In recent years, educational robotics has gained ground in educational policy around the world, and primary education is no exception. However, there has not yet been a thorough synthesis of methodologically appropriate empirical research on the effects of robotics upon cognitive performance among primary school students, which this paper attempted to do. Following literature screening, a total of eight studies published between 2018 and 2022 with a sample size of 567 children met inclusion criteria and were meta-analyzed. Resultantly, a medium aggregate effect size in favor of robotics experiments emerged (standardized mean difference of .641), which was significantly higher compared to non-robotics learning (p <.01). No between-study heterogeneity was detected. Subgroup analysis revealed a slightly larger overall effect for interventions on first- to third-graders rather than those in grades 4-6. Additionally, the analysis indicates that in order to enhance cognitive abilities in primary students, robotics interventions should be no longer than four weeks and involve robot construction. Based on the findings, implications, and suggestions are outlined for future research and practice.

The aim of this exploratory case study is to investigate the impact of the pandemic as an unexpected situational variable on university instructors’ perceptions and attitudes towards technology integration, as well as their foresight about the future of education in post Covid-19 era. The data for the study came from autobiographies, narratives, and opinionnaires. The findings revealed that the degree of familiarity with educational technology and eagerness to integrate technology into education made a difference in academicians’ adaptation to the new mode of delivery. As for their predictions for the future, an increase in the use of educational technology not only in teaching, but also in testing and assessment is expected. Participants also emphasized a need to enhance the infrastructure to avoid any further technical issues and offer continuous development opportunities for teachers and students to become familiar with new technologies.

Technology integration into learning is essential to supporting educational reform. On the other hand, the relationship between self-regulation (SR), technology integration self-efficacy (TISE), and technological pedagogical content knowledge (TPACK) has yet to be thoroughly studied. This study investigated preservice elementary teachers and the connection between SR, TISE, and TPACK. A quantitative approach and a survey-based approach were both utilized in the research project. The research was carried out at one of Indonesia's universities, and the data collected were from 224 preservice elementary teachers in their fourth year through a questionnaire. According to the findings, preservice elementary teachers' SR, TISE, and TPACK levels were above average. Preservice elementary teachers scored the highest on planning capability (PC), monitoring and controlling skills (MC/CC), and making others use computer technologies self-efficacy (MUCTSE). In contrast, they scored the lowest on information and communication technology (ICT). Besides that, SR and TISE positively and significantly affected pre-service teacher TPACK. In light of the findings, it is of the utmost importance to enhance the competency of preservice elementary teachers in using technology to integrate learning.

This study investigates the impact of external variables, technological pedagogical and content knowledge (TPACK) self-efficacy, and facilitating conditions on teacher adoption of online teaching technology during the COVID-19 pandemic. It employs explanatory research to characterize the effect of external variables on the variables of the technology acceptance model. 240 high school teachers filled out Google Forms survey questionnaires for six research variables and analyzed by the SmartPLS program. The result indicates that attitude and perceived usefulness significantly and positively influence behavioral intention. Perceived usefulness and ease of use have a strong positive effect on attitude. Furthermore, perceived ease of use has a considerable effect on perceived usefulness. Perceived usefulness and ease of use are not significantly influenced by external variables. Facilitating conditions significantly positively affect behavioral intention, whereas TPACK self-efficacy negatively affects behavioral intention.

This study aims to analyse the relationship between critical thinking and the learning experience provided by instructors through active learning models, specifically Project-based Learning (PjBL) and Simulation-based Learning (SBL), to the potential achievement of academic performance in undergraduate students. The main analysis technique employed in this research was logistic regression, with additional analysis techniques including discriminant validity, EFA, as well as Kendall’s and Spearman’s correlation, serving as a robustness check. The results of this study indicate significant correlations and effects of critical thinking (CT) on academic performance. Higher levels of CT are associated with a greater likelihood of achieving academic excellence, as indicated by the cum laude distinction, compared to not attaining this distinction. Experiences of receiving PjBL (0.025; 6.816) and SBL (0.014; 14.35) predicted the potential for improving academic performance to reach cum laude recognition, relative to not achieving this distinction. Furthermore, other intercept factors need to be considered to achieve cum laude compared to not achieving cum laude. We recommend that policymakers in higher education, instructors, and others focus on enhancing critical thinking and utilizing both Pub and SBL as learning models to improve students’ academic performance.

The present study aimed to improve the speaking skills of university students at the B1 level who presented limitations in their oral competence. An educational methodology based on designing and implementing an application adapted to the Common European Framework of Reference was developed and applied to boost language performance. A case study was used to conduct the two stages of this research; the former had to do with a control group where intervention was carried out using non-probabilistic sampling with students of the Computing Faculty; a pretest was applied to test the knowledge acquired in their classroom sessions during the first quarter in 2020. The second process was tracking an experimental group, which was assessed after implementing the developed methodology using the app "4skillsweb". A posttest was used to evidence learners' progress during the COVID-19 lockdown, and the results showed improved oral competence in aspects such as grammar and vocabulary, discourse management, pronunciation, and interactive communication, with about 95% confidence in its validation. A qualitative-quantitative methodology was used to determine the influence of the English app. A t-students test was implemented to corroborate the data analysis taken by both groups through SOFTWARE JMP v 11.0.0G.

This article investigates artificial intelligence (AI) implementation in higher education (HE) from experts' perspectives. It emphasises the view of AI's involvement in administrative activities in higher education, experts' opinions concerning the influence of the incorporation of AI on learning and teaching, and experts' views on applying AI specifically to assessment, academic integrity, and ethical considerations. The study used a qualitative method based on an unstructured qualitative interview with open-ended questions. The participants were thirteen individuals currently involved with higher education institutions and had various talents related to AI and education. Findings stress that implementing AI technology in administrative roles within higher education institutions is essential since it cuts costs, addresses problems efficiently and effectively, and saves time. The findings also revealed that AI plays a vital role in learning and teaching by speeding up the learning process, engaging learners and tutors, and personalising learning depending on the learner's needs within an entirely intelligent environment. AI can produce an accurate, objective, and suitable level of assessment. AI aids students in developing a stronger sense of integrity in their academic work by guiding them through AI-powered applications. AI must adhere to ethical laws and policies, ensuring its potential negative aspects are not overlooked or left unchecked.

This comprehensive systematic review delves into the increasing prevalence of integrating chatbots into language education. The general objective is to assess the current landscape of knowledge regarding chatbot utilisation and its influence on three crucial elements: students' skills, attitudes, and emotions. Additionally, the review seeks to scrutinise the advantages linked to incorporating chatbots in foreign language teaching, exploring their potential benefits while considering limitations and potential negative impacts on specific skills or user experiences. Consequently, this research offers valuable insights into the application of chatbots in foreign language education, shedding light on their potential advantages and areas that warrant further exploration and enhancement. The integration of chatbots in language learning, despite certain limitations, generally yields positive outcomes and enhances educational results in students' skills. Its characteristics can also influence a language learner's attitude, impacting factors such as motivation, interest, autonomy in learning, and engagement or even their sense of fun. Additionally, chatbots prove to be helpful in creating emotionally positive learning environments and can contribute to boosting students' self-esteem and self-confidence.

Given curiosity’s fundamental role in motivation and learning and considering the widespread use of digital stories as educational tools from the preschool age, we pursued measuring preschoolers’ curiosity when interacting with digital stories. Using 129 toddlers and preschoolers as a sample, three groups (one for each class) were given different versions of the same digital story to listen to: interactive, non-interactive, and animated. Toddlers' verbal and nonverbal behaviors were utilized to quantify curiosity as a condition brought on by the app. The participants' verbal and nonverbal behaviors were recorded during the digital reading aloud. Every child's data was encoded at one-minute intervals to examine concurrent behavior, and the results were then compiled. The findings show that interactive presentation formats encourage more touching and language use but less noise production and that interaction and the creative use of hot spots in digital illustrations are key elements in piquing viewers' curiosity while contributing to the strengthening of the engagement to the activity and the cultivation of critical thinking, creativity, and imagination.

In an era where diversity and digitalization significantly influence higher education, understanding and adapting to various learning preferences is crucial. This study comprehensively analyzes 394 scholarly articles from 1984 to 2022 using bibliometric methods, providing a dynamic overview of the research patterns in learning styles within higher education. We identified four stages of development during this period: 1984–1995 (Low-interest), 1996–2005 (Early development), 2006–2018 (Development), and 2019–2022 (Intensification). Our analysis highlights that the United States, the United Kingdom, and Australia were the top three leading publishers of research on learning styles in higher education. The results reveal three main topics of publications: educational technology, learning environments, and subject behaviors. This research not only identifies emerging research topics but also underscores the importance of adapting instructional strategies to diverse learning styles to enhance educational outcomes in higher education.

Successfully solving reality-based tasks requires both mathematical and text comprehension skills. Previous research has shown that mathematical tasks requiring language proficiency have lower solution rates than those that do not, indicating increased difficulty through textual input. Therefore, it is plausible to assume that a lack of text comprehension skills leads to performance problems. Given that different sociodemographic characteristics and cognitive factors can influence task performance, this study aims to determine whether text comprehension mediates the relationship between these factors and competence in solving reality-based tasks. Additionally, it examines the impact of systematic linguistic variation in texts. Using an experimental design, 428 students completed three reality-based tasks (word count: M = 212.4, SD = 19.7) with different linguistic complexities as part of a paper-pencil test. First, students answered questions about the situation-related text comprehension of each text, followed by a mathematical question to measure their competence in solving reality-based tasks. The results indicate that: a) Tasks with texts of lower linguistic complexity have a significantly higher solution rate for both text comprehension (d = 0.189) and mathematical tasks (d = 0.119). b) Cognitive factors are significant predictors of mathematical solutions. c) Text comprehension mediates the relationship between the impact of students’ cultural resources and cognitive factors and their competence to solve reality-based tasks. These findings highlight the importance of linguistic complexity for mathematical outcomes and underscore the need to reinforce text comprehension practice in mathematical education owing to its mediating role.

The concept of technological pedagogical content knowledge (TPACK) is presented as a framework that guides how to effectively integrate technologies in the educational environment. Through this model, we investigate the ethical implications related to the use of digital tools in teaching, and we outline the necessary knowledge that educators should have to address these issues of ethics and technology in the classroom. We assess the professional, ethical knowledge of pre-service teachers regarding their use of technologies using a descriptive and exploratory mixed-methods approach. The data for this research come from a Likert-scale questionnaire administered to 616 teacher-training students in Spain, as well as from personal interviews with 411 of them. From these data, we identify four of the eight dimensions of ethical knowledge: professional, ethical knowledge, ethics in the use of technologies, pedagogy for their integration in the classroom, and the use of content specific to the disciplines of pre-service teachers. The results obtained indicate that the preparation of educators with professional, ethical knowledge in training is insufficient, which highlights the need to address this issue in the post-pandemic context of the 21st century. Among the difficulties detected, it should be noted that this study is limited to a European university and a sample chosen for convenience, so it would be advisable to extend the study to other European universities.

 This study examines the research trends of Design Thinking (DT) in education during the period 2014–2024 through a systematic literature review. This study aims to analyze annual publication patterns, implementation across educational levels, research methodologies, authorship distribution, geographical spread, journal type distribution, and key themes from highly-cited publications in DT education research. The results show a significant increase in publications, especially in 2023–2024, reflecting growing academic interest in DT as an innovative approach to developing 21st-century skills. Qualitative research methods dominate, with most studies involving collaborative authorship. DT application was initially focused on higher education but expanded in secondary education while remaining limited in primary education. Asia leads in research contribution, while Africa shows lower output. Publications are distributed across educational, design-focused, and interdisciplinary journals. These findings underscore the importance of cross-disciplinary and global collaboration to accelerate DT adoption equitably. This study recommends strengthening educator training, developing holistic evaluation methods, and expanding quantitative research for more inclusive DT implementation.

This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptions of AI across various academic aspects, such as module outlines, learning outcomes, curriculum design, instructional activities, assessments, and feedback mechanisms. It evaluates the impact of AI on students' learning experiences, critical thinking, self-assessment, cognitive development, and academic integrity. This research used a structured survey distributed to 300 students through Microsoft Forms 365, yet the response rate was 29.67%. A structured survey and thematic analysis were employed to gather insights from 89 students. Thematic analysis is a qualitative method for identifying and analysing patterns or themes within data, providing insights into key ideas and trends. The limited response rate may be attributed to learners' cultural backgrounds, as not all students are interested in research or familiar with AI tools. The survey questions are about AI integration in different academic areas. Thematic analysis was used to identify patterns and themes within the data. Benefits such as enhanced critical thinking, timely feedback, and personalised learning experiences are prevalent. AI tools like Turnitin supported academic integrity, and platforms like ChatGPT and Grammarly were particularly valued for their utility in academic tasks. The study acknowledges limitations linked to the small sample size and a focus on undergraduate learners only. The findings suggest that AI can significantly improve educational experiences. AI provides tailored support and promotes ethical practices. This study recommends continued and expanded use of AI technologies in education while addressing potential implementation challenges.

The field of engineering education is constantly evolving to meet the challenges of technological and societal advancements. Continuous research should be conducted to identify the potential match between the skills that employers seek and the ones engineering graduates gain at college. The purpose of this study is to identify areas of agreement and disagreement between the faculty and employers regarding the skills and knowledge that engineering students acquire during their undergraduate education. The study uses an explanatory sequential design method by employing a questionnaire that was developed based on the Engineering Competency Model (ECM) by the Employment and Training Administration of the USA Department of Labor and responded to by 125 volunteer engineering faculty. Additionally, interviews were conducted with 2 industry professionals to gain deeper qualitative insights. The study found that while faculty mainly stated that students acquire personal effectiveness, academic, and workplace competencies in college, employers disagreed with these perceptions, particularly regarding interpersonal skills, integrity, professionalism, writing, and communication. Additionally, the study found a significant mismatch between faculty and employer assessments of industry-wide competencies, with employers expressing concerns about graduates' preparedness in areas like design, business, and sustainability. These findings suggest significant updates and cooperation with industry experts in engineering curricula and their implementation.

This research aims to systematically review the integration of artificial intelligence (AI) in English language teaching and learning. It specifically seeks to analyze the current literature to identify how AI could be utilized in English language classrooms, the specific tools and pedagogical approaches employed, and the challenges faced by educators. Using the PRISMA-guided Systematic Literature Review (SLR) methodology, articles were selected from Scopus, Science Direct, and ERIC, and then analyzed thematically with NVivo software. Findings reveal that AI enhances English teaching through tools like grammar checkers, chatbots, and language learning apps, with writing assistance being the most common application (54.55% of studies). Despite its benefits, challenges such as academic dishonesty, over-reliance on AI (27.27% of studies), linguistic issues, and technical problems remain significant. The study emphasizes the need for ethical considerations and teacher training to maximize AI’s potential. It also highlights societal concerns, including the digital divide, underscoring the importance of equitable access to AI-powered education for learners of all socioeconomic backgrounds.

Artificial intelligence (AI) has revolutionized higher education. The rapid adoption of artificial intelligence in education (AIED) tools has significantly transformed educational management, specifically in self-directed learning (SDL). This study examines the factors influencing Indonesian higher education students' intention to adopt AIED tools for self-directed learning using a combination of the Theory of Planned Behavior (TPB) with additional theories. A total of 322 university students from diverse academic backgrounds participated in the structured survey. This study utilized machine learning it was Artificial Neural Networks (ANN) to analyze nine factors, including attitude (AT), subjective norms (SN), perceived behavioral control (PBC), optimism (OP), user innovativeness (UI), perceived usefulness (PUF), facilitating conditions (FC), perception towards ai (PTA), and intention (IT) with a total of 41 items in the questionnaire. The model demonstrated high predictive accuracy, with SN emerging as the most significant factor to IT, followed by AT, PBC, PUF, FC, OP, and PTA. User innovativeness was the least influential factor due to the lowest accuracy. This study provides actionable insights for educators, policymakers, and technology developers by highlighting the critical roles of social influence, supportive infrastructure, and student beliefs in shaping AIED adoption for self-directed learning (SDL). This research not only fills an important gap in the literature but also offers a roadmap for designing inclusive, student-centered AI learning environments that empower learners and support the future of SDL in digital education.

Integrating generative artificial intelligence (GenAI) in education has gained significant attention, particularly in flexible learning environments (FLE). This study investigates how students’ voluntary adoption of GenAI influences their perceived usefulness (PU), perceived ease of use (PEU), learning engagement (LE), and student-teacher interaction (STI). This study employed a structural equation modeling (SEM) approach, using data from 480 students across multiple academic levels. The findings confirm that voluntary GenAI adoption significantly enhances PU and PEU, reinforcing established technology acceptance models (TAM). However, PU did not directly impact LE at the latent level—an unexpected finding that underscores students’ engagement’s complex and multidimensional nature in AI-enriched settings. Conversely, PEU positively influenced LE, which in turn significantly predicted STI. These findings suggest that usability, rather than perceived utility alone, drives deeper engagement and interaction in autonomous learning contexts. This research advances existing knowledge of GenAI adoption by proposing a structural model that integrates voluntary use, learner engagement, and teacher presence. Future research should incorporate variables such as digital literacy, self-regulation, and trust and apply longitudinal approaches to better understand the evolving role of GenAI inequitable, human-centered education.

The present study examined how empathy (affective and cognitive), basic psychological need satisfaction (autonomy, competence, and relatedness), and demographic factors (gender and academic achievement) jointly predict prosocial behavior during emerging adulthood. Grounded in Self-Determination Theory, this research explored whether relatedness need satisfaction mediates the relationship between empathy and prosocial tendencies. A total of N=889 undergraduate students from a large public university in the southeastern United States completed self-report measures assessing empathy, psychological needs, and prosocial behavior. Path analysis revealed that affective empathy and relatedness satisfaction were significant predictors of prosocial behavior. Relatedness also partially mediated the link between empathy and helping actions. Furthermore, gender and GPA contributed to prosocial outcomes, with female students and those with higher academic achievement reporting greater prosocial tendencies. These findings suggest that fostering emotional engagement and supporting students’ psychological needs—particularly the need for relatedness—may be key mechanisms for promoting prosocial development in educational settings during the critical stage of emerging adulthood.