Research

Deeper insight into how students actually learn

Current ed-tech platforms capture grades and completion rates. LabNotes.ai records the reasoning behind them — which step a student was attempting, what the grader decided, and the evidence it decided on. One row per turn, de-identified and exportable.

The Data

Structured data at every step of the learning process

Beyond quiz scores and completion rates, LabNotes.ai records how students work through problems — which step they were attempting, how many tries it took, what the grader concluded and on what evidence. Every graded turn is written down as it happens, then exported de-identified.

One row per graded turn: the step attempted, the verdict, and the reasoning behind it

Hint use, attempt counts, and time-on-task recorded per turn, not per problem

Salted pseudonyms — stable within a course, unjoinable across courses

Export to CSV or JSON for analysis in R, Python, SPSS, or any tool

tutoring_turn.jsonOne row · de-identified export
{
  "student_code": "S-7A3F91",
  "occurred_at": "2026-08-31T16:37:27Z",
  "assignment_title": "Measurement and Matter",
  "problem_number": 3,
  "turn_index": 2,
  "target_milestone_label":
    "Identify which stage of the scientific
     method the statement represents",
  "attempts_on_current": 1,
  "milestones_awarded": 2,
  "grader_reason":
    "Student correctly identified the statement
     as a hypothesis and gave two valid
     justifications: that it is a testable
     prediction, and that it followed a prior
     observation.",
  "hints_used_at_turn": 0,
  "is_pasted": false,
  "grading_ms": 3635
}

Research Agenda

Questions this platform is built to investigate

The data architecture is built around these core research areas.

01

Does guided AI tutoring lead to better exam outcomes than answer-providing AI tools?

Comparing exam performance, concept retention, and long-term understanding between students using Socratic-guided AI versus conventional AI assistance.

02

Do students become more independent learners over time?

Tracking hint usage, self-correction rates, and time-to-solution across a full semester to measure growing independence.

03

Does course-specific AI context improve learning transfer?

Controlled comparison of outcomes when AI references instructor-specific materials versus using only general subject knowledge.

04

Can behavioral data predict struggling students before exams?

Using attempt counts, hint dependency, and engagement signals to identify at-risk students early enough to intervene.

Infrastructure

Built for institutional and grant requirements

What is built today, stated plainly — and what is still on the roadmap.

FERPA Compliant

Student data handling is designed to support institutional compliance with FERPA

De-identified Exports

Salted pseudonyms, stable within a course and unjoinable across courses. No names, emails, or transcripts

Exportable Data

CSV and JSON exports for analysis in R, Python, SPSS, or any tool

Reproducible Grading

Grading runs deterministically against an open-source regression suite, so any mark can be re-derived

LMS Integration

Moodle grade sync today. LTI and SSO are on the roadmap, not yet built

Pilot Support

Dedicated onboarding, technical support, and data consultation for research partners

Let's build the evidence together

Our first cohort is underway in an undergraduate chemistry course, and the data described above is being collected now. If you want to study AI-assisted STEM learning with real data, we should talk.

Get in Touch