## 🧪 Project Research Context

**Why this exists.** *Grocery – Driven by Data* doubles as a living research lab for applied analytics. It follows a realistic journey—**structure → integrity → signal → strategy**—to study how humans and AI collaborate across the full data-science lifecycle.

**What we study.**

* **Data maturity:** How teams evolve from spreadsheet chaos to reproducible, analytics-ready pipelines.
* **Human–AI collaboration:** Three modes in practice—**AI Tutor** (guidance), **AI Coder** (co-creation), **AI Agent** (delegation)—and how roles, productivity, and quality shift across them.
* **Reproducibility & rigor:** The impact of clean DDL, versioned notebooks, validation tests, and audit trails on downstream modeling.
* **Interpretability & ethics:** How transparency, fairness checks, and decision accountability travel with the data and models.

**How we study it.**

* **Open artifacts:** Schemas (DDL), ERDs, ETL scripts, notebooks, validation harnesses, and reports are versioned and comparable across participants.
* **Comparable tasks:** Each challenge supplies fixed goals, baseline code, and acceptance criteria to enable apples-to-apples evaluation.
* **Lightweight telemetry (opt-in):** Time-to-completion, error types, and revision patterns help analyze learning curves and collaboration styles.
* **Mixed methods:** Quantitative metrics (e.g., load success, test pass rates, forecast error) plus qualitative reviews of design rationale and trade-offs.

**What “good” looks like.**

* **Reproducible**: End-to-end runs without manual edits; clear seeds, environments, and data contracts.
* **Traceable**: Decisions (imputation, features, modeling) documented with rationale and diagnostics.
* **Responsible**: Bias, leakage, and overfitting checks included by default; results communicated with honest uncertainty.

**Planned outputs.**

* **Benchmarks** for schema integrity, data-quality repair, forecasting robustness, and uplift modeling.
* **Design patterns** for human–AI teamwork across Tutor/Coder/Agent modes.
* **Teaching modules** that map directly from the challenges to course labs and workshops.
* **Promotion playbooks** (later stages) linking model outcomes to ROI, sensitivity, and risk.

**Multi-year arc.**

* **Year 1 – Foundations:** Establish shared datasets, baselines, and validation suites.
* **Years 2–3 – Collaboration:** Compare human-written vs. AI-assisted pipelines; analyze quality, speed, and interpretability.
* **Years 4–5 – Autonomy:** Evaluate Agent-style workflows with human oversight, emphasizing governance, explainability, and reliability.

**Invitation.** Students, practitioners, and researchers are welcome to contribute methods, critiques, and case studies. Industry partners can provide anonymized scenarios or constraints to pressure-test real-world applicability.