This preprint introduces MEDS, a corpus for studying how 14 nominal LLM folders respond about mathematics under two conditions: as a persona-free AI assistant or while role-playing a synthetic person with sociodemographics, favourite and disliked subjects, and OCEAN traits. Each run is intended to preserve five JSON artifacts covering four tasks: a seven-question interview; three self-efficacy and anxiety scales with justifications; two semantic-association batches; and 18 multiple-choice problems with explanations and confidence. The genuine contribution is the open corpus: it exposes prompts, answers, ratings, associations and metadata for descriptive comparisons across families and modes. The paper reports more varied score distributions under personas, negative mathematics associations in some models, classifier-labelled fallacies and accuracy-confidence gaps. These findings characterize generated text; they do not validate the profiles as realistic students. There are no human participants or population comparisons, and no reliability, factor structure, measurement invariance or criterion validity is reported for treating LLM scale responses as psychometric measurements. EmoAtlas measures emotional vocabulary and DistilBERT labels text; neither establishes felt emotion, anxiety, cognition or human-adjudicated fallacies. Full audit of all 34 pages, TeX source and the public commit finds 139,948 valid JSON files rather than 140,000. There are 28,000 started runs but only 27,987 complete runs: thirteen human-mode Granite runs preserve only the interview. Of 55,974 association outputs, 194 violate the required 25 cues; nine mathematical records replace expected question identifiers with values such as 20, 45, 80, 200 or 360; 23 answers have no reasoning and 59 final summaries are empty. The scale CSV exactly mirrors the 27,987 complete records and 1,287,402 items. In contrast, the demographics CSV has 27,543 personas, only 21,004 of which belong to complete human-mode runs; 6,539 are outside the selection and include two models absent from the final corpus. Ten folders mix model aliases or endpoints without checkpoint digests. The only released code is an unexecuted generation notebook: ROLE_MODES contains human twice, it requests 1,800 sessions with no seed and uses a placeholder model, so it cannot produce the 6,983 baseline LLM runs, the exact 14 environments, selection, cleaning or figures. Code for BERT correction, EmoAtlas, fallacy classification, networks, statistics and calibration is absent. Figure 8 has a further lineage break: public accuracy reproduces 13 of 14 bars, but none of the confidence bars equals the stated mean transformation `(score-1)/4`; Grok is 0.942 in released JSON versus 0.884 in the SVG, and Mistral Small 4 is 0.755 versus 0.508. Ministral 3B accuracy is 0.350 in the figure but 0.486 in the current corpus. Mathematical item 18 also uses BD in its proportion while supplying BC, so the published answer assumes an unwritten correction. MEDS is useful as a synthetic collection for exploratory audits and tool design, but it does not establish human realism, educational effectiveness, tutor safety or real group differences.
Research question
How to build and describe a multimodal corpus of synthetic responses that allows comparing mathematical performance, declared confidence, anxiety, self-efficacy, explanations, and semantic associations of different LLMs under assistant mode and simulated human person mode?