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23.04.2026. Junge Menschen nutzen Künstliche Intelligenz, um sich ihren Alltag zu erleichtern. Bei der Interaktion mit KI-Chatbots können sie jedoch auch Falschinformationen erhalten. In sozialen Medien stoßen sie immer häufiger auf KI-generierte Desinformation. Im sechsten weitklick-Webinar wird der Schwerpunkt darauf gelegt, welche Rolle KI bei Desinformation spielt und wie Lehr- und pädagogische Fachkräfte junge Menschen aufklären und für den Umgang damit stärken können. Dabei werden zentrale Fragen in den Fokus gerückt: Wie entstehen Falsch- und Desinformation durch KI-Anwendung und wie werden sie verbreitet? Was ist ein Deep Fake und wie erkenne ich es? Wie recherchiere ich zielgerichtet und faktensicher im Netz? Die Teilnehmenden lernen praktische Tools für die Online-Recherche kennen, mit denen junge Menschen Fakten schnell und zuverlässig überprüfen und mögliche Fake News identifizieren können: von einfachen Checks im Alltag bis hin zu systematischer Recherche und Fact-Checking-Methoden. Zum Abschluss werden konkrete Impulse für die medienpädagogische Praxis vermittelt. Ziel ist es, Fälschungen künftig schneller zu erkennen und diese Kompetenzen im Rahmen der Bildungsarbeit weiterzugeben. Veranstalter: weitklick / FSM e.V.. Link: https://www.weitklick.de/veranstaltungen/webinar-6/zwischen-fakten-und-fakes .

05.03.2026. Künstliche Intelligenz verändert, wie wir leben, lernen und kommunizieren – und sie ist längst Teil des Alltags junger Menschen. In dieser Fortbildung erhalten Lehrkräfte einen anschaulichen Überblick über die wichtigsten Facetten von KI: von den Grundlagen maschinellen Lernens über KI-Chatbots bis hin zu Fragen von Zuverlässigkeit und Desinformation. Neben einer kompakten Einführung in die Funktionsweise sowie Chancen und Risiken von KI-Systemen liegt der Fokus auf der praktischen Umsetzung im Unterricht. Die Teilnehmenden probieren verschiedene Unterrichtsmaterialien aus, reflektieren Einsatzmöglichkeiten und entwickeln eigene Ideen, wie sie die Themen zielgruppengerecht im Unterricht behandeln und Medienkompetenz bei Jugendlichen stärken können. Veranstalter: weitklick / FSM e.V.. Link: https://www.weitklick.de/veranstaltungen/21/ki-im-ueberblick .

27.04.2026. Die Hochschule für Wirtschaft und Recht (HWR) Berlin lädt dazu ein, ihren berufsbegleitenden Studiengang Master Nachhaltigkeits- und Qualitätsmanagement bei einer ihrer Online Info-Sessions kennenzulernen! Teilnehmende erhalten Informationen zu den Zielen und Inhalten des Studiengangs,  Zulassungsmodalitäten, Bewerbungsverfahren, Karriereoptionen und zu ihren individuellen Fragen. Veranstalter: Hochschule für Wirtschaft und Recht (HWR) Berlin / Berlin Professional School. Link: https://www.berlin-professional-school.de/veranstaltung/detail/384-online-info-session-master-public-administration .

12.03.2026. Die Hochschule für Wirtschaft und Recht (HWR) Berlin lädt ein, ihren internationalen, 100% online studierbaren Master Business Management bei einer ihrer Info-Sessions (englischsprachig) kennenzulernen: Master Business Management: Digital Business Management & Green Energy and Climate Finance. Teilnehmende erhalten Informationen zu den Zielen und Inhalten des jeweiligen Studiengangs, den Zulassungsmodalitäten, zum Bewerbungsverfahren und zu Karriereoptionen. Veranstalter: Hochschule für Wirtschaft und Recht (HWR) Berlin / Berlin Professional School. Link: https://www.berlin-professional-school.de/veranstaltung/detail/420-online-info-session-online-master-business-management .

23.09.2026. Der Einsatz von Künstlicher Intelligenz (KI) als ein spezifisches Phänomen innerhalb der Digitalisierung wird gesamtgesellschaftlich immer breiter diskutiert. Aus der Perspektive der Erziehungs- und Bildungsphilosophie lassen sich verschiedene Herausforderungen und Fragestellungen im Umgang mit Künstlicher Intelligenz (KI) aufwerfen, die im Rahmen der diesjährigen Herbsttagung der Kommission Bildungs- und Erziehungsphilosophie besprochen werden sollen. Veranstalter: Deutsche Gesellschaft für Erziehungswissenschaft (DGfE), Kommission Bildungs- und Erziehungsphilosophie. Link: https://www.dgfe.de/sektionen-kommissionen-ag/sektion-2-allgemeine-erziehungswissenschaft/kommission-bildungs-und-erziehungsphilosophie .

16.09.2026. Im Zentrum der 34. Jahrestagung der DGfE-Kommission Grundschulforschung und Pädagogik der Primarstufe steht im Jahr 2026 das Thema „Anfragen an die Grundschulforschung: Zwischen Grundlegung und Anwendungsbezug“. Bildungsinstitutionen sehen sich in Phasen multipler Krisen mit vielfältigen Aufgaben und Anforderungen konfrontiert. In diesen recht unsicheren und von Wandlungen geprägten Zeiten muss sich auch die Grundschule neu platzieren. Dabei widmet sie sich den Herausforderungen, die sich vor diesem Hintergrund für die Grundschulpädagogik und die Professionalisierung von Grundschullehrkräften aktuell abzeichnen. Veranstalter: Deutsche Gesellschaft für Erziehungswissenschaft (DGfE), Kommission Grundschulforschung und Pädagogik der Primarstufe. Link: https://www.hf.uni-koeln.de/43414 .

09.09.2026. Bewegungen sind für pädagogische Kontexte und die Erziehungswissenschaft im Allgemeinen sowie für die erziehungswissenschaftliche Migrations- und Rassismusforschung im Spezifischen konstitutiv. Im Rahmen der Kommissionstagung möchten wir daher die Vielschichtigkeit und Vielgestaltigkeit von Bewegungen und Gegenbewegungen sowie deren Bedeutung für und Ausdrucksformen innerhalb erziehungswissenschaftlicher Migrations- und Rassismusforschung thematisieren. Veranstalter: Deutsche Gesellschaft für Erziehungswissenschaft (DGfE), Kommission Erziehung und Bildung in der Migrationsgesellschaft. Link: https://www.dgfe.de/sektionen-kommissionen-ag/sektion-3-interkulturelle-und-international-vergleichende-erziehungswissenschaft/kommission-interkulturelle-bildung/tagungen .

09.09.2026. Die diesjährige Jahrestagung, die gemeinsam von der Kommission „Qualitative Bildungs- und Bi- ographieforschung“ und der Kommission „Psychoanalytische Pädagogik“ ausgerichtet wird, zielt darauf, Erziehungs-, Bildungs- und Biographisierungsprozesse in ihren psycho-/sozialen Bezügen und Dynamiken sowie in ihren individuellen, gesellschaftlichen und kollektiven Dimensionen in den Blick zu nehmen. Das Anliegen ist es, Phänomene des Zusammen|Halt|ens in Krisenzeiten aus unterschiedlichen theoretischen und method(olog)ischen Perspektiven zum Gegenstand zu machen. Veranstalter: Deutsche Gesellschaft für Erziehungswissenschaft, Kommission Qualitative Bildungs- und Biographieforschung & der Kommission Psychoanalytische Pädagogik. Link: https://www.dgfe.de/sektionen-kommissionen-ag/sektion-13-differentielle-erziehungs-und-bildungsforschung/kommission-psychoanalytische-paedagogik/tagungen .

15.04.2026. While technology is one of the most important factors shaping contemporary society, it is hard to say whether our current era regards it more with hope or with dread. Debates surrounding the role of technology in our lives are highly polarised. This conference seeks to explore the cultural and intellectual roots of technological optimism in the 1970s and 1980s. These decades – while marked by significant technological advancements, many of which had their roots in the more optimistic 1960s – were marked by pervasive undercurrents of technological pessimism. The conference aims to take into account various perspectives in regards to different geopolitical contexts. Those who are interested in attending the conference, can register by sending a brief e-mail to digital@ieg-mainz.de by 25 March 2026. Veranstalter: Leibniz-Institute of European History Mainz. Link: https://ieg-dhr.github.io/techno_optimism/ .

25.06.2026. Der Workshop dreht sich um das Thema "geschlechtliche Vielfalt in unserem Denken und Sprachhandeln" und geht den Fragen nach, inwiefern wir Diversität und Sensibilität auf sprachlicher Ebene praktizieren können. Gemeinsam schauen wir welche Kategorien unser Denken und Wahrnehmen prägen und beschäftigen uns mit Selbstreflexion und Entlernen. Der Workshop richtet sich an Menschen, die bereits Vorkenntnisse im Bereich geschlechtliche Vielfalt und gendersensible Sprache haben und diese vertiefen wollen. Veranstalter: Heinrich Böll Stiftung Baden-Württemberg e.V.. Link: https://calendar.boell.de/de/event/binaer-war-gestern-von-entgendertem-denken-und-gendersensiblem-sprechen-6 .

13.06.2026. Gegen Rassismus sind eigentlich alle. Die meisten weißen Menschen würden sich auch als tolerant und weltoffen beschreiben. Trotzdem (oder gerade deshalb) ist und bleibt Rassismus ein riesiges gesellschaftliches Problem, das Schwarze Menschen und Menschen of Color stark einschränkt. Das Seminar richtet sich an weiße Menschen, die bereit sind sich mit Weißsein und Rassismus auseinanderzusetzen. Voraussetzung ist eine Neugier sich mit sich selbst auseinanderzusetzen und der Wunsch gemeinsam mit anderen zu lernen, um sich weiterzuentwickeln. Veranstalter: Heinrich Böll Stiftung Nordrhein-Westfalen. Link: https://calendar.boell.de/de/event/was-hat-rassismus-mit-mir-zu-tun-0 .

16.05.2026. Dieser Workshop richtet sich an alle, die sich gegen digitale Hetze engagieren und sich für eine demokratische, respektvolle Diskussionskultur im Netz einsetzen möchten. In diesem Workshop analysieren wir, woher der Hass im Netz kommt, welche Gruppen besonders betroffen sind und welche Strategien Hater*innen nutzen. Vor allem aber geht es darum, wie wir mit Counter Speech aktiv werden und digitale Räume zurückerobern können. Veranstalter: Heinrich Böll Stiftung Baden-Württemberg e.V.. Link: https://calendar.boell.de/de/event/hate-speech-und-counter-speech-holen-wir-uns-das-netz-zurueck-0 .

26.02.2026. Die Hochschule für Wirtschaft und Recht (HWR) Berlin lädt ein, ihre internationalen MBA- und Masterstudiengänge bei einer ihrer Online Info-Sessions kennenzulernen: 13.00-13.30 Uhr: Berlin Full-Time MBA 13.30-14.00 Uhr: Master International Business Management Teilnehmende erhalten Informationen zu den Zielen und Inhalten des jeweiligen Studiengangs, zu Zulassungsmodalitäten, Bewerbungsverfahren, Karriereoptionen und zu ihren individuellen Fragen. Veranstalter: Hochschule für Wirtschaft und Recht (HWR) Berlin / Berlin Professional School. Link: https://www.berlin-professional-school.de/veranstaltung/detail/401-online-info-session-internationale-mba-masterprogramme .

07.05.2026. Antifeminismus ist nicht nur fester Bestandteil rechter und rechtsextremistischer Ideologie, sondern wirkt längst auch in die sogenannte „Mitte der Gesellschaft“. In diesem Workshop widmen wir uns verschiedenen Gegenstrategien: Wie können wir antifeministischen Aussagen begegnen und den Angriffen auf Vielfalt und Selbstbestimmung etwas entgegensetzen? Der Workshop richtet sich an alle, die bereits ein grundlegendes Verständnis von antifeministischen Ideologien haben und ihre Handlungssicherheit im Umgang mit antifeministischen Aussagen ausbauen möchten.  Veranstalter: Heinrich Böll Stiftung Baden-Württemberg e.V.. Link: https://calendar.boell.de/de/event/auf-antifeminismus-reagieren .

05.05.2026. In dem Workshop erhältst du fundiertes Wissen über die Struktur und Funktionsweise von Verschwörungserzählungen im Kontext der Klimakrise. Du lernst zentrale Akteure und Narrative der Klimawandelleugnung kennen und analysierst sozialpsychologische Mechanismen, die Menschen anfällig für solche Erzählungen machen. Neben theoretischen Einblicken bietet der Workshop praxisnahe Strategien für den Umgang mit Verschwörungsgläubigen. Veranstalter: Heinrich Böll Stiftung. Link: https://calendar.boell.de/de/event/greencampus-online-seminar-verschwoerungserzaehlungen-im-kontext-der-klimakrise-2 .

04.05.2026. In diesem Workshop möchten wir einen Austauschraum für diejenigen Personen schaffen, die mit dem Nahostkonflikt beruflich oder ehrenamtlich in Berührung kommen. Ziel des Workshops ist u.a. einen konstruktiveren Umgang in der Bearbeitung des Nahostkonfliktes zu erzielen. Unter Berücksichtigung der eigenen Zugänge, Erfahrungen und Positionierungen der Teilnehmenden sollen Wege gefunden werden, um den Nahostkonflikt besprechbar(er) zu machen. Veranstalter: Bildungswerk Berlin der Heinrich-Böll-Stiftung e.V., gefördert von der Berliner Landeszentrale für politische Bildung. Link: https://calendar.boell.de/de/event/der-nahostkonflikt-berlin-0 .

29.04.2026. Mikrofeminismus beschreibt kleine, alltägliche Handlungen und Strategien, mit denen feministische Impulse gesetzt werden – oft subtil, manchmal spontan, aber immer bewusst. Der Workshop ist für dich, wenn dir intervenieren oft zu anstregend ist oder du generell erschöpft bist und trotzdem neue Handlungsmöglichkeiten entwickeln möchtest. Veranstalter: Heinrich Böll Stiftung Baden-Württemberg e.V.. Link: https://calendar.boell.de/de/event/es-beginnt-im-kleinen-workshop-ueber-mikrofeminismen .

25.04.2026. Eine Rede kann gut vorbereitet sein. Die Reaktion darauf nicht. Was danach kommt, entscheidet oft darüber, ob Menschen zuhören oder abschalten. Im Mittelpunkt steht die Fähigkeit, auch dann überzeugend zu bleiben, wenn du spontan antworten musst.  Dieser Workshop richtet sich an Menschen, die häufiger bei Podien, im beruflichen Kontext oder in politischen Gesprächen Rede und Antwort stehen. Wenn du deine Reaktionsfähigkeit verbessern und deine Positionen überzeugender vertreten möchtest, findest du hier praktische Ansätze. Veranstalter: Heinrich Böll Stiftung Baden-Württemberg e.V.. Link: https://calendar.boell.de/de/event/rede-und-antwort-stehen-16 .

24.04.2026. Immer mehr stehen auch Moderator*innen und Prozessbegleiter*innen vor der Herausforderung, mit rechten Aussagen und Akteur*innen konfrontiert zu sein. Wie bewege ich mich im Spannungsfeld zwischen Neutralität meiner Moderationsrolle und der Sicherstellung einer demokratischen Gesprächskultur? Welche Werkzeuge und Methoden stehen mir zur Verfügung, um die Kontrolle über die Situation behalten? Das Seminar richtet sich an haupt- und ehrenamtliche Aktive und freiberufliche Moderator*innen und Seminarleitungen, welche bereits erste Erfahrungen in der Moderation von Gruppenprozessen und offenen Veranstaltungsformaten gesammelt haben. Veranstalter: Bildungswerk Berlin der Heinrich-Böll-Stiftung e.V., gefördert von der Berliner Landeszentrale für politische Bildung . Link: https://calendar.boell.de/de/event/moderation-im-kontext-rechter-und-rassistischer-positionen-13 .

23.04.2026. In diesem Workshop setzen wir uns mit dem Konzept der Intersektionalität auseinander. Wir betrachten, wie verschiedene Diskriminierungsformen und Identitätskategorien zusammenwirken und sich überschneiden. Der Workshop richtet sich an alle, die sich mit Intersektionalität beschäftigen möchten und ihr Bewusstsein für Diskriminierungen und Privilegien stärken wollen. Ob du neu im Thema bist oder dein Wissen erweitern möchtest – hier erhältst du konkrete Anregungen und Unterstützung. Veranstalter: Heinrich Böll Stiftung Baden-Württemberg e.V.. Link: https://calendar.boell.de/de/event/11-der-intersektionalitaet .

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.

Reading comprehension is an essential skill for learning and is facilitated through extensive reading, text discussion and analysis, and writing. With the spread of mobile technology, Artificial Intelligence (AI)-based chatbots, which combine speech recognition and semantic understanding, is a potential language learning tool to overcome students’ learning difficulties encountered in traditional instruction by providing personalized, real-time feedback. However, some students are not able to well organize learning contents through text-only reading, resulting in ineffective learning. Graphic organization is seen as a useful strategy to improve students' memory and comprehension. The study employed a quasi-experimental design to investigate the effects of an intelligent chatbot combining the graphical summarization strategy, speech recognition, and semantic comprehension on students' reading performance, learning experience, and tendency to think critically and reflectively. The subjects were 107 Grade 10 students, randomly divided into four groups, using a graphical summarization-based AI chatbot, a text summarization-based AI chatbot, graphical summarization-based learning, and text summarization-based learning. It was found that both the AI-based chatbot and graphical summarization strategy benefited students by improving their learning achievements; moreover, the significant interaction between the AI-based chatbot and graphical summarization strategy further showed the effectiveness of combining the two. In terms of intrinsic motivation, self-efficacy, critical thinking and cognitive load, the AI-based chatbot benefited the students more than traditional teaching; moreover, the graphical summarization strategy benefited the students more than text summarization did. The interview results further showed that combining the graphical summarization strategy and the AI-based chatbot enabled students to gain a more complete understanding of the learning content.

The question is the primary step in knowledge building (KB) learning. Students propose a profound question that can inspire them to engage in collaborative exploration actively, thereby forming deep KB. However, not all students can pose valuable questions in the current KB. Considering the potential role of questioning in promoting the development of community theory, this study employed an effectiveness validation research design to investigate how a data-driven assessment and feedback approach influences students’ ability to pose thoughtful questions in KB learning. Thirty-two sixth-grade students from a primary school in Yangzhou, China, participated in a 15-week KB learning process using the data-driven assessment and feedback method. The research findings indicated that by addressing challenges such as student anxiety over scores, challenges in teachers’ integration of feedback into instruction, and variations in students’ ability to interpret and apply feedback, the data-driven assessment and feedback approach was refined and optimized. This approach effectively supported students in posing deep questions by enhancing their domain contextuality, openness, and dynamicity. The data-driven assessment and feedback method proposed in this study provided researchers and teachers with operational references for human–computer collaboration and was expected to serve as effective tools for supporting KB learning and posing deep questions.

This study investigates the impact of AI research capacity on research and innovation productivities in higher education institutions in the top 10 countries, by employing Prais-Winsten Corrected Standard Errors (PCSE), Panel Quantile Regression (PQR), and Two-Stage Least Squares (2SLS) models from 2017 to 2021. The findings revealed that an increase in AI research output (AIRO) is improving the institutions' research productivity, while AI patent grants (AIPG) have a negligible effect on it; but AI research impact (AIRI) and AI talent concentration (AITC) have no impact on it. Furthermore, increases in AIRO, AIRI, and AIPG improve the institutions' innovation productivity, but AITC does not. Moreover, AI investment (AII) has a negligible impact on AI jobs, skills and AI software development; yet, the AII promotes bachelor’s degree AI professionals but negligibly impacts that of associates, less than bachelor’s degree, and advanced degrees AI professionals. On the other side, AII has a synergetic effect on AIRO and AIPG in stimulating the institutions' research productivity, but it has no such effect on AIRI and AITC. But with respect to the institutions' research innovation productivity, the AII has such an effect on AIRO, AIRI, and AIPG, whereas such an effect is negligible on AITC.

The recent emergence of generative artificial intelligence (GenAI) agents has brought significant potential and challenges in education. In science, technology, engineering, arts, and mathematics (STEAM) education, it is especially vital to cultivate students’ GenAI literacy and critical thinking skills. These competencies enable students to effectively apply artificial intelligence (AI) technologies in their STEAM learning. The present study proposed a GenAI agent-based STEAM learning approach and adopted the information world mapping (IWM) method to explore how a GenAI agent enhanced the engagement of 12 middle school students in a STEAM course, and their perceptions of GenAI agent-supported STEAM learning. These students fully participated in a 16-week course titled “Creative Design in STEAM Education.” Participants were asked to illustrate significant individuals, items, locations, and their interconnections to delve into their perceptions of and feedback on GenAI agent-supported STEAM learning. The results showed that while middle school students’ GenAI literacy improved, it did not reach a statistically significant level. In contrast, there was a significant enhancement in their critical thinking tendencies. Regarding students’ perceptions of GenAI agent-supported STEAM learning, the majority acknowledged that the GenAI agent played the roles of information provider and interaction facilitator in STEAM learning activities, particularly excelling in information retrieval and learning interactions. Moreover, the GenAI agent had positive influences on students in various aspects, including learning support, task completion, tool functionality, user experience, usage confidence and attitudes, as well as perceived warmth. Overall, the GenAI agent-based STEAM learning approach effectively enhanced middle school students’ technical skills and problem-solving abilities while also strengthening their teamwork, ethical awareness, and reflective capabilities, thereby demonstrating its potential for future applications in education.

Metacognitive skills are key transversal skills, strongly linked to academic achievement and essential for effective lifelong learning. They are widely recognized to underpin many of the core competencies required for effective social and professional participation, including core competencies such as critical thinking, problem-solving, and self-awareness, highlighted by United Nations Educational, Scientific and Cultural Organization (UNESCO) as key to Education for Sustainable Development. Digital learning technologies and virtual learning environments increasingly form a core strategic dimension of teaching and learning within Higher Education, however effectively harnessing them to enhance student learning remains challenging. This paper presents the Metacognition Design Framework, an evidence-based learning design that scaffolds the development of metacognitive skills in students through blended learning. It draws on the tools available within the virtual learning environment to provide a unique, and potentially powerful, opportunity to extend ‘metacognitive influence’ on student activity and learning outside standard class times with realistic levels of staff input. The design provides clarity for planning accessible learning interventions, their linkage with appropriate technology, and for guiding how face-to-face and online, or fully online, components interrelate. It also delivers an effective conduit to convey information in a practical format that readily permits adaptation to local contexts. Overall, it offers a practical way to re-frame educators as designers, increasing educator design capacity and the effective deployment of technology to enhance learning, at the same time targeting a learning outcome with high potential impact.

To address the rising cybersecurity threats associated with educational technology in K-12 schools and the gap in cybersecurity education, we designed and implemented a multimedia-based Educational Technology (Edtech) Cybersecurity and Privacy Awareness-Ask-Action (AAA) professional development (PD) for school administrators, technology support staff, and teachers. This study sought to identify the PD’s effectiveness, specifically focusing on the participants’ knowledge increments and the perceptions of their experience. Fifty K-12 school personnel with diverse demographic, technology and contextual backgrounds (district types and school levels) participated. Wilcoxon signed-rank tests indicated a significant increase in participants’ knowledge between pre- and post-test. Inductive coding identified emerging themes from post PD survey and follow-up interviews, highlighting participants’ perceived benefits, challenges encountered and future needs. Participants expressed an overall positive attitude, appreciating the increased cybersecurity awareness and enhanced ability to recognize and act against potential cyber threats to protect students and schools. However, challenges such as technical complexity and resource constraints were noted. Participants suggested future cybersecurity professional development could include more contextual guidance, AI-related cybersecurity topics, and provide up-to-date resources.

This study compares engineering college students’ learning motivation between AI-featured and traditional scaffolding methods using a mixed-methods approach. Two different types of class activities, one involving solving a computational problem and the other assembling an essay, were designed to be carried out using both AI-featured and conventional methods. All activities were designed using the Attention, Relevance, Confidence, and Satisfaction (ARCS) model and results were analyzed using both quantitative and qualitative means. Findings show that participants’ motivation was significantly higher in all Attention, Relevance, Confidence, and Satisfaction categories when they used AI-featured methods for solving computational problems while their motivation was significantly higher when they used AI-featured methods only in Attention and Confidence for the essay activity. Qualitative findings describe how AI features motivated learners based on the different types of scaffoldings. Additionally, how the AI features are related to each ARCS model categories are summarized. The results suggest strategies for optimizing the design of AI-featured scaffolds to motivate learners in STEM fields grounded in the ARCS model. This involves linking learners’ goals and interests to real scientific applications of AI by expanding ‘Relevance’ and adjusting learning requirements to strengthen ‘Confidence’ and ‘Satisfaction’.

This research investigates the design, development and implementation of Multiple Learning Analytics Dashboards (MLADs) with the goal of enhancing data-driven decision-making among teachers in primary education. The study presents a Design-Based Research (DBR) approach to ensure the iterative development and refinement of MLADs through collaboration with educational professionals. The research involved four key steps in the presented DBR approach: 1) identifying the requirements through stakeholder interviews; 2) generating design ideas through brainstorming sessions and prototyping; 3) collaboratively designing and developing MLADs and conducting usability testing to gather feedback; and 4) implementing the MLADs in real-world educational settings. This paper outlines the research methodology, the participants involved, and the progress made toward developing these Learning Analytics Dashboards (LADs). The outcomes highlight the creation of tailored MLADs for teachers across multiple municipalities, as well as the lessons learned from real-world implementation. Additionally, we analyze user feedback from teachers regarding the dashboard’s clarity, navigation, functionality, and design, providing a comprehensive view of the dashboard’s usability and areas for future enhancement.

Background Briefly describe the educational challenge, the relevance of AI and AR, and why improving practical skills learning is important. Purpose/Objective State the primary aim of the study (to investigate the impact of AI and AR on practical skills learning) and any secondary aims (motivation, engagement, retention). Methods Include study design (mixed-methods, quasi-experimental), participants, sampling, instruments (questionnaires, observations, interviews), intervention (AI + AR), and analysis methods. Results Summarize the key findings: significantly higher post-test scores in the AI/AR group, improved motivation, engagement, satisfaction, and stronger 3-month retention relative to controls. Conclusion Provide a concise interpretation that AI and AR effectively enhance practical skill acquisition and have important implications for education and training.

Students’ socioeconomic background is a strong predictor of academic achievement. Likewise, teaching quality is considered an important prerequisite influencing students’ educational opportunities and their academic achievement and affective outcomes. Empirically, there is still a need for research on these presumed relations. Especially on the associations between younger students’ perceptions of teaching quality and students’ mathematics achievement and mathematics confidence. Drawing theoretically on educational effectiveness research, this study investigated relationships between aspects of teaching quality (classroom management and instructional clarity) and mathematics achievement and mathematics confidence. These relationships were explored in a secondary analysis of students’ perceptions of their teacher’s activities and instructions as indicators of teaching quality. The rationale is that students experience and perceive the teacher’s actions in the classroom, and they may be influenced differently. Aggregating students’ perceptions at the classroom level will decrease the bias of individual ratings. The sample comprised grade four data from the Swedish 2019 cycle of the IEA Trends in Mathematics and Science Study (TIMSS) (N = 3,965). Multilevel structural equation modelling was used to keep classroom variation separate from individual variation when exploring associations between classroom management and instructional clarity and mathematics achievement, and mathematics confidence. Results showed that at the classroom level, mathematics achievement is strongly related to aggregated classroom management. At the individual level, classroom management was associated significantly with mathematics achievement, and instructional clarity with mathematics confidence. Additionally, teaching quality could be beneficial for students with lower SES backgrounds, as findings indicated an achievement gap between classrooms in Sweden. Limitations and implications are discussed.

Understanding nanoscale phenomena poses significant challenges for students because it requires reasoning about emergent behaviours that are invisible and highly abstract. Agent-based models (ABMs) can function as embodied learning environments, enabling students to manipulate individual agents and observe system-level outcomes while engaging embodied processes such as perspective taking, causal inference, and motor-supported thinking. In this study, two treatment conditions were developed: (a) working with ABMs and (b) watching visualization videos. The aim was to investigate whether ABMs are associated with greater engagement in embodied reasoning strategies than videos, and whether such engagement is associated with conceptual understanding in nanoscience education. Twenty-seven undergraduate students were assigned to one of the two conditions, and their conceptual understanding was assessed through pre- and post-tests alongside process data. Results showed that ABM students demonstrated greater gains in explanatory knowledge, knowledge transfer and engaged more frequently in embodied reasoning strategies. Beyond nanoscience, the findings illustrate how technology-enhanced instructional designs that leverage embodied processes—such as gesture, spatial transformation, and active manipulation—may support students’ reasoning about complex scientific phenomena across STEM domains.

Mobile augmented reality (MAR) provides learners with an immersive and interactive experience. However, few studies have applied situated learning theory (SLT) to the design of MAR in educational contexts, and even fewer have explored its use in the learning of Tang poetry. This study aimed to address this gap by designing and developing a mobile augmented reality application (app) based on situated learning theory (SLT-MAR) to support primary school students in learning Tang poetry. The study involved 120 4th-grade students from a public primary school in central China. Using a QUAN-qual sequential mixed-methods experimental design, this study examined the effectiveness of the SLT-MAR app. The results indicated that students using the SLT-MAR app outperformed students in the MAR and traditional classroom learning (TCL) groups in terms of Tang poetry achievement, motivation, and technology acceptance. In addition, semi-structured interviews revealed that students perceived the SLT-MAR app as enhancing their motivation and found it beneficial to their Tang poetry learning. The study discusses both theoretical and practical implications, contributes to the literature on integrating SLT with AR technology in educational settings, and provides insights for future research and practice.

This design-based research study examines the implementation of inclusive pedagogy in an English as a Foreign Language (EFL) classroom in a Chinese elementary school, integrating the Teaching Proficiency through Reading and Storytelling (TPRS) method within the Multi-Tiered System of Support (MTSS) framework. Addressing a critical gap, it explores how inclusive pedagogy can be effectively applied within China’s exam-oriented education system, where such practices are often misunderstood or underutilised. Over three years, iterative cycles of planning, action, development, and reflection refined teaching strategies and assessed their impact on diverse learners. Data were collected through teacher field notes, classroom videos, parent and peer-teacher feedback, and student assessments, including standardised tests and reading evaluations using the Oxford Reading Criterion Scale. The findings indicate that inclusive pedagogy significantly enhanced students’ English proficiency, engagement, and sense of belonging, benefiting high-, mid-, and low-performing students alike. Additionally, the study highlights the transformative role of teacher collaboration and parental involvement in overcoming cultural and systemic barriers. By providing empirical evidence of the effectiveness of inclusive pedagogy in a non-Western context, this research offers valuable insights for educators and policymakers aiming to implement inclusive practices in diverse educational settings. The findings underscore the importance of evidence-based, culturally responsive approaches to inclusive education that support all learners.

Design-based research (DBR) is a widely adopted methodology in the learning sciences. As an alternative to the conventional experimental studies in controlled lab settings, DBR recognizes the intricate dynamics of the naturalistic educational settings and distinguishes itself through well-defined contexts and iterative design cycles that generate findings to inform both theory and practice. A significant advancement in DBR is Sandoval’s introduction of conjecture mapping (CM) in 2004 and subsequently refined in 2014. CM provides a visual framework to elucidate the DBR designs, with the primary objective of identifying the most critical features of a learning design. This paper reports on a case of adopting CM in a three-cycle DBR study of Character Education Learning through Digital Storytelling in Primary 5 classrooms in Singapore. It refines the conjecture formulation and introduces a generic conjecture statement to reinforce the robustness of DBR design logic. Design principles, intervention evidence, and complementary evidence are incorporated as new components to strengthen the links between DBR and CM frameworks. Additionally, it enhances the visual notations of the map to systematically distinguish conjectures by their status and prominence within each cycle. This paper exemplifies the application of CM. It contributes to the ongoing discourse on CM, while advancing the discussion of DBR as a meta-methodology and proposing future directions for DBR and CM research.

A review of the literature reveals a discrepancy about what type of task with concept maps is the most effective for individual learning. Furthermore, to date, no research has compared these tasks in individual and collaborative learning contexts. This paper explores the influence of the different tasks on learning, involving concept maps and summaries. The participants were 226 undergraduates who were randomly assigned to 12 experimental conditions. Two independent variables were considered: the knowledge representation task (fill-in-the-blanks concept map, sort a shuffled concepts-provided map, self-construct a map, write a summary) and the structure of the activity (individual + collaborative, collaborative + individual, and fully individual). In addition to the evaluation of comprehension and delayed recall, 4195 verbal exchanges during the collaborative activities were recorded and analyzed. Results confirm an interaction between the type of task and the structure of the activity. The students who self-constructed complete concept maps and then discussed them in pairs obtained better learning results than those in other conditions. Verbal interaction was much more dialogic in this type of task, with a significantly greater proportion than in the other conditions of in-depth exploratory talk episodes, and a lower proportion of cumulative talk. However, the fill-in-the-blanks and shuffled-concepts conditions provided a greater proportion of superficial exploratory talk, and the collaborative summary condition generated a greater proportion of non-dialogic talk fragments. The findings are discussed in the context of the ICAP (Interactive, Constructive, Active, and Passive learning) framework, cognitive load theory and the sociocultural perspective on dialogic learning.

In an increasingly competitive educational landscape, understanding the determinants of academic success is imperative. While prior research has identified epistemic curiosity as a predictor of academic achievement, the psychological mechanisms through which curiosity cultivates internal resources—such as grit and learning flow—remain underexplored. Drawing on Psychological Capital Theory and Self-Determination Theory, the present study investigates how epistemic curiosity promotes grit and learning flow, which together constitute a serial mediation pathway leading to academic success in a large sample of Chinese adolescents. Utilizing data from 17,352 students across 36 primary and secondary schools in 21 provinces in China (age = 9–18, M = 14.1, SD = 2.9), we employed established measures such as the Epistemic Curiosity Scale, Grit-S, and Study-Related Flow Inventory. Our findings reveal that: (1) epistemic curiosity, grit, learning flow, and academic performance are significantly and positively correlated; (2) learning flow and grit may potentially serve as partial mediators in the relationships between epistemic curiosity and academic performance; (3) grit and learning flow point to a potential sequential mediation process connecting epistemic curiosity to academic performance. This research advances the theoretical understanding of curiosity-driven psychological resource development and offers valuable implications for educational policy and practice, emphasizing the importance of nurturing these psychological traits to enhance student performance.

Research on online problem-based learning—and computer-supported collaborative learning at large—has mostly focused on either the order of group members’ interactions (using time-oriented methods) or the co-occurrence of interactions (using network methods) within the same collaborative episode, while work on longitudinal dynamics has so far been lagging. In this study, we implement a novel method that combines the advantages of both approaches: the relational and temporal dimensions, which is temporal network analysis. Additionally, to capture changes at different temporal scales, we use sequence analysis and multilevel growth models to study how interactions and patterns unfold across time. Our results showed that students who used interactive socioemotional or regulated constructive patterns were more productive in terms of cognitive and knowledge productivity. Explicit group regulation was infrequent and emerged in response to challenges, questions, or disagreements, often with teacher support. Most groups settled into stable regulatory patterns early on, with limited change over time, and transitions—when they occurred—were usually between similar patterns. Our results also suggest that regulation does not naturally improve with time alone, underscoring the importance of early, targeted instructional support to foster more productive regulatory approaches.

Since the beginning of computer-supported collaborative learning (CSCL) research, collaborative writing has been playing a pivotal role as a tool for learning and knowledge construction. In the study presented here, we ask to what extent large language models may not only assist individuals in their writing processes but also serve as a collaboration partner. For this purpose, we analyzed the writing process of individuals supported by ChatGPT. We introduce the use of recurring n-grams as a means for textual uptake, that is, the extent and granularity with which human writers adopt and adapt artificial intelligence (AI)-generated text. On the basis of the overlaps between the ChatGPT output and participants’ final texts, we identified clusters of text reproducers, integrators, and reconstructors. Participants in these clusters differed not only in their subjective contributions and authorship but also in their prior use of ChatGPT and their affinity toward technology interaction. Referring to the conceptualization of interindividual interactions as uptake events, we suggest that n-grams are adequate means to analyze the uptake process in AI-supported human writing. Our findings show that AI-supported writing comprises distinct uptake patterns that differ systematically in the degree of textual reuse and perceived authorship, thereby revealing varying modes of engagement in human–AI co-writing, ranging from passive uptake of AI-generated text to more active and integrative forms of collaboration.

The formation of effective collaborative programming groups is vital for collaborative knowledge innovation. Previous research has predominantly examined the influence of group composition approaches from a computational perspective, yet there remains a limited resolution of their real-world educational impacts. This study offers empirical insights into the effects of homogeneous versus heterogeneous groups on student performance within collaborative programming contexts. The group composition system was established using a genetic algorithm, with the inclusion of socio-emotional competence, learning styles, and academic achievement. A total of N = 478 students aged between 13 and 15-years-old voluntarily participated in the study and were divided into 42 heterogeneous groups (n = 166), 40 homogeneous groups (n = 163), and 36 random groups (n = 149) with a group size of four. All participants were subjected to identical pedagogical conditions under a double-blinded study design. Collaborative programming performance was assessed both summatively and formatively, incorporating multi-source evidence from teacher observations, student self-reports, and peer evaluation scores. The results indicate that heterogeneous groups notably outperform homogeneous groups and random groups across most measurements. Implications for implementing collaborative programming in real-world classroom settings are provided.

As smart technologies become part of daily life, families face new opportunities and challenges in learning together. This paper introduces FamiData Hub, a speculative computer-supported collaborative learning (CSCL) prototype that supports families in building critical data literacy within smart homes. Through workshops with 17 families, the study explores how collaborative learning emerges through interaction, storytelling, and shared problem-solving, with family roles shifting dynamically. The findings challenge traditional adult-to-child teaching models, proposing instead a multidirectional learning space where anyone—including children and digital tools—can be the “more knowledgeable other.” The study highlights the value of family centered, socially embedded approaches to critical data literacy and offers insights for designing intergenerational CSCL systems to foster critical data literacy.

Mutual engagement, the dynamic process through which collaborators reciprocally take up and sustain one another’s ideas and actions, is crucial to collaborative problem solving (CPS). However, existing research has yet to fully specify concepts or methodologies needed to capture these dynamic characteristics. This gap highlights the need to examine how these patterns evolve across different CPS phases to inform more sophisticated instructional strategies that enhance collaborative learning. This exploratory study integrates multimodal and content analyses to examine phase-sensitive patterns of mutual engagement in small teams. In total, 28 college students participated in video-recorded CPS activities across four distinct phases. The findings revealed that high-performing teams displayed structurally complete elaborative sequences, in which invitations to contribute were taken up, elaborated, and reciprocated. Low-performing teams, by contrast, exhibited fragmented sequences that failed to return to elaboration. These interactional differences co-occurred with distinct multimodal signatures. High-performing teams exhibited greater interest, less frequent neutral emotions, and early posture synchrony, patterns that were especially pronounced during the ideation phase. Conversely, low-performing teams showed lower interest, persistent neutral emotions, and late, reactive posture synchrony. The findings elucidate the socio-cognitive characteristics of mutual engagement and demonstrate the potential for integrating emotional and behavioral indicators for a richer understanding. These insights can inform the design of instructional scaffolding and phase-sensitive support systems to enhance successful collaborative learning.

Productive collaborative discourse requires students to continuously advance ideas, often through the creation, modification, and integration of digital artifacts in a communal space. Without these processes, ideas remain isolated, fragmented, and unable to advance shared understanding. To support such discourse processes, this study proposes a knowledge synthesis (KS) intervention to facilitate a process of creating knowledge syntheses from ideas represented in digital artifacts and then leveraging these knowledge syntheses, represented in new digital artifacts, to deepen student collaboration. To examine the enactment of this intervention in a graduate-level course, we asked: What were the key characteristics of students’ knowledge synthesis artifacts? How did student groups use the synthesis artifacts during their discourse? To what extent did the synthesis artifacts facilitate collaborative discourse? We analyzed multiple data sources—including student-created synthesis artifacts, perception data, classroom video recordings, and co-constructed group artifacts—using a combination of descriptive, content, and interaction analyses. Findings revealed diverse approaches to knowledge synthesis and showed that synthesis artifacts facilitated discourse progression, fostered a range of knowledge practices, and supported the evolution of group artifacts. By promoting knowledge synthesis and examining its role in collaborative discourse, this study contributes to computer-supported collaborative learning (CSCL) by advancing the theoretical understanding of knowledge synthesis and offering pedagogical strategies for supporting this practice in classrooms.

In collaborative learning game environments where competition and collaboration coexist, conflicts among students are not uncommon. While conflicts of ideas and opinions are prevalent during collaborative learning, they are often perceived as elements to be avoided. One of the main concerns about conflict is its ability to trigger negative emotions, such as anger, which can compromise effective peer interaction, collaborative learning, and, in turn, diminish the quality of group discussions. However, this raises the question of whether anger always negatively affects collaborative learning. Most studies on negative emotions are related to test anxiety or boredom, while the impacts of emotions such as anger on learning are less explored. Especially within computer-supported collaborative learning (CSCL), there is limited research on how anger impacts students’ collaborative activities and learning. To address these issues, this paper aims to explore the potential relationship between anger and its impact on students’ collaborative discourse in a hybrid game-based simulation. Our findings suggest that anger has the potential to facilitate diverse and productive collaborative discussions. Students, driven by their anger, delved deeper into game mechanics, linked concepts to real-life situations, and employed various forms of logical reasoning to substantiate their opinions. However, the moment a student exhibited “tilting“ behavior, the quality of their collaborative discussions plummeted. Our findings provide important preliminary insights into the concept of “tilting” within immersive collaborative learning games and how it may manifest; they also offer guidance on the timing for educators’ intervention in collaborative discourse when anger arises among students.

Background: Online hate speech on social networks and the Internet is an increasingly pervasive phenomenon to which both children and adolescents are exposed. Objective: Our study’s main objective was to ascertain whether collective intelligence can improve their handling of hate speech. Methods: We conducted the study on the Collective Learning platform, comparing results between three groups of Spanish adolescents aged 15–16 years. The groups were of different sizes: one large group (G1, n = 123) and two smaller groups (G2, n = 18; G3, n = 23). Results: The experiment showed that the conditions for the emergence of collective intelligence were met within the large group (G1) but not in the two small groups (G2 and G3). The large group, as a collective, acquired capacities to deal with hate speech; however, this did not occur in the two smaller groups. Conclusions: Our study explains how the emergence of collective intelligence in online environments helps group members acquire a series of competencies. In particular, collective intelligence can help adolescents learn to deal with hate speech.

The increasing availability of multimodal sensing technologies has opened new avenues for studying human interactions. However, there remains a lack of systematic synthesis regarding which multimodal metrics are most predictive of productive collaborations. This study addresses this gap by conducting a systematic literature review of 163 studies published since 2000. Grounded in the theoretical framework of multimodal collaboration analytics (MMCA; Schneider et al., 2022), we examine how different data modalities—verbal, gaze, body, head, log, and physiological—are used to assess collaboration. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Liberati et al., 2009), we categorize studies on the basis of the types of collaborative indicators, the metrics extracted from multimodal data, and the methods used to establish relationships between them. We find several gaps, including an over-representation of lab-based studies with small sample sizes, reliance on simplistic individual or group synchrony metrics, and a lack of standard indicators for collaboration. We discuss related Grand Challenges for MMCA, including scaling up research through field-based studies, developing interpretable models that contribute to theory, computing sophisticated sensor-based metrics that better capture the temporal dynamics of interaction, and designing interventions that support collaboration using fine-grained, high frequency sensor data.

Collaborative learning deepens understanding by elaborating knowledge and facilitating memory-related information processing through interactions with others. In computer-supported collaborative learning (CSCL), mechanisms identified in collaborative learning are scaffolded through tools such as group awareness and scripted collaboration. While collaborative learning is considered effective, it remains unclear how older adults learn in collaborative environments using concept maps, and how cognitive decline may hinder their learning. Therefore, this study investigates differences between younger and older adults in collaborative learning with concept maps, focusing on learning performance, concept map performance, and the collaborative learning process. Learning performance was assessed using test scores, concept map performance through concept map evaluations as a tool for externalizing knowledge, and the collaborative process using the Interactive-Constructive-Active-Passive (ICAP) framework, which captures cognitive engagement. Results showed that younger adults had higher learning performance than older adults, while older adults showed no significant improvement, indicating a lack of learning gain. Similarly, younger adults outperformed older adults in concept map performance, and no improvement was observed in older adults for concept map scores. This suggests that older adults found it more difficult to elaborate knowledge, such as integrating new information. Regarding the collaborative learning process, younger adults were more likely to engage at the active, while older adults showed higher engagement at the constructive and interactive levels. Epistemic network analysis (ENA) revealed stronger connections between constructive and interactive behaviors in younger adults, and between active and interactive behaviors in older adults. These findings suggest that while younger adults progressively deepen their engagement during collaborative learning, older adults may require the reactivation of memory to engage in elaboration. These results offer insights into designing effective CSCL environments tailored to the learning needs of older adults.

Dialogic education is largely advocated as a means to promote dialogue and reduce polarization. Chatbots based on large language models (LLMs) carry the potential to scale dialogic education by serving as conversation partners and sustaining a dialogic space on various topics. They combine human-like conversational abilities with machine patience. To explore this potential, we fine-tuned an LLM-based chatbot, LlamaLo, using a corpus of productive discussions. We analyzed ten discussions with LlamaLo on contentious topics, such as liberalism and cultural appropriation. Our findings show that LlamaLo effectively opens dialogic spaces by questioning interlocutors’ assumptions, presenting alternative perspectives, and providing relevant knowledge. However, challenges, such as negative tone and bias, could undermine the dialogic space and should be addressed computationally and pedagogically. We conclude that dedicated LLM-based chatbots have the potential for enhancing dialogic education and enabling seamless scripting responsive to real-time needs.

Understanding how multiple dimensions of learning engagement co-develop during collaborative programming remains a critical challenge. Drawing on the four-dimensional engagement framework encompassing behavioral, cognitive, emotional, and social components, this study employs multimodal learning analytics (MMLA) to investigate the dynamic interplay among engagement dimensions, prior knowledge, and leadership type in a university-level collaborative programming course. Group-level ICAP (Interactive, Constructive, Active, Passive) modes were coded from multimodal interaction data, while learning engagement was assessed via integrated behavioral, cognitive, emotional, and social indicators. Findings reveal that prior knowledge supports individual task execution but contributes to collaborative engagement only under strong leadership. The three core dimensions of learning engagement—behavioral, cognitive, and social—were found to be significantly interrelated, reflecting a tightly coupled system of action, thinking, and peer interaction. In contrast, emotional engagement showed weak or inconsistent correlations with the other dimensions. Furthermore, interactive discourse fostered richer engagement and higher achievement, with group formation strategies moderating these effects through the interplay of prior knowledge and leadership type. While ICAP modes and overall learning engagement were strongly correlated—partly reflecting overlapping cognitive components—ICAP captured discourse patterns, whereas engagement scores aggregated multidimensional indicators across the task, suggesting complementary rather than redundant constructs. These findings advance engagement research by integrating discourse classification and multidimensional profiling, offering practical guidance on group formation, scaffolding, and real-time engagement monitoring to enhance collaborative programming pedagogy.