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