Due to irregular daytime routines, chronotype changes, side jobs and exam periods after the COVID-19 pandemic, university students are trying to find new balances in their everyday life. The aim of the present study is to analyze the impact of daytime chronotype and hour preferences for the circadian rhythm on academic achievement among university students, considering their sleep habits and class frequencies in daytime and nighttime classes. Furthermore, this study aimed to analyze the differences in chronotype preferences with regard to age, sex, and academic schedule students are attending. A sample of 87 university second-year Psychology and Management students attending the academic year 2021/2022 after the governmental relief measures of the COVID-19 pandemic completed a 13-item questionnaire on sleep habits and preferences. Further variables encompassed sleep behaviors, such as age, sex, daytime and nighttime classes, as well as academic achievement. The results of the study showed that university students had a higher preference for the morningness type. Additionally, chronotype traits explained 30% (values from regression analysis) of academic achievement but did not directly impact academic results. The sleep-wake cycle diverged among age groups because the youngest participants (19–21 years old) and older participants (35–44 years old) reported higher academic scores during the first semester and the full academic year. No significant differences were identified with respect to sex. There is a lack of literature explaining the effect of sleep hours on academic achievement among students after stay-at-home rules during the COVID-19 pandemic. Thus, it is imperative to understand the difficulties students face with regard to their studying hours, working shifts, and daytime or nighttime classes to create a sustainable university system that attends to students’ needs and necessities.

This research aimed to explore the influence of a robotic program using the robot kit "RoboBuilder RQ+110" on students' attitudes toward effective communication. The study used a quantitative research design and involved 475 grade 4 (10 years old) students from Malaysia's Selangor and Malacca states. A quasi-experimental research (pre-test & post-test) approach with control and experimental groups was adopted, and the data were analyzed with inferential statistical test and repeated measures analysis of variance (ANOVA) using SPSS 25 software at 0.05 significance level. Questionnaires were administered to collect data from the experimental and control groups. The results showed statistically significant changes (α ≤ .05) in attitudes toward effective communication for the experimental group that received a robotics program compared with the control group. The study results suggest that innovative technological tools or programs such as robotics programs are recommended as innovative science, technology, engineering, and mathematics (STEM) program rooted in constructivism to improve students' attitudes toward effective communication.

During the implementation of the inclusive education policy in several countries in Association of Southeast Asian Nations (ASEAN), the psychological health of special education teachers should be considered as a key success factor. This study explored special education teachers’ perceived work stress (PWS), bio-psychological burn-out symptoms (BPS), and transformational teaching (TFT) in inclusive education in Indonesia and Thailand. There were 177 Indonesian and 199 Thai teachers completing a series of questionnaires that included BPS, PWS, and TFT. The results showed that BPS and PWS were high, whereas BPS and TFT were significantly different across nations. No gender differences were found among both Indonesian and Thai teachers. Moreover, TFT could be significantly predicted by positive age and negative work stress, which explained 8% of the variance among Indonesian teachers (R2 = .08, F(4, 172) = 4.18, p < .01) and by positive age and negative burnout symptoms, which explained 6% of the variance among Thai teachers (R2 = .06, F(4, 186) = 3.18, p < .05). Furthermore, inclusive education policymakers and stakeholders should be aware of psychological health improvement including burnout symptoms and work stress, which negatively invade the role of TFT among special education teachers in both countries.

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.