Fa Zhang

With the rapid development of artificial intelligence (AI) and digital education, older adults increasingly participate in formal learning environments such as universities for the elderly. However, little is known about how these learners engage with AI-enhanced technologies, what motivates them, and what psychological dynamics influence their adoption and use. This qualitative study draws on semi-structured interviews with elderly students at a university-for-older-people in China to explore their experiences, perceptions of trust, motivation, and emotional responses to AI learning tools. Using thematic content analysis, we identify key themes such as perceived usefulness, trust (competence, benevolence, integrity), anxiety, self-efficacy, and social connectedness. We interpret these findings through a trust-and-learning framework, and we discuss design and pedagogical implications for developing AI-supported learning systems that empower older learners. Based on these insights, the paper proposes design recommendations for accessible AI learning environments, including scaffolded onboarding, transparent AI explanations, multimodal assistance, error-tolerant interfaces, and community-supported learning. The study contributes actionable guidance for AI developers, course designers, and lifelong learning institutions seeking to build inclusive learning technologies for older populations.

Introduction: Early self-regulation is a crucial predictor of how well preschoolers respond to challenging, frustrating, and distracting situations. Examining the mediating role of contextual and individual factors provides insight into how external influences and personal characteristics shape children’s ability to navigate these challenges. This study aims to investigate whether specific contextual (e.g., parental stress) and individual (e.g., emotional skills) factors mediate the relationship between preschoolers’ self-regulation and their behavioral responses to transitions, frustration, and distraction.Methods: We utilized data from the 2021 National Survey of Children’s Health, which includes a nationally representative sample of children aged 3–5 in the U.S. (N = 11,554). Structural equation modeling was employed to examine the mediating effects of parental stress (contextual factor) and emotional skills (individual factor) on the association between preschoolers’ self-regulation and their behavioral responsiveness.Results: The analysis revealed that higher levels of self-regulation were associated with more positive emotional skills, lower parental stress, and improved behavioral responsiveness in children. Among the mediators, parental stress significantly mediated the relationship between preschoolers’ self-regulation and behavioral responsiveness, while emotional skills did not show a significant mediating effect.Discussion: These findings suggest that contextual factors, particularly parental stress, play a more substantial mediating role than individual emotional skills in shaping the relationship between self-regulation and behavioral responses in preschoolers. This highlights the importance of addressing parenting-related stress in intervention programs aimed at supporting children’s behavioral development.

This study investigated how domain specificity of academic motivation related to the connection between instructional clarity and academic achievement in science and mathematics. It focused on three aspects of domain‐specific academic motivation—self‐concept, intrinsic value, and utility value—drawing from the Expectancy‐Value Theory model. The study analyzed data from a nationally representative sample of 9060 eighth‐grade students in Australia, with an average age of 14.1 years. After controlling for student demographics, socioeconomic status, and parental education level, the findings from structural equation modeling showed that instructional clarity positively related to students’ mathematics achievement but did not significantly relate to their science achievement. Academic self‐concept in both science and mathematics subjects was identified as the strongest motivational factor in student achievement. Additionally, the study demonstrated that instructional clarity was a stronger predictor of academic motivation to learn science compared to mathematics. These results offer empirical support for the connection between instructional clarity and students’ academic achievement in science and mathematics via a domain‐specific motivational pathway.

We investigated the structure of science motivation among a nationally representative sample of grade 8 students in the United States from the TIMSS dataset. Drawing on an integrative conceptual model of motivation, the following constructs from situative expectancy-value theory (SEVT) and self-determination theory (SDT) were examined through confirmatory factor analyses, including self-concept, task value (intrinsic and utility value), and sense of relatedness, to test the underlying factor structure of motivation. Internal validity evidence was established, which showed that a four-factor model fits the data significantly better than a three-factor model and one-factor model. This study contributes to the current literature by providing evidence of the four dimensions of motivation in a manner that is theoretically aligned with SEVT and SDT, and validated using the TIMSS dataset. The student’s sense of relatedness as a key interpersonal facet of learning motivation is highlighted in this study.

This study aimed to further the understanding of transactional relationships that exist between problem behaviors and academic performance in early childhood. Early academic and behavior difficulties increase the risk of school disengagement, academic failure, and dropout. Although children’s academic and behavioral difficulties have been shown to be intercorrelated, little research has focused on how the relationship reciprocates and progresses in early childhood. This study investigated how problem behaviors (i.e., externalizing and internalizing) influence and are influenced by academic performance (i.e., poor reading and math) from kindergarten to third grade. Participants included 18,135 students (51.22% boys) derived from a nationally representative sample in the Early Childhood Longitudinal Study, Kindergarten Class of 2011 (ECLS-K: 2011). Teacher ratings of children’s internalizing (low self-esteem, anxiety, loneliness, or sadness) and externalizing (fighting, arguing, showing anger, impulsively acting, and disruptive behaviors) problem behaviors, as well as direct assessments of children’s academic performance (reading and math), were collected yearly. Cross-lagged panel modeling (CLPM) was employed to examine reciprocal relationships between problem behaviors and academic performance over time from kindergarten to third grade. The results supported the transactional relationships in early childhood, with higher externalizing as well as internalizing problem behaviors predicting lower academic performance and lower academic performance predicting higher externalizing and internalizing problem behaviors. The implications for research, prevention, and early intervention regarding the progression of academic and behavioral problems are discussed.