Abstract

The integration of Generative Artificial Intelligence (GenAI) tools into introductory programming (CS1) education challenges established pedagogical and assessment models. This systematic review synthesizes recent empirical literature, anchored by a meta-analysis of 32 controlled studies (2020-2024), to examine what GenAI assistance does and does not do for novice learning. The evidence reveals an efficiency-understanding paradox: relative to unassisted instruction, GenAI use significantly improves student performance scores (Standardized Mean Difference, SMD = 0.86), while gains in conceptual understanding are statistically negligible (SMD = 0.16, falling to -0.03 under sensitivity analysis). A five-profile taxonomy of novice-AI interaction indicates that learning impact depends on how students engage rather than on tool presence. This paper proposes an AI-integrated curriculum redesign framework grounded in constructive alignment, recalibrating learning outcomes, teaching activities, and assessment strategies toward process-based evaluation of the student's problem-solving journey.

References

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