Hey ChatGPT—Is a Louis Vuitton Bag an Investment? Evaluating LLM Readiness for Use in Financial Literacy and Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The prevalence of large language models (LLMs) such as ChatGPT has wowed the world with its ability to generate text in a human-like manner. While educators evaluate how AI will impact the future of learning, we identify mistakes ChatGPT has made. We further extend this concern to nonfinancially sophisticated users seeking to improve their financial literacy who may not possess the financial acumen to determine when the AI is hallucinating. Using a longitudinal study, our analysis frames the prompts and subsequent findings within the four stages of the Dunning-Kruger effect to explore how users of varying expertise receive output from the LLMs. We find that ChatGPT cannot always fully distinguish between three different user groups. Our findings have important implications for accountants, educators, and students using LLMs as a tool in work and education and for the general population looking to bypass financial experts for their personal finance needs. Data Availability: Data will be made available upon request. JEL Classifications: M41.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it