Studio

Self-Regulated Teaching Intelligence

An instructor-facing research prototype that turns ordinary class recordings into inspectable, evidence-linked feedback for post-class reflection.

Explore itResearch prototype · Internal · Live-classroom pilot ahead
SRTI live analysis view showing session metadata, a four-stream pedagogical timeline, and the speaker-attributed transcript.
ScreenshotThe live analysis view: session metadata, the four-stream pedagogical timeline, and the transcript in one place. The session is a public MIT OpenCourseWare lecture analyzed by the live pipeline, not private classroom data.

The problem

Instructors get little timely, structured evidence about their own classrooms: end-of-semester evaluations arrive too late, peer observations are rare, and memory is a weak basis for judging talk balance, wait time, questioning, or participation.

The approach

Class audio becomes a speaker-attributed transcript and four streams of pedagogical events (content, instructor moves, student engagement, and reflection triggers), alongside deterministic measures like talk ratio, wait time, and question rate. The pipeline is deterministic-first: the language model proposes events, deterministic code decides, and every event is verbatim-anchored so an instructor can jump from any claim to the passage behind it.

Research basis

Self-regulated teaching is the instructional counterpart to self-regulated learning: instructors need evidence to set goals, monitor their practice, and adapt. SRTI turns that construct into a working instrument, and its pilot (20 open-courseware lectures, human-ratified reference labels, per-stream accuracy results) is the lab studying its own tool before trusting it.

What it does today

  • Ingests class audio, transcribes it locally, and separates speaker turns with editable attribution.
  • Organizes transcript-grounded observations into four streams: content events, instructor moves, student engagement, and reflection triggers.
  • Computes deterministic measures: instructor/student talk ratio, wait time, turn counts, longest monologue, question rate and type.
  • Lets an instructor filter the session timeline by stream or speaker and jump from any claim to its supporting passage.
  • Keeps analysis version history, with model and schema metadata on every run.
  • Ships a reproducible evaluation harness with a 20-lecture human-ratified reference corpus.

What it does not do

  • No hosted product, self-service onboarding, or production support; this is a private, internally deployed prototype.
  • Not validated in live classrooms; the pilot corpus is public courseware, which underrepresents student interaction.
  • Audio-only: it cannot observe body language, movement, visual materials, written work, or silent engagement.

Inside the prototype

A selected student contribution linked to the supporting transcript passage.
ScreenshotEvery analytic claim links to the transcript passage that supports it; here, a student contribution and its source. Public courseware audio, with speakers appearing only as anonymous labels.
Reflection-trigger cards with urgency labels and transcript evidence.
ScreenshotReflection triggers with urgency labels and transcript evidence: the stream that feeds post-class reflection, and the weakest performer in the pilot (F1 0.22 after rubric revision). Labeled experimental for exactly that reason.
Architecture diagram: class recording through ASR and diarization, deterministic-first LLM extraction, validation, metrics, and dashboard feedback.
DiagramThe deterministic-first pipeline: the language model proposes events per chunk, mechanical code merges and validates, and every event is verbatim-anchored before it reaches the dashboard.
Timeline figure showing 91 verbatim-anchored pedagogical events across four streams during an 80-minute session.
Diagram91 verbatim-anchored events across the four streams in one 80-minute session, extracted live by the platform from a public MIT OpenCourseWare lecture.

Evidence

Working pipeline on real lecture audio

The screenshots are live pipeline output over an 80-minute public MIT OpenCourseWare lecture, with 91 verbatim-anchored events across four streams. They are not seeded fixture data.

As of 2026-08-28

Measured, including the failures

A 20-lecture pilot across five disciplines: 1,346 candidate events, 1,112 retained after human ratification, 100% verbatim-anchor resolution, and per-stream accuracy reported honestly from F1 0.68 down to 0.22.

As of 2026-07

Engineering depth

1,363 automated tests documented in a dated internal evidence memo, alongside a reproducible evaluation harness.

As of 2026-07-06

What we are not claiming

  • The pilot shows feasibility, not that the system accurately evaluates teaching. Concrete student-engagement events were strongest (detection F1 ≈ 0.68, 0.86 type agreement on matched events); instructor moves reached only ≈ 0.33 and reflection triggers 0.22 after rubric revision. The weak streams are named on purpose.
  • The pilot is small and bounded: 20 lectures, one run each, no confidence intervals, and reference labels that were LLM-assisted then human-ratified rather than an independent expert-coded gold standard.
  • It is a professional-development and research tool. It is not designed, and should not be used, for employee evaluation or student surveillance.

Responsible use, privacy, and rights

  • Real classroom audio, transcripts, student identities, speaker maps, and instructor analytics are never published, and live-classroom use requires consent, IRB, and privacy review.
  • Interface imagery uses public MIT OpenCourseWare material with attribution; no private classroom data appears.

More from the Studio

All entries

Use it

Course Sphynx

A faculty-facing platform for helping instructors redesign courses through guided, transformation-oriented support.

Explore it

GATOS qualitative analysis workflow

A research workflow for using open-source generative AI and machine learning to support inductive qualitative codebook development.

Explore it

CATS QDA

A local-first workspace where qualitative researchers analyze sensitive interview data with AI assistance they can inspect, override, and audit.