From prompts to programmes: natural language programming as a metacognitive scaffold for GenAI-assisted problem solving

Purpose Generative artificial intelligence (GenAI) can support programming education but may also encourage cognitive offloading when learners request complete solutions instead of developing problem-solving competence. This paper aims to frame natural language programming (NLPg) as a metacognitive scaffold prioritising problem framing, decomposition and verification over answer production. The contribution is primarily pedagogical, positioning NLPg as a competence-oriented practice for human-centred AI-supported learning in digital and cyber humanities contexts. Design/methodology/approach The study reports an exploratory pilot in a master’s digital humanities course. Thirteen student prompt logs (439 prompts) were analysed using cue dictionaries and three trace-based indicators capturing metacognitive regulation, delegation versus co-reasoning and decomposition with explicit validation. Trace data were complemented by rubric-based outcomes from oral project defences. Findings Prompt logs reveal a characteristic “build-and-check” pattern: planning and monitoring cues are frequent, while reflection is the least stable NLPg component. Rubric evidence shows weaker evaluation in the baseline cohort and higher scores for framing, decomposition and validation in the GenAI-enabled cohort despite increased task complexity. Research limitations/implications The study is exploratory and context-specific; findings rely on a small cohort and do not support causal claims, calling instead for longitudinal and cross-institutional validation. Practical implications Assessment in AI-rich learning environments should prioritise traceable reasoning through specifications, decomposition artefacts, validation evidence and brief reflection notes. Social implications Competence-oriented NLPg can strengthen learner agency and reflective engagement in AI-mediated learning and knowledge environments. Originality/value The study links NLPg routines, competence frameworks and trace-based evidence to support competence-oriented GenAI pedagogy aligned with DigComp 3.0 and emerging cyber humanities perspectives.