Computer and information literacy, or CIL, measures the ability to use computers to investigate, create and communicate effectively in different settings. Computational thinking, or CT, assesses the ability to recognize real-world problems that can be expressed computationally and to develop algorithmic solutions. In the study’s within-school comparisons, strict limits were negatively associated with CIL scores at B = −14.94 and with CT scores at B = −17.31, with both p-values below .001. Flexible limits that made exceptions for homework showed the opposite pattern: their associations with CIL and CT were positive at B = 2.17 and B = 8.34, respectively, also with p-values below .001.

Similar contrasts appeared when researchers compared schools. Schools with more students reporting strict parental limits had lower average performance in CIL and CT, with coefficients of −48.12 and −47.22, both statistically significant at p < .001. Schools in which flexible rules were more common instead had higher average CIL, at B = 9.74 with p < .05, and substantially higher CT, at B = 33.67 with p < .001. The models distinguished a student’s household rule from the prevalence of similar rules among classmates, allowing the analysis to separate within-school individual differences from broader school-level patterns.

The study used three-level linear models reflecting the structure of ICILS, which places students within schools and schools within countries. In null models, 34% of the variance in CIL and 48% in CT was attributable to differences between schools, while 50% and 22%, respectively, was associated with between-country variation. The researchers adjusted for socioeconomic status, gender, immigration status, computer experience, information and communications technology self-efficacy and internet access at home. Continuous variables were standardized, school-average predictors were included alongside group-mean-centered individual variables, and models were estimated with restricted maximum likelihood.

Because ICILS assessment outcomes were reported as five plausible values per domain, the researchers fitted separate models for each set and combined the estimates using Rubin’s rules. They also incorporated rescaled student sampling weights to reflect the study’s complex survey design, with weights adjusted within each country. The CIL analysis included students in 4,609 schools across 29 countries, while the CT analysis covered 2,722 schools in 19 countries. This difference in coverage reflects the fact that CIL was a core domain in more participating systems, whereas CT was administered as an optional domain.

Household circumstances remained important even after these controls. Socioeconomic status was positively associated with both outcomes at the individual and school levels, as were general ICT self-efficacy and home internet access. Computer experience was also positively associated with performance within schools, at B = 8.51 for CIL and B = 9.74 for CT, both with p < .001. The socioeconomic measure combined parental education, parental occupation and the number of books available at home. These relationships mean that the screen-time estimates should not be interpreted as isolated effects of a single parental choice.

The clearest evidence of socioeconomic moderation concerned flexible rules and computational thinking. The interaction between socioeconomic status and flexible limits was positive at B = 3.82, with p < .01, indicating that the association between homework exceptions and CT was stronger among students from more advantaged socioeconomic backgrounds. No statistically significant moderation by socioeconomic status was detected for CIL. The interaction between socioeconomic status and strict limits was also not significant for CT, meaning the evidence does not show that socioeconomic background altered the negative association observed for rigid restrictions.

The results add large-scale evidence to a body of research in which screen restrictions have produced inconsistent findings. Previous work has linked restrictions to fewer opportunities to develop digital skills, while other studies have found benefits from moderate technology use or structured parental guidance. The new analysis suggests that the relevant distinction may be purpose as well as duration: a rule that ends a student’s access before educational work is completed differs from one that places recreational use in a structured boundary while allowing school tasks. The analysis is consistent with, but cannot prove, the possibility that flexible rules preserve opportunities for learning by doing while strict limits remove access at moments when digital tools may be needed.

Several limitations constrain what can be concluded. The design is cross-sectional and relies on students reporting whether parental limits apply during school days, so it cannot show whether rules preceded changes in digital competence or whether more digitally capable families selected particular strategies. The analysis cannot determine whether the associations operate through academic engagement, self-regulation, parental digital knowledge or other mechanisms. CT was assessed in fewer countries than CIL, and only student-level weights were used, as the authors noted limitations in available multilevel weighting methods. The relatively broad category of screen regulation also leaves the frequency, consistency and quality of parental enforcement unresolved.

Future research could test the proposed mechanisms with longitudinal and mixed-method studies, while also measuring negotiation, parental digital competence and co-learning more precisely. Comparisons across cultures could examine whether expectations about obedience, independence and technology use change the relationship between rules and digital performance. For now, the study’s clearest result is that “less screen time” and “more structured screen use” are not interchangeable: in large international Grade 8 samples, no-exception restrictions were associated with weaker digital performance, while flexible homework exceptions were associated with stronger performance, especially in computational thinking and among students with higher socioeconomic status.

Subject of Research: Parental screen-time regulation and adolescents’ computer literacy and computational thinking

Article Title: Parental screentime limits and child computer and information literacy and computational thinking: a multilevel analysis of 2023 ICILS data

Article References: Tlessov, A., Courtney, M. G. R., Vassilova, M., & Kambatyrova, A. (2026). Parental screentime limits and child computer and information literacy and computational thinking: a multilevel analysis of 2023 ICILS data. Large-scale Assessments in Education, 14(1), Article 56. https://doi.org/10.1186/s40536-026-00328-4

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00328-4

Keywords: screen time, adolescents, digital literacy, computational thinking, parental mediation, ICILS 2023, socioeconomic status, computer skills, Parental, screentime, limits, child

The researcher, Jonas Mandalunes, exploited a rarely used survey item that asked students whether their parents limited screen time on school days and, crucially, whether that limit included the time they spent doing schoolwork on a screen. Students fell into three groups: an inclusive limit that treated an hour of homework like an hour of entertainment, an exempt limit that protected schoolwork and restricted only the rest, or no limit at all. Across the 28 systems, a median of 14.8 percent of students reported inclusive limits, 28.3 percent reported exempt limits, and 57.4 percent reported no limits. In other words, among families that set limits at all, roughly two-thirds carved schoolwork out of the daily budget.

The headline result is striking in its consistency. Students whose limits counted schoolwork scored 24.7 points lower in computer and information literacy than students whose limits exempted it, after adjusting for socioeconomic background, gender, home language, and immigrant background. That gap — about 0.30 within-system standard deviations, or roughly 29 percent of the width of a proficiency level — was statistically significant and negative in all 28 education systems, ranging from 48.1 points in Norway to 9.1 points in Oman. The pattern replicated almost perfectly in computational thinking, where the 19 systems that administered that assessment showed a pooled difference of 32.9 points, again negative everywhere.

Just as telling is what the study did not find. Students with no limit scored essentially the same as students with exempt limits in computer and information literacy — a pooled difference of just 0.6 points, small enough to satisfy a formal equivalence test with a margin of ten points. Having a rule, in itself, was not associated with lower competence. The entire penalty was concentrated among the minority of students, roughly one in seven, whose parents counted homework against the clock. This finding reframes the simpler comparison in the official ICILS report, which found students without limits scoring higher in most countries: the study shows mathematically that this binary gap is almost entirely a product of how many inclusive limits a system contains, since the share of inclusive limits among all limits ranged from about one-sixth in Denmark to more than half in Azerbaijan.

The study tested three competing explanations, and none fit alone. The first, an opportunity account, holds that digital skills grow through sustained, purposeful use, so a limit that swallows homework time leaves fewer chances to practice. The second, a displacement account, suggests that counting schoolwork might actually help by curbing the multitasking soup of homework and entertainment on the same device. The third, selection, argues that parents adopt inclusive limits reactively — because a child is struggling, shows weak self-control, or blends homework with play — so the rule marks pre-existing differences rather than causing them.

Several findings point toward selection. Students with inclusive limits came from lower socioeconomic backgrounds in 27 of 28 systems, had less computer experience in 27, expected a university degree less often in 24, and reported lower self-efficacy in 25. The gap shrank steadily as more characteristics were controlled: from 29.8 points without covariates to 24.7 with the basic adjustments, to 22.1 points when age and educational expectations — rough proxies for prior attainment — were added, and to 20.6 points when students were compared only with their own schoolmates. An exploratory meta-regression added a telling gradient: the gap was largest precisely where inclusive limits were rare, shrinking by 0.07 standard deviations for every 10-percentage-point increase in their prevalence. That is the pattern one would expect if an uncommon rule is adopted for specific, child-specific reasons, and harder to explain if the rule itself imposed a fixed cost.

Yet the opportunity account is not dismissed. Students with inclusive limits reported learning internet-related tasks — searching, evaluating sources, spotting scams, managing privacy — less often outside school, where parental rules apply, but not at school, where they do not. That setting-specific pattern is exactly what the opportunity account predicts. They also reported less recreational device use, which may mean fewer occasions for the informal, self-directed exploration through which some digital skills develop. But the simplest version of the story fails: these students reported just as much digital schoolwork at home as their peers, and slightly more long homework sessions. Meanwhile, students with no limits reported the most recreational use and the most multitasking, yet scored like the exempt group in literacy and lower in computational thinking — a reminder that raw hours of screen use track competence poorly.

The author also confronted the elephant in the room: unmeasured confounding. Prior achievement, self-regulation, and parental digital competence are not captured in ICILS, and any of them could shape both the rules families set and the skills students display. A sensitivity analysis following modern omitted-variable methods found that a confounder as strong as the socioeconomic index would shrink the gap only to about 21 points, and one three times as strong would leave roughly 13 points. But the analysis is candid about the limits of that reassurance: prior achievement likely predicts digital competence far better than socioeconomic status does, and a variable that strong would need only a weak link to rule type to account for the entire difference. The 25-point gap, the study stresses, should not be read as the causal effect of a rule.

The methodological care behind these estimates deserves note. The analysis used plausible values, sampling weights, and 75 jackknife replicate weights, pooled system-level estimates through random-effects meta-analysis, and verified the code by reproducing published national averages and the international report’s own limit-versus-no-limit comparison. Results proved robust to reweighting for item nonresponse, to using the parallel non-school-day item, and to adding or excluding individual systems. Students who skipped the rule item scored lower than those who answered it, but inverse-probability reweighting left the conclusions unchanged.

The practical upshot is twofold. For researchers, the message is that surveys of parental mediation must record what a limit covers, not merely whether one exists — studies linking screen rules to achievement, sleep, or wellbeing may otherwise average over rule types whose associations differ in sign and size. For families, the findings align with guidance from the American Academy of Pediatrics and Canadian movement guidelines, which already distinguish recreational from educational screen use, though the study is explicit that it cannot show that changing a rule would change a child’s competence. What it does show is that the one-in-seven students whose parents count homework as screen time differ from their peers in ways the data capture only partly — and that teachers may want to pay attention to exactly those students.

Subject of Research: The relationship between the scope of parental screen-time limits and adolescents' assessed digital competence in ICILS 2023

Article Title: When homework counts as screen time: parental limits and adolescents’ digital competence across 28 education systems in ICILS 2023

Article References: Mandalunes, J. (2026). When homework counts as screen time: parental limits and adolescents’ digital competence across 28 education systems in ICILS 2023. Large-scale Assessments in Education, 14(1), Article 54. https://doi.org/10.1186/s40536-026-00326-6

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00326-6

Keywords: screen time, parental mediation, digital competence, homework, ICILS 2023, computational thinking, computer and information literacy, adolescents, large-scale assessment, meta-analysis, educational technology, parenting

The study, led by Sukanya Chaemchoy of Chulalongkorn University together with Thanakrit Supsin, analyzed data from 24,273 students nested in 1,049 schools across Chile, Cyprus, Denmark, Greece, Korea, Norway, Romania, the Slovak Republic, Slovenia, Sweden, Chinese Taipei, and Uruguay. These were the 12 systems that administered ICILS 2023’s optional questionnaire on generative AI, which asked principals how likely they believed ChatGPT and similar tools were to help or harm students’ learning in their schools. The researchers linked these leadership reports with achievement data, principal questionnaires, and surveys completed by schools’ ICT coordinators, constructing an unusually rich picture of the organizational digital climate surrounding each student.

The five focal conditions were carefully distinguished. Positive ChatGPT expectations measured principals’ anticipated benefits, such as greater student interest in learning, better written work, and improved critical evaluation of information. Negative ChatGPT expectations captured anticipated harms, including shallow conceptual understanding, submission of work that is not the student’s own, and dependence on AI tools. Instructional ICT-use expectations reflected whether teachers were expected to integrate digital technology into teaching, assessment, and monitoring of progress, while ICT collaboration expectations concerned professional communication and collaboration via technology. Finally, pedagogical ICT hindrances, reported by ICT coordinators, captured constraints such as insufficient teacher skills, limited preparation time, weak pedagogical support, and restrictive policies.

Methodologically, the study is a masterclass in caution. The authors estimated separate weighted two-level models for each education system, treating students as nested within schools and adjusting for student sex, home internet access, computer experience, within-school socioeconomic background, and school socioeconomic composition. Because CIL was measured using five plausible values, each model was run five times and the results formally combined. The family of 60 primary tests, five school conditions across 12 systems, was then subjected to Benjamini-Hochberg false discovery rate control, a correction that dramatically raises the bar for what counts as a credible finding in large-scale educational research.

The outcome was unambiguous at the top line: not a single school condition produced a false-discovery-rate-retained association with CIL. Three coefficients did carry raw p values below 0.05. In Korea, higher ICT collaboration expectations were associated with roughly 9.55 points lower CIL, and pedagogical ICT hindrances were associated with about 4.11 points lower CIL. In the Slovak Republic, the direction reversed dramatically: ICT collaboration expectations were associated with 7.82 points higher CIL. Yet all three signals carried an FDR-adjusted q value of 0.730, meaning they are best read as nominal, system-specific hints for future replication rather than established associations.

The Korea-Slovak Republic contrast is perhaps the most intriguing descriptive finding. The same survey instrument, measuring the same construct, produced estimates with opposite signs and non-overlapping confidence intervals in the two systems. The authors are careful not to overclaim: no formal test of slope heterogeneity was conducted, and the study did not measure the national policies, governance arrangements, curricula, or implementation histories that might explain the divergence. But the pattern echoes earlier ICILS research from 2013, which found that the school-level conditions linked to teachers’ ICT use differed across Australia, the Czech Republic, Germany, and Norway, suggesting that identical school-scale scores may be embedded in fundamentally different institutional realities.

A secondary analysis reinforced this caution about pooled summaries. When the researchers constrained the focal slopes to be equal across systems, using equal total weights for each education system, all five common-slope confidence intervals included zero. The near-zero pooled estimate for ICT collaboration expectations simply cannot represent both Korea’s negative and the Slovak Republic’s positive coefficient. As the authors note, adjusting for system mean differences through country indicators does not demonstrate that school-level relationships are homogeneous, a point long emphasized in methodological work on multilevel modeling of country effects.

Six prespecified families of sensitivity analyses, covering 350 focal comparisons, tested whether the conclusions depended on weighting choices, socioeconomic decomposition, coding decisions, complete-case selection, influential schools, or survey-design variance estimation. Every sensitivity confidence interval overlapped its primary counterpart, and no comparison met the prespecified material-sensitivity criterion. The three nominal signals kept their direction in every available comparison, but they never escaped the multiplicity adjustment. In short, the null finding for school digital conditions is not a fragile artifact of one particular model specification.

What did matter, consistently, was student background. Within-school socioeconomic background showed positive adjusted associations with CIL in all 12 systems, with coefficients ranging from 7.78 to 26.52 CIL points per index point. School socioeconomic composition was also positive everywhere, at 30.57 to 64.49 points per index point, though the authors treat it cautiously because the aggregated school mean had reliability below 0.90 in seven systems. Computer experience was positively associated with CIL in every system, and female students outperformed male students in adjusted comparisons across the board. These results align with prior meta-analytic evidence of a positive, if modest, relationship between socioeconomic status and ICT literacy.

The implications reach beyond academia. As governments pour resources into digital infrastructure and school leaders form opinions about generative AI, this study warns against conflating leadership expectations with classroom reality. A principal who expects ChatGPT to boost learning is not necessarily leading a school where students actually develop stronger digital competencies, and none of the measured school conditions reliably distinguished high-CIL schools from low-CIL schools once socioeconomic and experiential factors were accounted for. The indirect pathway from leadership vision through organizational conditions, teacher practice, and student learning opportunities remains largely unmeasured, and this analysis explicitly did not test whether expectations influenced implementation or whether teacher practices mediated any association.

The authors also flag important limitations. The design is cross-sectional, so no causal or temporal claims are possible, and the 12 systems were defined by participation in the optional ChatGPT questionnaire rather than representative sampling of countries. The focal measures were principal and ICT coordinator reports rather than observations of actual teaching or student AI use, and included students had higher weighted mean CIL than excluded students in every system. Several systems, notably Chile, Norway, and Denmark, contributed fewer than 50 schools, widening school-level confidence intervals. These constraints mean the findings generalize only to the participating systems and samples analyzed.

Still, the study’s core message is a timely corrective to technological optimism. At a moment when generative AI is reshaping debates about homework, assessment, and information literacy, the largest multilevel evidence base yet assembled on principals’ AI expectations and students’ actual digital skills finds no common direct link between the two. What predicts assessed computer and information literacy, in these data, is not what school leaders expect of ChatGPT or their teachers, but the socioeconomic circumstances and accumulated computer experience that students bring with them. Until future research connects leadership expectations to real implementation, teacher enactment, and students’ digital activities, the study suggests, expectations about AI in schools should be read as organizational commentary, not as predictors of learning.

Subject of Research: Associations between school digital conditions and student computer and information literacy across 12 ICILS 2023 education systems

Article Title: School digital conditions and student computer and information literacy across 12 education systems: system-specific multilevel evidence from ICILS 2023

Article References: Chaemchoy, S., & Supsin, T. (2026). School digital conditions and student computer and information literacy across 12 education systems: system-specific multilevel evidence from ICILS 2023. Large-scale Assessments in Education, 14(1), Article 42. https://doi.org/10.1186/s40536-026-00315-9

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00315-9

Keywords: ICILS 2023, computer and information literacy, ChatGPT expectations, school digital conditions, ICT in education, generative AI, multilevel modeling, socioeconomic status, digital inequality, international assessment, plausible values, false discovery rate