Prompt engineering as cognitive scaffolding for ethical and explanatory quality in AI-mediated financial learning

Abstract Background Large language models (LLMs) are increasingly used in personal finance education, yet the role of instructional prompt design in shaping explanatory quality and ethical responsiveness remains underexplored. This matters because trustworthy financial guidance and instruction depend on clarity, accuracy, and appropriately cautionary framing. Method We conducted a Design-Based Research (DBR) Phase 1 artefact-validation study using content analysis of 120 GPT-4-turbo (May 2025) generations. Ninety diagnostic prompts varied linguistic framing, cognitive level, and ethical intent; thirty Phase 1b prompts instantiated a derived taxonomy. Two expert reviewers scored outputs (0–3) for financial accuracy, conceptual clarity, and ethical tone (inter-rater reliability: $$\kappa =0.82$$ ). Results Prompt form was a pedagogical determinant of content quality. Procedural prompts outperformed interrogatives on clarity ( $$U=418$$ , $$p