Ulrich Trautwein

Many students use generative AI (GenAI) tools for schoolwork on their own, without pedagogical guidance. Yet it remains unclear whether such use supports or undermines their academic development, or which students turn to it. We examined both questions using TOSCAneo data from 4,062 students in 136 secondary schools in Germany. Data were collected at the beginning and end of the 2023/2024 school year and included standardized achievement tests and measures of academic self-concept, value beliefs, and effort across six subjects. Students’ reported use of GenAI tools for academic activities formed two related factors: task-completion use and learning-oriented use. GenAI use increased over the school year for both task-completion use (d = 0.35) and learning-oriented use (d = 0.33), with only 5% of students never using GenAI. In cross-lagged panel models, lower prior achievement predicted more subsequent GenAI use in all six subjects for task-completion use and in five of six for learning-oriented use. Neither form of use predicted subsequent achievement. A use profile characterized by more task-completion and less learning-oriented use predicted lower subsequent effort in four of six subjects. No comparable pattern emerged for self-concept or value beliefs, and prior self-concept, value beliefs, and effort did not consistently predict later GenAI use. Overall, rather than predicting later achievement, self-directed GenAI use was related to which students turn to these tools and how much effort they invest. Guidance may therefore be particularly important for lower-achieving students, who use GenAI more than their higher-achieving peers.

Students’ school well-being is pivotal to both immediate academic success and long-term life outcomes, yet research has rarely compared a broad range of potential predictors within a common longitudinal framework. In this preregistered study, we used a lagged exposure-wide approach to examine 55 theoretically and empirically selected candidate factors in three annual waves of data from 2,401 German students (MAgeT1=10.75 years, 47% female), assessed from Grade 5 onward. Individual characteristics and students’ perceptions of teachers and teaching quality were associated with changes in school well-being. Notably, these associations were observed beyond academic performance, parental characteristics, and a broad range of covariates. Different factors were associated with school belonging and school satisfaction, highlighting the multifaceted nature of school well-being. By systematically comparing a broad set of candidate factors, our findings identify modifiable factors that may represent promising targets for future interventions aiming to support students’ well-being alongside their academic development.

Self-regulation (SR) has emerged as a pivotal construct in educational research, yet its diverse theoretical accounts often lack comprehensive integration. This paper addresses this by proposing an integrative conceptual framework that consolidates four core research traditions on SR in educational settings, each emphasizing distinct underlying mechanisms: (1) Stable Dispositions (e.g., personality traits; Matthews et al., 2009; Roberts et al., 2007; Song et al., 2020), (2) Limited Resources (e.g., working memory capacity and executive functioning; Friedman & Miyake, 2017; Paas et al., 2003; Sweller, 1994), (3) Driving Forces (e.g., motivation, interest, and affect; Eccles & Wigfield, 2002; Hidi & Renninger, 2006; Trautwein et al., 2019), and (4) Learning Activities (e.g., self-regulated learning, cognitive and metacognitive strategies; Winne & Hadwin, 1998; 2008; Zimmerman, 2000). By synthesizing these perspectives, our framework positions SR as a dynamic, interdependent system, emphasizing how interactions and compensatory mechanisms explain individual differences and influence learning outcomes. We further argue for an integrative methodological toolkit, leveraging advanced machine learning and computational models, to capture the multi-level, temporal, and social dynamics inherent in SR. This holistic approach is crucial for advancing a comprehensive and ecologically valid understanding of SR in educational contexts.

Despite their best intentions, individuals often give in to the temptation of distractions and avoidance, leading to a pattern of putting off essential responsibilities. This behavior can occur at ...

Self-regulation competence is known to be a critical driver of academic achievement, mental health, and lifelong success. However, research on education and educational practice in K-12 remains fragmented in that groups of researchers focus on conceptualizations from different traditions, including self-regulated learning (SRL), executive functions (EFs) and personality psychology (e.g., conscientiousness), and there is limited cross-talk between these groups. In this article, we propose an integrated multicomponent model that unifies these perspectives and conceptualizes self-regulation competence as a developmental “superpower” that is both predictive of a broad range of outcomes and amenable to educational support. We examine three core propositions: (1) Self-regulation competence—broadly defined—predicts important cognitive, social, and emotional outcomes in school and across the life span; (2) SRL, EFs, and conscientiousness show considerable conceptual and empirical overlap, supporting their integration into a comprehensive framework; and (3) self-regulation competence can be effectively fostered through targeted and embedded educational interventions. To fully develop and exploit the superpower qualities of self-regulation competence, promoting self-regulation competence should become a guiding principle of education systems. High-quality teaching—characterized by effective classroom management, cognitive activation, and student support—and whole-school approaches are needed to orchestrate the promotion of self-regulation competence in real-world educational contexts.