The aim of this research was to assess changes in secondary school students’ grades longitudinally, including the semester before the COVID-19 pandemic, the period of distance learning, and two semesters when students had returned to face-to-face learning. In this longitudinal study, n=263 Latvian students’ grades from the period of six semesters (autumn 2019 to spring 2022) were collected and analyzed for seven study subjects (mathematics, English, Latvian, biology, chemistry, physics, and literature), using Friedman’s ANOVA, and Wilcoxon test for comparison. Results show that grades increased for several study subjects during the beginning of the distance learning period (e.g., mathematics and Latvian). However, this initial increase diminished after students had returned to schools to study in-person, especially for the subjects of mathematics and Latvian (native language). Decreases in students’ grades after returning to face-to-face studies indicate possible accumulated negative long-term effects of distance learning. The dynamics of the grades differ in various study subjects (e.g., relative stability in chemistry, decrease in mathematics, Latvian, biology), thus justifying the approach to analyze each study subject or study field separately. This study gives insight into longitudinal changes in students’ academic achievement, following the same students throughout their whole secondary school period from 10th to 12th grade during the pandemic.

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.

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.