Educational field-note coding lab

For qualitative methods instructors, turn consented examples and approved rubrics into instructor-reviewed practice activity. Address this specific problem: students struggle to distinguish observation from interpretation. The aim: practice methodological distinctions on examples. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

The buyer creates a project, supplies consented examples and approved rubrics, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final instructor-reviewed practice activity before use. Retain source links and a version history for the next cycle.

How the AI works

AI assists these bounded tasks: separate note types; compare coding choices; explain evidence gaps. Use only consented examples and approved rubrics and preserve uncertainty in instructor-reviewed practice activity. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.

Safeguards

Use educator-reviewed content and answer keys. Apply appropriate access and consent for learner records and distinguish completion from demonstrated learning. One organization, one defined input format and one representative pilot batch using consented examples and approved rubrics. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.

What to build first

Costed pilot: One organization, one defined input format and one representative pilot batch using consented examples and approved rubrics. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: separate note types; compare coding choices. Support the third task through an assisted review queue: explain evidence gaps. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of instructor-reviewed practice activity. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.

What it can connect to

Learning resources, course portals and educator review processes. Learning portals, employee or member directories and completion exports. Validate standards and identity requirements before promising native LMS compatibility. Begin with uploads and exports of consented examples and approved rubrics. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.

The screens in detail

Provide a learner home with the next useful lesson, a practice activity and progress evidence. Give authors a source-linked course editor and assessment review queue. Supervisors see completed tasks and explicit sign-offs. Use short modules that work on mobile as well as desktop. Open with brief and sources; move into educational field-note coding lab for the detailed task; finish in review and delivery for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.