Integrating AI literacy into information culture formation for pre-service teachers: a quasi-experimental study with a reflexive-adaptive model

Introduction As artificial intelligence (AI) transforms education, pre-service teachers require competencies beyond traditional information literacy. Existing information culture models lack AI-specific components, and most empirical studies in this domain lack control groups, fail to account for the Hawthorne effect, and do not assess long-term retention. This study reconceptualized information culture by integrating AI literacy as a distinct component, developed a six-component integrative model with a reflexive-adaptive mechanism, and validated formation criteria with psychometric rigor. Methods A quasi-experimental pre-test/post-test control group design was implemented across four universities in Uzbekistan ( N = 498; experimental n = 249, control n = 249) over 16 weeks. The control group received equal instructional hours (44 h) using traditional methods to control for the Hawthorne effect. A validated diagnostic instrument (Cronbach's α = 0.87; test-retest r = 0.84; Kendall's W = 0.83) assessed three criteria: motivational-axiological, cognitive-operational, and reflexive-creative. A delayed post-test was administered 3 months post-intervention. Results The experimental group showed significant improvement: high-level students increased from 8% to 35% ( t = 7.64, p < 0.01; χ 2 = 52.8, p < 0.01; Cohen's d = 0.99). The control group showed modest improvement (7–10%). The delayed post-test confirmed 86% retention of gains ( t = 6.82, p < 0.01), indicating durable competency formation. Discussion The study contributes a validated framework for integrating AI literacy into information culture formation in teacher education. As a single-country quasi-experimental study with cluster-level assignment, its findings should be interpreted cautiously and require replication in other contexts.

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