ETR&D

Promoting students’ collaborative discussions has consistently been a focal topic in the field of computer-supported collaborative learning. Productive collaborative discussions rarely happen spontaneously without external support, and student groups usually encounter challenges in developing a high-quality collaborative knowledge construction. To address this gap, this research designed a group knowledge awareness (GKA) tool by using knowledge graph approach to promote collaborative discussions in China’s higher education. A within-subject design research was conducted to investigate the effects of the GKA tool on groups’ collaborative knowledge construction. The findings revealed that the GKA tool had positive effects on collaborative knowledge construction, students’ domain understanding, and collaborative cognitive load. In addition, students reported positive collaborative learning experiences with the support of the GKA tool. Based on the results, this research provided technological implications for developing and applying the GKA tools in education and pedagogical implications to promote collaborative learning supported by GKA tools.

Computer vision is the automated analysis of visual imagery by computer algorithms that includes, but not limited to object detection and identification, three-dimensional shape estimation, material recognition, and segmentation. The intervention consisted of two to three weeks of professional development that emphasized computer vision technologies with middle school teachers from Title I schools/districts in the states of Arizona and Georgia. Each location trained six in-service teachers. The questions answered through this research were: After in-service teachers engage in professional development emphasizing computer vision: (a) how do their perceptions of computer vision change? (b) how do their perceptions of human vision change? And (c) what are the differences between their perceptions of computer vision and human vision? Personal Construct Theory (Kelly, 1955) was used to explore our research questions. Elements (n = 2; computer vision and human vision) were defined and pairwise comparisons yielded constructs (n = 18) administered in the form of repertory grids. Hierarchical cluster analysis was performed, and clusters were identified. Results showed that in-service teachers’ perspectives of computer vision changed with construct shifts within all four dendrograms that contained between one to eight constructs; all clusters yielded mean increases. Perspectives of human vision stayed relatively consistent across two clusters. The element human vision had a 6% (n = 1) shift in cluster membership, and the element computer vision generated a 72% (n = 13) change in the number of constructs that shifted clusters. Comparisons of computer vision and human vision indicated that in-service teachers had richer perspectives of computer vision after professional development. The significance of this study rests in its contribution to the limited research on computer vision in teacher education. The results show that a relatively short (two to three weeks) professional development experience can have an impact on in-service teachers’ perspectives of computer vision classroom use.

Automated writing evaluation (AWE) technologies have emerged as promising tools that streamline the feedback process and strengthen students’ writing skills. This meta-review synthesized eleven systematic reviews and meta-analyses on AWE research published from 2015 to 2025. Before the main analyses, all selected reviews were evaluated using Many-Facet Rasch Model (MFRM) to determine the study quality. Next, syntheses methods employed narrative approach and text mining analysis. The results suggested the shift from rule-based AWE system to AI-driven AWE tools over three decades. The synthesized findings from meta-analyses supported the effectiveness of AWE on surface-level writing (e.g., grammar, spelling) but highlighted its limitations in improving high-order level of writing (e.g., argumentation). Further, drawing on moderator analyses, educational levels and duration deserve attention in the implementation of AWE. Finally, persistent challenges, future research directions, and practical pedagogy were also identified and discussed. Overall, the present meta-synthesis study supports the potential value of AWE as an adjunct tool rather than a replacement for human feedback in writing instruction.

Feedback is a crucial element in supporting student learning, yet little is known about how students’ perceptions—rather than just the quality and quantity of feedback—impact their experience. This study addresses this gap by first identifying four key dimensions of students’ perceptions of feedback: intimacy, intellectual respect, efficiency, and credibility. Using a 2 × 2 factorial design with 129 undergraduates in an online discussion context, we examined how feedback source (teacher vs. machine) and target (participatory vs. cognitive) influenced these perceptions. Furthermore, we assessed how these dimensions predicted two outcome variables: behavioral intention and perceived effectiveness. A two-way MANOVA revealed that students perceived teacher-generated feedback significantly more positively than identical machine-generated feedback. Interaction effects between source and target were also found, particularly regarding credibility. Multiple regression analyses revealed that efficiency was a key predictor of behavioral intention, while intellectual respect and credibility emerged as key predictors of perceived effectiveness. The findings suggest theoretical and practical implications for design and use of feedback in online learning, advocating for a future that values teacher–machine complementarity.

Generating creative ideas is essential for designers, as creativity underpins all subsequent stages of the design process. This study investigates the impact of Artificial Intelligence Generated Content (AIGC) and traditional methods on design ideation in the context of design education. A controlled experiment was conducted with 21 undergraduate industrial design students of similar academic backgrounds, divided into an experimental group (AIGC-engaged brainstorming) and a control group (traditional brainstorming). Students’ creative outputs were evaluated based on four criteria: novelty, feasibility, correlation, and utility. The study further examined how different design themes and students’ questioning strategies influenced outcomes. Results show that, overall, AIGC-engaged brainstorming outperformed traditional methods in enhancing the novelty, feasibility, and correlation of learners’ design ideas. However, variations in design themes affected AIGC’s creative effectiveness, suggesting the need to balance the strengths of both AIGC and traditional approaches in educational settings. About utility, different interaction patterns between student groups and AIGC led to divergent results. Drawing on the Geneplore model of creative cognition, this study proposes an AIGC-engaged cognitive–prompting model of design ideation, offering practical guidance for effective collaboration to enhance creative performance in design education.

This longitudinal study, grounded in Dynamic Systems Theory (DST), explores how language teachers’ integration of AI tools evolves over an 18-week period, revealing AI adoption as a complex pedagogical transformation rather than a simple technological shift. Drawing on these findings, the research introduces two models: (1) the DST-informed Pedagogical Content Knowledge (PCK) model, which specifies AI-empowered PCK by detailing the five domains of knowledge teachers draw upon, and how these inform teaching practice and scaffolding strategies for personalised student learning; and (2) the AI-in-PCK stage framework, which maps the trajectory of AI adoption, illustrating how teachers’ concerns and practices evolve from initial exploration and experimentation to strategic integration and ongoing learning, while responding to classroom realities and student feedback. Together, these models illuminate adaptive, multifaceted changes in PCK and teaching practice, highlighting how AI integration shapes decision-making and professional growth. The findings underscore critical implications for designing flexible, context-responsive professional learning and systemic support strategies, particularly in under-resourced rural contexts, and provide a foundation for future AI-in-PCK research.

Traditional cognitive training often relies on repetitive exercises, which can lead to boredom and diminished engagement. Game-based design has the potential to address this issue by making training more engaging and enjoyable. Nevertheless, many existing cognitive training games are not grounded in theoretical frameworks. In addition, many situational training games lack meaningful connections to real-life scenarios, which may influence training effectiveness and learner experience. To bridge these gaps, this study proposes a situational cognitive training game framework, which consist of cognitive training foundation (classic inhibition trainings), motivational design framework (game elements), and human–computer interaction framework (authentic contexts). Furthermore, a series of innovative situational training games was developed based on the framework. To ensure that training-oriented games could still retain the affective advantages of game-based learning, we compared these games with casual games from three perspectives: training outcomes, motivation, and game experience. A total of 38 university students from Taiwan participated in the experiment, engaging in both types of games. The findings revealed that the cognitive training games significantly increased inhibition ability, comparing post-test scores between groups and within group. Moreover, participants reported higher motivation, particularly in the dimension of confidence, when playing situational training games than when playing casual games. No significant difference was found in overall game experience between the two types of games. These findings suggest that situational training games can effectively enhance inhibition ability while sustaining motivation and providing a comparable game experience to casual games. The results highlight the importance of designing executive function training games grounded in theory and incorporating game elements such as real-life authenticity and appropriate challenges to support learners’ sense of relatedness and autonomy.

The integration of computational thinking (CT) and mathematics learning in K–12 education has garnered increasing scholarly interest; however, its application within the domain of probability remains underexplored. This study designed and implemented a seven-week Scratch-based probability module and investigated its effects on students’ CT and mathematics learning, as well as influencing factors: gender and prior programming experience. Using a mixed-methods approach, data were collected from 31 seventh-grade students at a Shanghai middle school via pre- and post-tests, interviews, and students’ digital makings. The results revealed that the Scratch-based probability module significantly enhanced students’ CT concepts (particularly the ‘conditionals’ concept), CT perspectives: ‘expressing’ and ‘connecting’, and facilitated students’ mathematics learning, with notable improvements in ‘classical probability’ and ‘compound events’. While gender moderately influenced CT concept mastery, no significant difference existed in mathematics concept performance. Prior programming experience showed no significant influences on performance in either CT concepts or mathematics concepts. These findings contribute to the growing body of literature on CT integration in mathematics education by broadening the scope of mathematical content suitable for CT integration, offering practical implications for curriculum-aligned CT integration, and shedding light on the complex interplay between CT and mathematical learning.

As online learning continues to grow, understanding how learners regulate their cognition both individually and collaboratively is critical to designing meaningful online learning experiences. While the Community of Inquiry (CoI) framework provides a well-established model for designing quality online courses, its relationship with online learners’ metacognitive development in collaborative settings remains underexplored. This study investigated the relationships between online learners’ shared metacognition—including self-regulation and co-regulation—and their perceived social, teaching, and cognitive presences, as outlined in the CoI framework. Before investigating these relationships, we evaluated the psychometric properties of the partially validated Shared Metacognition Questionnaire (Garrison and Akyol, The Internet and Higher Education 24:66–71, 2015) and provided additional validity and reliability evidence. The study included 348 graduate students enrolled in 25 fully online courses in the United States. Data were analyzed using confirmatory factor analysis and structural equation modeling. The findings revealed that (1) the SMQ is a valid and reliable instrument for assessing learners’ metacognition in online collaborative settings, (2) shared metacognition significantly correlates with social, teaching, and cognitive presences, (3) cognitive presence demonstrates the strongest correlation with shared metacognition, followed by social and teaching presences, and (4) compared to self-regulation, co-regulation is more influential across social, teaching, and cognitive presences. These findings contribute to the measurement of online learners’ metacognition, advance the understanding of the CoI framework by linking it to metacognitive processes, and offer practical implications for designing collaborative online learning environments that foster metacognitive development.

Effective teaching plays a vital role in fostering cognitive and psychological development in humans. Virtual reality simulations, along with the embedded, artificial intelligence (AI)-powered virtual student agents, offer opportunities for preservice teachers to practice teaching iteratively in a dynamic, context-rich manner. However, preservice teachers face challenges when practicing in these AI-enhanced VR simulations. In this study, we designed and investigated model-based supports aimed at scaffolding preservice teachers during simulation-based pretraining, in-simulation, and post-simulation stages. This experimental study with 57 preservice teachers indicated significant positive impacts of model-based support on teaching knowledge and skills development. Furthermore, significant improvements in knowledge of teaching were observed in the experimental groups using model-based support in the AI-enhanced VR simulation, from pre- to post-test. However, the difference between the experimental and control groups in teaching self-efficacy was nonsignificant. The implications of these findings and potential future directions for designing learning support in VR simulation-based teacher education are discussed.

Students often struggle to stay engaged and effectively regulate their learning in online environments, which can negatively impact their learning experiences. Despite the established importance of self-regulation of learning skills (SRLs) in maintaining engagement, many students face significant challenges in developing and implementing these skills due to a lack of adequate feedback. This is primarily due to tutors' high workloads and the difficulties inherent in engaging students in online settings. This study examines the impact of Learning Analytics (LA)-driven interventions to improve students' SRLs in online learning environments. Specifically, it compares the impact of feedback from Generative Artificial Intelligence (GenAI) and human tutors in a nine-week statistics course delivered via MOODLE at a higher education institution, employing LA and clustering techniques to model SRLs based on Ye & Pennisi’s (2022) framework. In a quasi-experimental design, participants with varying SRLs were assigned to either a tutor-feedback or GenAI-feedback group. Feedback readability and reliability evaluations indicate that LA-driven GenAI-produced feedback was significantly more readable than human tutor feedback (p < 0.01) and demonstrated higher reliability than tutor-generated feedback. Results show that students in the low SRLs cluster receiving GenAI feedback exhibited statistically significant improvements in goal-setting skills (p < 0.05) and overall SRLs levels (p < 0.05) compared to the tutor-feedback group. In contrast, no significant differences were observed among high SRLs cluster students. This study underscores the potential of LA-driven GenAI feedback to be able to develop tailored, scalable feedback, improving SRLs performance of low SRLs students in online higher education contexts. Future research should explore these effects across diverse student groups and investigate the collaborative potential of semi-automated feedback systems that include tutors.

This study examined social networks and self-regulation in two different co-creation environments (2D digital and 3D VR co-creation) through SNA and ANCOVA. The study utilized a quasi-experimental research design with 44 tenth-grade students, 24 males (55%) and 20 females (45%), from an English class at a public senior high school in northern Taiwan. To assess the effects of the environment, the classes were divided into a control group and an experimental group, with a valid sample of 22 in each group. As the results showed, 3D VR co-creation manifested more restricted social networks with fewer cliques but higher cohesiveness, reciprocity and betweenness. This suggests not only greater community solidarity and stability in 3D VR co-creation but also greater needs of 3D VR co-creators for interconnection when facing the novelty effect. Such dependence on interaction corroborated the dominance of lower-level cognitive strategies in 3D VR communities and echoed the ANCOVA results showing that strategy use was the most prominent self-regulatory skill in 3D VR co-creation. The limitation on gender composition was specified. Further implications are discussed, and suggestions for increased co-creation time, coworking strategies, and the development of a predictive model as scaffolding are offered.

This study examines the evolving relationship between AI chatbots and academic integrity and students’ AI chatbot usage in high schools one and a half years after the release of ChatGPT. Through a comprehensive survey of students across six schools (N = 4,354) in the United States, we investigated students' self-reported cheating behaviors, patterns of AI use across different school-related tasks, and student perspectives on appropriate AI use in academic settings. Our findings revealed that overall cheating rates remain stable at 72.06%, consistent with historical baselines and prior studies, suggesting that AI availability has not changed overall cheating prevalence in high school. Additionally, more students reported using AI chatbots for support tasks like concept explanation and idea generation. Regarding students' reported preferences for allowing AI chatbots for school-related tasks, at this point, they still strongly supported using AI for conceptual understanding and brainstorming, and they maintained clear boundaries against using it for completing entire assignments. These findings suggest that while AI’s prevalence has not altered the patterns of academic integrity at schools, students' evolving perspectives on appropriate AI use provide valuable insights for schools and administrators integrating AI into traditional school settings.

Gamification shows promise for enhancing student engagement in higher education; however, most research employs traditional experimental designs that fail to capture the iterative, context-sensitive process necessary for effective implementation. This study demonstrates how Educational Design Research (EDR) provides a robust methodological framework for gamification research, moving beyond effectiveness questions to understand how game-based interventions can be systematically designed and refined over time. Responding to persistent engagement challenges in a graduate instructional design course where students reported content as “dry and uninteresting,” a collaborative research team employed EDR methodology across six iterations. Using the Werbach and Hunter framework, the team systematically integrated gamification dynamics (space exploration narrative), mechanics (mission-based progression), and components (leaderboards, badges, interactive multimedia). Data from 188 participants included performance metrics and satisfaction surveys (n = 143, 76% response rate), analyzed through mixed-methods approaches, including systematic qualitative coding. Findings reveal students engaged most actively with narrative-driven missions and collaborative discussions. Students reported enhanced motivation compared to traditional course formats, with qualitative analysis revealing appreciation for coherent storylines, meaningful progression, and authentic connections between game elements and learning objectives. The iterative EDR process enabled continuous refinement, resulting in five empirically grounded design principles: collaborative design, narrative continuity, interactive technological ecosystems, peer-to-peer interaction, and flexible engagement strategies. This study provides practical guidance for gamification implementation and methodological innovation in educational technology research, demonstrating how EDR’s systematic approach captures complex contextual factors influencing gamification success and generates transferable theoretical understanding about game-based learning design.

This article introduces a Strategic, Co-created, and Iterative Learning Design and Development (SCILDD) process, based on models drawn from the literatures of instructional design, design-based thinking, and design-based research. SCILDD includes five essential design components: the initial establishment of strategy, the evolution of that strategy throughout the process, iterative cycles of development, co-creation with target learners, and three clear phases of work that approximately align with analysis (A), design and development (DD), and implementation and evaluation (IE). When utilized together, these components function to leverage the benefits of previous models while mitigating their challenges. SCILDD is intended to invite flexibility and adaptability while maintaining strategic focus, to support design processes situated within a wide range of contexts. The Classroom Pivotal Response Training (CPRT) case study illustrates the application of SCILDD to create (a) a web-based tool to help teachers determine which CPRT components should be prioritized for individual students or instructional settings, and (b) a virtual training module to allow hands-on application of CPRT concepts while supporting large-scale training tailored to teachers and paraprofessionals. This initial application of SCILDD resulted in products perceived to be engaging, usable, and useful. Considerations for future SCILDD application are discussed.

Engaging in independent learning during self-study time is an essential part of learning in higher education. The ubiquity of mobile devices and their applications offer students a flexible, on-the-go learning experience. However, flexible learning environments require students to self-regulate their learning. The current study examined student engagement with a mobile study app intended for students to engage in retrieval practice with feedback as part of self-regulated learning (SRL). Three research questions were addressed: (1) What are the student engagement profiles that can be identified based on students’ activity with the study app? (2) Do the profiles differ in SRL and academic performance? and (3) What is the relationship between the identified student profiles, SRL, and academic performance? Through a learning analytics approach, results identified three distinct student profiles: active, disengaged, and utilitarian. Self-efficacy and time-management positively predicted exam grades, while self-evaluation showed an inverse effect. Active engagement positively influenced exam grades, underscoring individual differences in SRL.

In recent years, significant advances in brain imaging technologies, artificial intelligence (AI), and neuroscience have significantly improved our understanding of how the brain functions in the learning process. This research aims to explore the role of neuroscience and AI in designing effective feedback mechanisms by investigating their impact on learning-teaching processes. To achieve this goal, the study addresses the following questions: "1) What are the objective orientations, thematic patterns, methodological approaches, and key findings of neuroscience research investigating feedback processes?" and "2) What are the objective orientations, thematic patterns, methodological approaches, and key findings of AI research investigating feedback processes?" This research adopts a systematic review methodology that encompasses four key stages: planning, searching, selection, and synthesis. Using specific keywords, relevant studies in educational sciences, educational psychology, and neuroscience were systematically identified from the Web of Science (WOS) database, specifically targeting publications indexed in SSCI, ESCI, and SCI-E. Through comprehensive thematic analysis, we systematically mapped the research landscapes in both domains. The systematic review reveals that neuroscience and AI research provide complementary insights into feedback effectiveness. The findings suggest that optimal feedback design requires integrating neurologically-informed principles with AI-enabled delivery systems to create developmentally appropriate and individually adaptive learning environments. This study further highlights a notable deficiency in interdisciplinary research that integrates neuroscience and AI approaches to optimize feedback for performance and learning. Limited collaboration between these fields hinders knowledge exchange and prevents mutual enhancement of feedback methodologies. These fields have developed independently with limited integration and a lack of comprehensive theoretical exploration and investigation into various feedback types and strategies.

Developing sculpting and carving skills among art college students remains a critical problem in art education, where the conventional approaches are not very constructive for creativity, skill generation, and spatial conception development. To overcome this challenge, 3D Virtual Reality (VR) technology was developed for the enhancement of sculpture and carving skills. This research examined the difference between sculpting in a VR environment and traditional clay modeling, including their impact on creativity, skill acquisition, student motivation and participation, learning time, spatial ability, and creativity. A mixed method was used that involved quantitative questionnaires and qualitative interviews, with 532 undergraduate students who were a part of both VR and clay models, focusing on data collection. The result showed that the students were able to perform better in VR environments with enhanced creativity, technical skills, spatial thinking, and higher levels of engagement and motivation when compared to clay sculpting. It was found that VR allowed students to learn and perform complex sculpting processes in a short time, and students were more innovative in the ways they designed sculptures. It was also shown that VR facilitated the acquisition of 3D space and, more importantly, provided an interactive setting that was immersive and promoted creativity. This study demonstrates the use of VR in the field of arts education, providing evidence that VR can change the course of traditional sculpting and carving. The results highlight the VR’s ability to improve technical skills as well as artistic skills. The research indicates that VR improves the process of teaching sculpting techniques and provides a better understanding of the creative function and faster learning of the sculpting process.

The online inquiry-based learning (OIBL) environment enhances autonomy and provides a safe context for implementing science education. It facilitates the development of students’ inquiry skills and understanding of scientific concepts. However, the effectiveness of OIBL is often compromised by students’ limited metacognitive abilities, particularly in planning, monitoring, and evaluating their inquiry processes. To address this challenge, this study designed a metacognitive scaffolding-supported online inquiry-based learning (MS-OIBL) approach to enhance students’ self-regulation and support effective inquiry learning in OIBL environments. A quasi-experimental study was conducted with fifth-grade students at two similar-level schools in Beijing, China. The study evaluated the impact of the MS-OIBL approach on students’ science achievement, metacognitive ability, and inquiry skills. Data were collected through surveys and log entries. A total of 236 students participated; 121 in the experimental group used the MS-OIBL approach, and 115 in the control group used a traditional OIBL approach without metacognitive scaffolding. The results revealed that the MS-OIBL group significantly outperformed the traditional OIBL group in terms of science achievement, metacognitive regulation ability, and inquiry skills. However, no significant differences were found between the two groups in overall metacognitive ability or metacognitive knowledge. These findings suggest that the MS-OIBL approach effectively improves students’ science performance and inquiry skills. It also promotes metacognitive regulation behavior and enhances metacognitive regulation ability. This study provides valuable insights for teachers, students, and researchers in the fields of science education and metacognition. It highlights the potential of metacognitive scaffolding to optimize online inquiry learning environments.