Generative AI use and academic achievement: a cross-sectional study of Czech seventh-grade students

Generative AI use and academic achievement: a cross-sectional study of Czech seventh-grade students

This study examines how Generative Artificial Intelligence (GenAI) use is associated with academic achievement among Czech seventh-grade students. Grounded in the deep versus surface approaches to learning framework, we distinguish between the frequency of GenAI use and students’ endorsement of reliance on GenAI over understanding, defined as the belief that knowing how to prompt AI effectively is more important than developing one’s own conceptual understanding. Using cross-sectional survey data from a nationally stratified sample of 2,307 students, we estimated structural equation models predicting IRT-scaled achievement in mathematics and Czech language. Frequency of GenAI use was not positively associated with achievement in either subject. In contrast, stronger endorsement of reliance on GenAI over understanding was consistently and negatively associated with achievement, with a more pronounced relationship in Czech language. Neither GenAI use frequency nor reliance significantly moderated the associations between learning-related dispositions and achievement. These findings suggest that the educational implications of GenAI depend less on how often students use AI tools and more on whether AI is approached as a support for understanding or as a substitute for cognitive engagement. The results contribute to debates on epistemic offloading and the role of AI in shaping students’ orientations toward learning.

Slovak MPs’ response to the 2022 Russian invasion of Ukraine in light of conspiracy theories and the polarization of political discourse

Slovak MPs’ response to the 2022 Russian invasion of Ukraine in light of conspiracy theories and the polarization of political discourse

This study investigates Slovak political discourse on Facebook in the weeks before and after the invasion began. We examine the discourse through the lens of Discourse Network Analysis, combining qualitative content analysis of MPs’ Facebook posts with quantitative bipartite social network analysis. During the two weeks, we retrieved all posts from all MPs who had an active public Facebook page. We manually coded all posts and created a bipartite discourse network consisting of MPs connected to each other via shared discourse codes in two timepoints. We performed a series of exploratory analyses that identified the content of the political discourse, the structure of the political discourse network, and the mechanisms driving the change of the political discourse network. Our results show that the invasion dramatically changed political discourse in Slovakia, with the domestic coalition-opposition struggles losing prominence among the invasion-related topics. The structure of the political discourse network showed a strong coalition-opposition split. While coalition MPs had largely pro-Ukrainian sentiments, opposition MPs largely communicated pro-Russian propaganda. A cluster of opposition MPs consistently spread conspiracy theories both before and after the invasion began, supporting a “conspiracy singularity” theory—the tendency of actors to spread multiple different conspiracy theories and interconnect various conspiracy theories into one overarching narrative. The change of the discourse network at the beginning of the invasion was largely driven by the agenda setting of several parties, agenda reinforcement, and increasing political polarization.

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Ukrainian refugees struggling to integrate into Czech school social networks

Ukrainian refugees struggling to integrate into Czech school social networks

We provide a brief insight into the integration of Ukrainian refugees in school social networks in the Czech Republic following the mass migration caused by the Russian invasion of Ukraine. Our sample contains twelve classrooms with a total of 266 students in grades 5 to 9; 21.05% of the students were of Ukrainian origin. We employed multiplex exponential random graph modelling to assess the level to which Ukrainian refugees were integrated within peer networks, capturing both friendship and exclusion ties. We then employed a meta-analytical procedure to aggregate the results from the individual classrooms and a meta-regression to study the relationship between classroom ethnic composition and the level of integration of Ukrainian refugees. We found social networks to be formed heavily along ethnic lines with strong ethnic homophily in friendship ties and a propensity of the Ukrainian students to both send and receive fewer friendship ties than their Czech classmates. We found no evidence that the Ukrainian students sent or received more exclusion ties than their Czech classmates, suggesting that the Ukrainian students did not face explicit rejection from classmates; rather, we saw a tendency of the Ukrainian students to be neglected. Our findings stand in contrast to reports from school headmasters who asserted that the social integration of Ukrainian students was seamless. We further found a higher proportion of Ukrainian students in classrooms to be related to stronger homophilic behaviour and a lower tendency of Ukrainian students to make friends. Our results therefore imply that increased classroom diversity may negatively influence the integration of refugees in social networks.

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