Achieving Sustainable Development Goal 4 (Quality Education) requires innovative and inclusive approaches that sustain attention, foster engagement, and build digital literacy in primary education. This study examined the impact of Scratch on sustaining attention among third- and fourth-grade students (ages 9–11). A total of 89 students participated, divided into control and experimental groups, to evaluate the effects of integrating Scratch into classroom activities. Data were collected through anonymous surveys with parental consent and analyzed using nonparametric statistical tests. The results showed that Scratch-based instruction enhanced sustained attention, particularly in interactive and digital activities such as active participation, video, and web-based tasks. In contrast, traditional textbook and workbook tasks showed weaker effects. Scratch also reduced common reasons for attention loss, with significant decreases in perceptions of excessive lesson length and loss of interest. Gender differences were minimal, confirming Scratch’s inclusivity, and strong correlations between problem-solving activities indicated that engagement, collaboration, and multimedia tasks mutually reinforce attention. These findings provide empirical support for Scratch as a tool that sustains attention, reduces disengagement, and promotes equity in learning. Although limited by reliance on self-reported data and a single educational context, the study offers evidence that Scratch advances innovative pedagogy aligned with SDG 4.

This paper explores how school culture and key organizational culture dimensions, power distance, humane orientation, performance orientation, and in-group collectivism, affect different types of conflict (task, relationship, and process) in primary schools in Serbia. It also examines how gender and teachers’ organizational commitment moderate these relationships. Data were obtained by surveying 380 respondents, all of whom were primary school teachers in Serbia. The participants were between 23 and 65 years old. Of the total sample, 19.47% were male, and 80.53% were female. The mean values ranged from 1.8046 to 4.9847, with standard deviations between 0.7699 and 1.4785. The research was conducted using a simple random sampling technique. Teachers were given questionnaires through Google Forms, which they completed online. Printed versions were also distributed and later entered into the database. The study was guided by two research questions and two hypotheses. Data analysis was performed using SPSS (Statistical Package for the Social Sciences). The findings reveal that the school culture dimension of teacher professionalism and goal setting (r = −0.297 **; β = −0.496) and the organizational culture dimension of humane orientation (r = −0.303 **; β = −0.198) have the most substantial negative effects on conflict, indicating their beneficial role in reducing it. In contrast, power distance shows a strong positive relationship with conflict, particularly with relationship conflict (r = 0.230 **; β = 0.201). Additionally, excessive emphasis on teacher collaboration and performance orientation appears to increase relationship conflict (β = 0.226; β = 0.261, respectively). Gender differences emerged: cultural dimensions were more effective in reducing conflict among women than men. Among male teachers, power distance had a stronger influence, significantly increasing task conflict (r = 0.303 **). The school culture and organizational culture dimensions significantly reduce the conflict dimensions in the case of high teacher commitment. The dimension power distance has a statistically significant and positive effect on conflicts when organizational commitment is high (r = 0.247 **). Therefore, school culture and organizational culture dimensions achieve stronger effects in committed women, while power distance achieves stronger effects in committed men.

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education. One such technology is ChatGPT, a powerful large language model developed by OpenAI. In this paper, the authors consider the possibilities and limitations of applying artificial intelligence, primarily ChatGPT, in the educational process. In addition to general definitions, this popular technology is briefly presented, as well as the possibilities of its application. Consideration is mainly carried out from the educational aspect, with the role of the teacher still in the centre. At the same time, appropriate didactic, pedagogical, and methodical possibilities were presented, with which the teacher can be innovative and even more successful, effectively applying ChatGPT in everyday educational work. Of course, the limitations arising from the application of artificial intelligence are also presented. In this article, the benefits and risks related to the use of ChatGPT in education are discussed. The article argues that ChatGPT has at least five main benefits, such as creating learning assessment, enhancing pedagogical practice, offering virtual personal tutoring, creating an essay or research article outline, and brainstorming ideas. However, there are risks related to academic integrity issues, unfair learning assessment, inaccurate information, and over-reliance on AI. The article offers a set of recommendations for the effective use of ChatGPT for educational purposes. As a final result, we get a more realistic picture of this current topic, which also shows the pedagogical aspects and does not diminish the role of the teacher.

The increasing use of artificial intelligence (AI) in graphic engineering, design and desktop publishing has created a need for educational environments that combine structured instructional materials with on-demand learner support. This paper presents the second development phase of the ASKING educational platform, referred to as ASKING AI+, in which a dedicated AI chatbot is integrated into the existing multimedia learning environment. The original ASKING platform provides practical lessons for Adobe, Affinity and Microsoft Office applications in text-and-image, video and AI-avatar formats. These instructional modalities have already been examined in two controlled student studies, providing an empirical basis for further platform development. The new ASKING Assistant is configured to answer domain-specific questions, explain practical procedures step by step, support troubleshooting and design decisions, and generate formative quizzes on request. The assistant prioritizes the platform knowledge base and is accessible from the platform homepage and from the end of each lesson, allowing learners to request help at the point of need. The paper describes the development rationale, chatbot configuration, website integration and the relationship between the new conversational layer and findings from the previous evaluations. A dedicated student evaluation of the chatbot-enhanced platform is planned as the next research stage, with particular emphasis on perceived additional learning support, usefulness of question answering and quizzes, clarity, learner control and objective learning outcomes.

Generative artificial intelligence (GenAI) is increasingly available to schools, but evidence on GenAI acceptance and intention to use it among subject teachers in rural primary education remains limited. This study examined behavioral intention to use GenAI among 202 subject teachers teaching Grades 5–8 in rural Serbian primary schools through an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework and semi-structured interviews with 20 survey participants. The initial composite-score regression explained 10.5% of Behavioral Intention variance (R² = 0.105). Performance Expectancy was marginally positive (β = 0.136, p = 0.049) and Facilitating Conditions showed a nominal, unadjusted negative association (β = −0.187, p = 0.009); however, the Performance Expectancy coefficient was no longer nominally significant with HC3 robust standard errors (p = 0.054), and neither coefficient survived Holm correction applied to the HC3 hypothesis-test p-values. Prior GenAI use (83.7%) did not materially change the model. A post hoc PLS-SEM sensitivity analysis, using theory-guided indicator refinement and 10,000 bootstrap resamples, showed composite reliability and convergent-validity indices of CR = 0.820–0.928 and AVE = 0.560–0.865, with low HTMT values (maximum = 0.482). Unadjusted bootstrap intervals suggested a positive Effort Expectancy path and a negative Facilitating Conditions path, but none of the eight hypothesized paths survived family-wise multiplicity correction. Facilitating Conditions also showed severe floor restriction (M = 1.34, maximum = 2.50/5; 52.0% at the minimum), while interviews described infrastructure as a barrier—a qualitative pattern that diverged from the negative coefficient. The findings therefore indicate limited and measurement-sensitive support for transferring an extended UTAUT model to this context and emphasize the need for stronger prospective measurement validation, context-specific constructs, and longitudinal research.

In contemporary organizations, digital learning environments and AI-supported instructional modalities play an increasingly important role in workforce upskilling and operational efficiency. Despite growing investments in video-based learning and AI-generated instructional agents, empirical evidence on their effectiveness remains inconclusive. This study examines whether different digital learning modalities influence skill acquisition, task performance, retention, and user perceptions in a simulated work-related context. An experimental study was conducted with 65 participants assigned to one of three learning conditions: static instructional material, video-based instruction with human narration, and video-based instruction with an AI-generated avatar. Performance was assessed through a pretest–posttest design, a practical task simulating a typical data-processing activity, and a delayed retention test after seven days. Participants also evaluated the learning experience in terms of clarity, engagement, and overall effectiveness. The results revealed no statistically significant differences between instructional modalities in knowledge acquisition, task performance, or retention. Similarly, no statistically significant differences were observed in participants’ self-reported ratings. However, qualitative findings suggested that some participants perceived the AI-generated avatar as somewhat distracting, despite generally positive evaluations of the video-based formats. These findings did not provide evidence that more technologically advanced and resource-intensive learning formats led to superior performance outcomes in the present sample. The findings highlight the importance of instructional design quality over technological complexity and point to a potential mismatch between user preferences and actual performance. From a management perspective, the results raise relevant questions regarding the cost-effectiveness of AI-supported learning solutions and provide evidence-based insights for decision-making in organizational learning and digital transformation strategies.