Frontiers in Education: Digital Learning Innovations

IntroductionThe application and influence of artificial intelligence (AI), and specifically Large Language Models (LLMs), in educational processes is widely discussed. However, there remains a gap in research on using LLMs as peer-like contributors in collaborative learning contexts.MethodsThis article reports a mixed-methods quasi-experimental study investigating how positioning ChatGPT as a peer-like feedback provider shapes student-teachers’ learning and collaboration during group lesson-design activities. The study employed a counterbalanced crossover structure for knowledge assessment and a sequential two-task design for authentic artifact production. A total of 102 teachers in training (M_age = 38.87, SD = 8.01), organized into 21 groups, completed two authentic design tasks within a single session.ResultsAcross the session, students progressively adapted to AI interaction, refining how they queried the model and how they evaluated and integrated its suggestions. Results indicate a Post-Withdrawal Sustained Performance (PWSP) effect: improvements observed during AI-available phases were not followed by a detectable decline in the immediately subsequent AI-withdrawn phase within the study timeframe. This pattern was clearest for technology-related knowledge and was consistent with stable artifact quality after AI removal. While ChatGPT support increased efficiency and contributed to technology-focused insights, qualitative evidence also pointed to tensions, including reduced peer-to-peer idea-building in some groups and concerns about creativity.DiscussionOverall, the findings suggest that integrating LLMs as a feedback team-mate can support collaborative design work without immediate post-withdrawal performance costs, particularly when learners are scaffolded to engage critically with AI output rather than accept it unreflectively. These results carry implications for the design of AI-enhanced collaborative activities, highlighting the need to balance AI efficiency gains with sustained opportunities for authentic peer dialogue.

Despite the proven efficacy of corpus linguistics in language education, its adoption in English for Specific Purposes (ESP) classrooms remains limited due to the technical complexity of corpus tools, particularly in non-English departments. Addressing this “usability gap,” this study evaluates the effectiveness of the Digital Corpus-based Instruction (DCI) model, a novel instructional framework that synergizes corpus data, QR-code technology, and Mobile-Assisted Language Learning (MALL). The study aims to determine if providing corpus data through familiar mobile interfaces can improve lexical competence among non-English majors in low-resource settings. Employing a quasi-experimental design with a convergent parallel mixed-methods approach, the study involved 30 non-English-major undergraduates in an Indonesian university setting. Quantitative data from standardized pre- and post-tests, focusing on vocabulary matching, collocation awareness, reading, and professional writing, were triangulated with qualitative insights from classroom observations, Focus Group Discussions (FGDs), and interviews to capture behavioral and cognitive shifts. The results revealed a statistically significant improvement in the experimental group (p < 0.001), with a very large effect size (Cohen's d > 1.93). Significant gains were observed in collocation awareness (from 2.30 to 5.13) and professional writing accuracy (from 7.83 to 10.10). Qualitative findings corroborated these metrics, indicating a pedagogical transformation from passive learning to active, data-driven inquiry and increased professional confidence. This study contributes to the field by demonstrating that DCI effectively reduces the cognitive load of corpus analysis, offering a scalable, pedagogically viable solution for enhancing ESP competence in higher education contexts.

In the era of digital transformation, the application of remote sensing (RS) data in geography education and its effective integration into the teaching and learning process has become one of the pressing educational challenges, particularly in fostering students’ spatial thinking competencies. The purpose of the study is to identify the possibilities of incorporating RS data into school geography, to develop an effective teaching methodology based on its application, and to test it in practice. The research employed a review of international publications, surveys, correlation and comparative statistical analysis, modeling, analysis of regulatory documents, and pedagogical experimentation. The survey involved 124 geography teachers from different regions of Kazakhstan, while the experimental study included 52 eleventh-grade students from one secondary school selected through random sampling (26 students in the experimental group and 26 students in the control group). The survey results revealed that 62% of teachers use satellite imagery during lessons; however, 69% of them utilize RS data primarily for visualization purposes. The results of the pedagogical experiment demonstrated that the average academic achievement of students in the experimental group increased by 18.2%, whereas the improvement in the control group was 3.6%. Furthermore, the use of RS data positively influenced students’ geospatial thinking, research skills, and analytical abilities. The study further established that the structural-content model of RS-based teaching materials developed as a result of the research can contribute to improving existing methodologies. The proposed model is designed to modernize school geography curricula, enhance teachers’ methodological capacity, improve students’ learning outcomes, and promote geospatial literacy. The findings of the study demonstrate that the systematic integration of RS data into school geography education can serve as a scientific and methodological foundation for modernizing educational content, fostering students’ contemporary scientific and technological competencies, and improving the overall quality of geographic education.

IntroductionGenerative artificial intelligence (GenAI) is rapidly reshaping higher education. However, evidence remains limited regarding its pedagogical utility and learning benefits in university dance learning environments.MethodsThis study employed a quasi-experimental design with 60 university students who were randomly assigned to either an experimental group using a GenAI-based teaching tool (GEN Dance) or a control group using a conventional multimedia tool. GEN Dance supported real-time, interactive dance learning activities.ResultsThe GenAI-supported condition (GEN Dance) demonstrated statistically significant advantages over the conventional multimedia condition across all three assessed learning-related domains.DiscussionThese findings suggest that GenAI can enhance learning outcomes in higher education dance contexts and support more interactive instructional experiences. This study extends the emerging literature on GenAI-enabled teaching and provides empirical evidence for the integration of GenAI tools in university dance learning environments.

IntroductionModern e-learning platforms are increasingly using personalized elements to better address diverse learners and enhance learning outcomes. To investigate the effects on the development of students' metacognitive thinking, this study develops an adaptive e-learning environment based on two personalized factors: learning styles and knowledge levels.MethodsThe study, which used a quasi-experimental design with undergraduate students, revealed that there was no obvious effect on metacognitive growth when content was personalized for learning styles.ResultsThis reflects the increasing concern among academics about the usefulness of learning styles in personalized learning. On the other hand, metacognitive engagement was found to be strongly influenced by prior knowledge.DiscussionThe findings imply a shift to evidence-based adaptive design, arguing that to successfully promote higher-order cognitive abilities, e-learning environments should give priority to empirically verified factors like knowledge expertise.

IntroductionILS are AI-enhanced educational technologies increasingly implemented in K-12 education.MethodsWe analyzed 72 peer-reviewed articles from Scopus and WoS (2014–2024) following PRISMA 2020 guidelines using systematic evidence synthesis and thematic analysis.ResultsEighty-nine percentage of overall positive outcomes; ITS dominate (46%); regional and disciplinary variations identified.Discussion/conclusionCritical effectiveness factors identified; gaps in CT development and non-STEM research noted; longitudinal research needed.

This study explores the pedagogical bases of ICT integration in pre-service chemistry teacher education within the “University-School Bridge” framework. The research aimed to examine how digital technologies and pedagogical strategies collectively influence the professional development of future chemistry teachers. Using a quantitative quasi-experimental longitudinal design with pre-, mid-, and post-tests and statistical analysis via ANOVA, the study evaluated changes in students’ chemistry knowledge, digital literacy, and pedagogical competencies. Participants included 34 pre-service teachers and 224 secondary school pupils. The finding revealed significant improvement across all variables, with large effect sizes for overall performance (η2 = 0.65), and pedagogical methods (η2 = 0.41). The integration of digital tools such as Moodle, Kundelik.kz, and Quizlet, combined with classroom-based mentoring, was associated with higher levels of reflective practice and professional readiness. The results also support the University-School Bridge model as being associated with improved alignment between theoretical preparation and school-based practice. These outcomes highlight the importance of continuous, data-driven, and practice-oriented, adaptive, and research-minded educators.

IntroductionBalancing potency and feasibility is essential for implementing digital technologies in school settings, where time and attention are limited. We introduce precision streamlining—a novel intervention design strategy that reduces overall duration while preserving effectiveness through targeted personalization. Instead of delivering all content to all students, this approach uses personalization to reduce the total material delivered—focusing only on what is most impactful for each individual.MethodsWe applied this strategy to an existing attention training program for adolescents, reducing its length by 45%. We then tested the streamlined version across 13 public high schools.ResultsIn Study 1, the shortened intervention produced significant improvements in emotional regulation, growth mindset about attention, self-efficacy, and classroom focus. Study 2 replicated these findings in a larger and more diverse sample. Study 3 entailed a direct comparison of students who either received the original, longer version or the shortened version (N = 1715). While the original intervention produced stronger effects on self-efficacy and emotional regulation, the shortened version still yielded significant gains across all four outcomes.DiscussionThese findings support precision streamlining as a promising design strategy. They also demonstrate that a brief attention training program can offer meaningful benefits for student learning and well-being—while remaining feasible to implement in time-constrained school settings.

The emergence of Large Language Models (LLMs) has opened new possibilities for language learning through conversational interaction with chatbots. Yet, little empirical evidence exists on how students experience such interactions and how corrective feedback should be provided. Research suggests that immediate corrective feedback is generally more effective than delayed feedback. Nevertheless, learners' perception of this effectiveness and their preferences for feedback timing, particularly in the domain of Computer-Assisted Language Learning (CALL), remain underexplored. This study investigates the feasibility of providing immediate feedback and examines the impact of feedback timing on user experience and grammar learning gains in English. An in-the-wild experiment was conducted with 66 L2 English learners, who integrated chatbot sessions into their English course as an extracurricular activity over one semester. Participants were randomly assigned to two groups receiving feedback either during or after the conversation. Findings reveal no significant difference in learning gains, but immediate feedback enhanced user experience, leading to overall positive perceptions of the chatbot. Additionally, we explore users' perceptions of the chatbot's social role and personality, offering a roadmap for future enhancements. These results provide valuable insights into the potential of LLMs and chatbots for language learning.

IntroductionThis study investigated the relationships among gifted behavior, Generative AI (GenAI) anxiety, and learning motivation among gifted EFL university students in Saudi Arabia, with an additional focus on the mediating role of GenAI anxiety and the moderating effect of gender.MethodsUsing a quantitative correlational design, data were collected from 304 purposively selected undergraduates classified as gifted based on institutional records and psychometric confirmation using Gifted Rating Scales. The participants answered the verified instruments of gifted behavior, GenAI anxiety, and academic motivation.ResultsAnalysis of structural equation modeling showed that gifted behavior was associated with lower GenAI anxiety and higher motivation, and that GenAI anxiety had a significant negative association with motivation. The mediation analysis indicated that the relationship between gifted behavior and motivation was partially mediated by GenAI anxiety. Gender was a significant moderator of the relationships between gifted behavior and motivation, as well as between GenAI anxiety and motivation, with both associations being stronger among females.DiscussionThe findings contribute to the growing body of research on the psychological impact of AI implementation in EFL learning, suggesting that gifted learners’ responses to GenAI technologies are characterized by distinct affective and motivational patterns. The research provides theoretical insights into giftedness and technology-related emotions, providing pedagogical implications for creating AI-based EFL learning environments that are sensitive to learners’ cognitive and affective profiles.