Learning to Prompt, Prompting to Learn: Novice Programmers' Self-Regulated Learning with GenAI

• In Event: AERA Research in Progress Roundtable Session One
  In Invited Roundtable: Research-in-Progress RT021 (Wednesday)

Abstract

The rise of Generative AI (GenAI) is transforming programming education, yet how learners self-regulate in these new environments remains underexplored. This qualitative case study uses a think-aloud protocol and interviews to investigate the Self-Regulated Learning (SRL) strategies of a novice programmer using AI for a statistical task. Preliminary findings from a single case reveal an adaptive, yet instrumental, SRL pattern. The participant evolved his prompting strategy from a tedious step-by-step method to a holistic “task dumping” approach for maximum efficiency. This revealed gaps in conceptual monitoring, such as ignoring statistical assumptions provided by the AI, suggesting an SRL process optimized for production over understanding.

Author

• Shumin Zhao, University of Nevada - Reno