching models may offer a promising direction for supporting student engagement, and the study provides early empirical indications and a potential framework for applying BCI-based technologies in educational contexts. Future research with larger and more diverse student samples, as well as fully randomized controlled designs using identical instructional content, will be essential for assessing the robustness and generalizability of these preliminary findings.
{"title":"Brain–Computer Interface driven BOPPPS: Empirical evidence for enhanced educational practices","authors":"Yang An, Steven W. Su","doi":"10.1016/j.compedu.2025.105550","DOIUrl":"10.1016/j.compedu.2025.105550","url":null,"abstract":"<div><div>Current educational practices often fall short in delivering personalized instruction, accurately assessing teaching effectiveness, and fostering interactivity in educational settings. Integrating Brain–Computer Interface (BCI) technology with the BOPPPS (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, and Summary) teaching model may offer a promising solution to these challenges, yet remains largely unexplored. This study employed a quasi-experimental design involving 24 undergraduate students. The experimental group utilized a BCI-enhanced Scripted Performance-based BOPPPS model (SP-BOPPPS), while the control group followed the traditional BOPPPS model. The instructional approach was examined using electroencephalogram (EEG) data, classroom tests, and project completion evaluations. Preliminary results suggested that students using the SP-BOPPPS model appeared to show higher test scores and project completion outcomes than those using the traditional BOPPPS model, although these observations were made across different but closely related course topics. EEG analysis from this small exploratory sub-sample of four participants suggested preliminary trends toward higher attention levels and more positive emotional states under the SP-BOPPPS model. These qualitative observations may indicate that BCI technology helps provide real-time information about students’ cognitive and emotional states, potentially supporting more personalized instructional adjustments. Taken together, these tentative findings suggest that integrating BCI into traditional teaching models may offer a promising direction for supporting student engagement, and the study provides early empirical indications and a potential framework for applying BCI-based technologies in educational contexts. Future research with larger and more diverse student samples, as well as fully randomized controlled designs using identical instructional content, will be essential for assessing the robustness and generalizability of these preliminary findings.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"244 ","pages":"Article 105550"},"PeriodicalIF":10.5,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784991","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}
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