Direct-Answer Versus Socratic Generative AI Support in Programming Learning: Differences in Epistemic Laziness, Computational Thinking, and Computational Thinking Network Structures

Abstract

Generative artificial intelligence can support programming learning, but differences associated with response modes remain unclear. This two-class quasi-experiment compared direct-answer support (n = 57) with Socratic support (n = 48) across three 90-minute sessions in a Java course. Pre- and post-intervention questionnaires assessed epistemic laziness and five computational thinking dimensions, while reflection logs were analyzed using epistemic network analysis. After adjustment for corresponding pretest scores, Socratic support showed lower self-reported epistemic laziness and higher abstraction, decomposition, algorithmic thinking, and evaluation scores, with no significant difference in generalization. Network analysis revealed distinct computational thinking configurations: direct-answer support showed stronger decomposition–algorithmic-thinking and algorithmic-thinking–evaluation connections, whereas Socratic support showed stronger abstraction–decomposition, abstraction–generalization, and decomposition–generalization connections, suggesting different patterns of problem analysis, solution development, and knowledge transfer. Given the two-class design, findings are interpreted as between-case differences. Overall, response mode may shape computational thinking outcomes and the organization of related processes in programming learning.

The authors' abstract, as published at the source. International Journal of Human-Computer Interaction, 2026 · DOI ↗