Accountable scientific lead
Must be named before a study begins and remains responsible for design, scientific validity, interpretation, reporting, and correction.
TEAM AND GOVERNANCE
Scientific Roles With Explicit Responsibility
Every proposed study must identify who owns instructional design, learner outcomes, scientific decisions, methods review, data stewardship, required oversight, and final responsibility for claims.
OPERATING MODEL
Accountability Before Execution
AI tools may assist with teaching, analysis, and code, but they cannot hold responsibility. Before a study begins, named human leads must accept responsibility for learner welfare, design, scientific validity, interpretation, reporting, and correction.
CORE FUNCTIONS
Required Roles Across the Research Lifecycle
Learning and domain research
Defines teachable practices, meaningful outcomes, domain-grounded tasks, instructional conditions, and boundaries of interpretation.
Research engineering
Builds reproducible analysis environments, benchmark harnesses, provenance systems, and secure computational workflows.
Methods and oversight
Methods reviewers challenge design and analysis. The responsible institution or authorized review body determines required ethics review and participant safeguards.
PUBLIC ROSTER STATUS
No Named Investigator Is Currently Published
No investigator or institutional affiliation is currently listed because none has been verified for publication.
Future profiles will state the person's role, verified affiliation, scholarly identifier where available, conflicts of interest, and the studies or functions they oversee.
Discuss a research roleWays to Work Together
Defined collaboration scopes for educators, learning scientists, scientific leads, universities, methods specialists, and research infrastructure teams.
Protocol review
Stress-test a planned study before recruitment, data access, or outcome analysis begins.
Multi-lab replication
Coordinate common measures and reproducible analysis across independently run sites.
Benchmark development
Build domain-grounded tasks and expert annotations for AI-for-science evaluation.
Open methods
Create reusable protocols, governance templates, provenance schemas, and reporting guides.
RESEARCH COLLABORATION
Define Responsibility Before Work Begins
Describe the learning context, scientific domain, data constraints, evaluation problem, and expertise already represented. A collaboration scope should identify missing teaching, methods, engineering, data, and oversight roles before execution.
No affiliation is published before both parties confirm it.