ayjays132/AI_Mastery_Foundation_Curriculum

Hugging Face-ready Parquet package

AI Mastery Foundation Curriculum

A premium staged foundation dataset for building models with a cleaner first layer of academic knowledge, reasoning discipline, preference judgment, benchmark awareness, and tool-use behavior before they move into larger downstream corpora.

31,331Total packaged rows

20Grade bands

10Subjects per grade

0Duplicate IDs

0Duplicate prompt-response pairs

8HF configs

A foundation layer built for serious training pipelines

Most datasets are raw piles of examples. This one is structured like a training system: concepts first, supervised answers next, preference contrasts after that, reward references for scoring, benchmark rows for evaluation, and agentic traces for tool-aware behavior. It is designed to make the earliest stage of a model pipeline feel less chaotic and more curriculum-shaped.

Curriculum-first Rubric-backed Preference-ready Benchmark-aware Agentic-tool-use ready Modern HF Parquet

What This Dataset Is

AI Mastery Foundation Curriculum is a staged training and evaluation package spanning Pre-K, K-12, undergraduate, graduate, and doctoral learning. It is organized for practical AI training workflows: curriculum grounding, supervised fine-tuning, preference optimization, reward-model/reference judging, benchmark evaluation, and agentic tool-use behavior. The goal is not just to provide rows, but to provide a progression: what the model should know, how it should answer, how better answers differ from weaker ones, and how performance should be checked.

Curriculum graph SFT conversations RLHF pairs Reward references Benchmarks Agentic traces Curated expansion

Design intent

The package is built as a foundation layer: broad enough to cover academic basics, structured enough for training pipelines, and clean enough to be mixed with later domain corpora. Each major training surface keeps prompts, responses, rubrics, scoring dimensions, titles, and metadata aligned so the dataset can support training, judging, retrieval, filtering, and inspection.

Recommended Use

Use this as an early foundation and alignment-oriented curriculum layer: first expose a model or training pipeline to clean educational structure, worked reasoning, expected answers, rubrics, and preference contrasts; then continue into larger domain-specific or general-purpose corpora. The dataset is also useful for model evaluation because the benchmark, reward, and agentic configs keep task format, rubric, and metadata consistent.

Before large-scale tuning

Use it as a structured primer so early training sees ordered knowledge instead of only loose internet-style text.

During alignment work

Use preference, reward, and rubric fields to connect answer quality with explicit scoring criteria.

During evaluation

Use held-out benchmark and agentic configs to track reasoning, uncertainty, transfer, and tool behavior.

During dataset ops

Use the shared schema, titles, metadata, and Parquet layout for filtering, dashboards, audits, and mixing.

Training Stage Map

Stage 01Curriculum Grounding

Use curriculum to expose the model or indexing layer to grade, subject, concept, prerequisite, misconception, and objective structure.

Stage 02Supervised Reasoning

Use sft for instruction-following, worked answers, mistake correction, and grade-appropriate response style.

Stage 03Preference Alignment

Use rlhf to teach contrastive judgment between strong and weak answers with explicit preference reasons.

Stage 04Reward and Judging

Use reward for rubric-backed references, scalar scoring setups, or LLM-judge calibration.

Stage 05Benchmark Evaluation

Use benchmark as held-out evaluation across mastery, transfer, reflection, and capstone-style tasks.

Stage 06Agentic Behavior

Use agentic for tool routing, trace shape, verification, uncertainty, and escalation behavior.

Config Purpose Guide

all

Unified table for broad mixing, inspection, sampling, and lightweight baseline training.

curriculum

Concept-level map across grade bands, subjects, objectives, prerequisites, and misconception metadata.

sft

Conversation-style supervised rows for instruction following and answer construction.

rlhf

Chosen and rejected answers for preference optimization and reward comparisons.

reward

Reference-answer rows with rubrics and scoring dimensions for judge or reward-model work.

benchmark

Held-out test split for capability tracking, regression checks, and curriculum coverage audits.

agentic

Tool-use traces and agentic test items for routing, verification, and escalation behavior.

curated_expansion

Large cleaned expansion of examples and solutions folded into the package as normal dataset rows.

Configs and Counts

Config	Split/File	Rows
all	all/train.parquet	31,331
curriculum	curriculum/train.parquet	200
sft	sft/train.parquet	2,730
sft	sft/validation.parquet	270
rlhf	rlhf/train.parquet	1,003
reward	reward/train.parquet	3,000
benchmark	benchmark/test.parquet	1,240
agentic	agentic/train.parquet	600
agentic	agentic/test.parquet	600
curated_expansion	curated_expansion/train.parquet	21,688

Schema

Every split uses the same schema so configs can be combined safely:

id, record_type, grade_level, subject, concept_id, title, text, messages, prompt, response, chosen, rejected, rubric, scoring_dimensions, metadata, source_file.

Nested details are preserved as JSON text in metadata where useful. The shared schema makes the package simple to load with the modern Hugging Face dataset-card config system.

id
Stable row identifier.

record_type
Config/source role.

grade_level
Pre-K through PhD.

subject
Academic domain.

prompt
Task or instruction.

response
Reference answer.

chosen
Preferred answer when available.

rejected
Contrastive weak answer.

rubric
JSON scoring guide.

metadata
JSON route details.

Loading

from datasets import load_dataset

sft = load_dataset("./hf_dataset", "sft")
curated = load_dataset("./hf_dataset", "curated_expansion")
agentic = load_dataset("./hf_dataset", "agentic")

Direct Parquet loading also works:

from datasets import load_dataset

ds = load_dataset("parquet", data_files={"train": "./hf_dataset/all/train.parquet"})

Quality Status

Verified loadingAll configs load with current Hugging Face datasets using load_dataset("./hf_dataset", config).

Clean IDsDuplicate IDs are 0 in every packaged split.

Clean pairsDuplicate prompt-response pairs are 0 in every packaged split.

Portable formatThe public package uses standard Parquet files and dataset-card YAML configs.

Workflow Fit

Foundation pass

Sample curriculum, sft, and curated_expansion to build broad educational coverage before heavier domain corpora.

Alignment pass

Mix rlhf and reward when training preference, scoring, ranking, or judge components.

Evaluation pass

Use benchmark and agentic/test as held-out checks for reasoning, transfer, tool use, uncertainty, and escalation.

Inspection pass

Use all for search, filtering, dashboards, metadata QA, and unified analytics over the entire package.

Creation Provenance

The dataset is authored, selected, structured, cleaned, and packaged as the AI Mastery Curriculum dataset. Some expansion drafting used the Gemini API during local development, then the resulting rows were curated, deduplicated, renumbered, normalized, and repackaged into this publishable dataset format.