Socratic, Artifact-Gated Generative AI Scaffolding in a 6E Inquiry Unit: A Clustered Quasi-Experiment in Elementary Students’ Computational Thinking

Abstract

Open-ended computational thinking (CT) inquiry challenges one teacher to support learners progressing at different rates. This clustered quasi-experiment evaluated TALPer, a generative-AI tutor providing student-initiated, artifact-contingent Socratic prompts during a six-lesson 6E unit. Ten intact Grade 3 classes in Taiwan (N = 229) completed the same curriculum, tasks, teacher, platform, and instructional time. Conditions differed in a bundled scaffolding configuration involving source, delivery modality, initiation, privacy, and artifact requirements. A two-level model controlling for pretest CT estimated an adjusted posttest difference favoring TALPer ( B = 4.05, SE = 1.58, Wald 95% CI [0.94, 7.15], p = .011; sensitivity p = .034; Hedges’ g = 0.52). Class-clustered analyses found no Holm-adjusted difference on any Intrinsic Motivation Inventory subscale; Perceived Choice had the largest positive estimate ( B = 0.27, cluster-adjusted SE = 0.12, 95% CI [−0.02, 0.55], p = .061, pHolm = .306; Hedges’ g = 0.38). The Condition × Pretest interaction was nonsignificant ( B = −0.109, SE = 0.125, 95% CI [-0.354, 0.137], p = .385). Descriptive coding of 101 matched responses showed different distributions of expected and planned AI-use orientations; no inferential transition test was conducted. Findings provide preliminary evidence concerning the bundled intervention but do not isolate component contributions.

The authors' abstract, as published at the source. Journal of Educational Computing Research, 2026 · DOI ↗