Teaching a subject in a vocational high school (VHS) requires specialized instructional design strategies. Recently, instructional VHS used a computer-based platform to teach theory and practice. The computer will assist teachers in creating instructional media. This study aims to design augmented reality-based virtual laboratory media for electro-pneumatic practicum learning in order to ease teachers and students to learn the pneumatic practice. It is specially designed for practical learning purposes. The method used in this research is research and development. The assessment results from Instructional design experts 4.5, material experts 4.5, media experts 4.4, and language experts 4.8. Based on the assessment of experts, this augmented reality-based virtual laboratory media is feasible to use. Next, the results of this study are a prototype of students' pneumatic practice tools installed on mobile phones consisting of eight worksheets. In the designed augmented reality media, 1) 3-dimensional pneumatics can be rotated in all directions so that students understand. 2) Pneumatic job sheet, where on the augmented reality media designed for the simulation circuit, there is an explanation of the components and how they work, and a simulation through markers.

During COVID-19 in Athens, Greece, 535 general education and 170 special education teachers were tested for computer use self-efficacy, ICT competence, and computer attitudes. Demographic and occupational factors impacted computer attitudes and computer use self-efficacy. The GCAS and GCSES showed that general and special education teachers liked computers. Teachers were computer-savvy and confident. Computer attitudes boosted computer use self-efficacy. Computer self-efficacy is strongly linked with computer attitudes, subscales of confidence and affection and moderately linked with cognitions about computers. Age, position, and ICT training substantially influenced computer attitudes and computer use self-efficacy. ICT-trained teachers had improved their attitudes and computer use self-efficacy. Computer self-efficacy and attitudes about computers did not change for special education teachers, but computer confidence increased. Except for those under 25, younger teachers demonstrated higher computer self-efficacy than older ones.

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.

The central focus of this study is exploring the potential of Artificial Intelligence (AI) Chatbots in enhancing digital dialogue for students. The study investigates the key attributes of Chatbots that can contribute to the feasibility of facilitating digital dialogue to improve students' communication skills through discussions and dialogues. The study employed a descriptive method using a questionnaire to gather the perspectives of 35 educational experts on the use of AI Chatbots in digital dialogue skills. This study revealed that using AI Chatbots plays a crucial role in enhancing digital dialogue skills and can be effectively integrated into instructional practices to facilitate meaningful dialogue among students. Finally, the study recommends that educational technology specialists leverage new technologies, such as Al Chatbots to help improve student performance and facilitate digital dialogue in education.

Psychosocial and educational intervention approaches employ diverse treatment frameworks, most of which involve delivering some form of feedback to participants about their behavior. General conceptions of feedback are well-known to underlie mainstream therapeutic and educational approaches. Recently emerging ‘smart’ approaches also rely on feedback principles. However, little scholarship is available to stitch together evolving strands of feedback principles and no literature characterizes explicitly the diverse landscape of feedback practices employed in education or intervention science. This paper reviews intrinsic conceptions of feedback along with diverse cases of its use in intervention and education. Based on our consideration, we present a typology of feedback modalities that we hope will enrich the efforts of interventionists and educators to design treatment and educational frameworks incorporating feedback.

This research investigated the validity of measurement and causal model of online scam protection behavior (OSPB) among at risk Thai students. The sample comprised 286 high school students from three demonstration schools under the University. Data were analyzed using descriptive statistics, confirmatory factor analysis (CFA), and structural equation modeling (SEM). The factor loadings for all items satisfied the standard criteria with scores ranging from .40 to .80, item-total correlations ranging from .405 to .718, and Cronbach’s alpha coefficients ranging from .773 to .928. The modified model demonstrated a better fit with the empirical data (χ² = 47.62, df = 37, p = .113,  χ²/df = 1.287, RMSEA = .032, SRMR = .028, GFI = .97, CFI = 1.00, NFI = .99). All factors: a) awareness of online risks, b) inhibitory control, c) game-based learning, d) social support, and e) motivation to prevent online scams can predict 81% of OSPB. The motivation to prevent online scams strongly influenced OSPB, with an effect size of .60. Additionally, all factors can predict 88% of the motivation for online scam prevention, suggesting that Protection Motivation Theory (PMT) is a suitable framework for understanding and evaluating Thai students' preventive behaviors in online deception scenarios. This newly developed instrument is highly reliable and can be effectively used by researchers and educators to assess the risk of online fraud victimization among high school students.

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.