MYNOEMA·The measurement layer for counselor training2026

Turn "this training works" into measurable infrastructure

AI takes on the practice and measurement that scale; people keep the encounters that matter. MyNoema builds the measurement layer for counselor training — wherever a claim of "this works" has to hold up, it should pass through this layer.

▶Demo

See Anamna run, in 90 seconds

A real session with a simulated client — and the utterance-level behavioral coding and four layers of structured feedback the system returns.

The full 90-second demo, hosted on YouTube. Open in a new tab ↗

01Problem

The pipeline for qualified counselors breaks in three places

The demand needs no argument — the breakdown does. Three structural failures in the pipeline, one of them independently measured.

No bridge
Between graduation and practice-ready hours
Training programs offer only a modest amount of supervised individual-counseling practice, while independent practice demands far more. No institution reliably supplies the hours in between — and few trainees can close that gap on their own.
Too few
Qualified supervisors, in every market
Accredited supervision is scarce everywhere: a small pool of certified supervisors against a large and growing population of trainees. The bottleneck is not demand for training — it is the capacity to supervise it.
d = −0.52
Empathy decline from practice without feedback
Randomized controlled trial (n=94): practice without structured feedback not only fails to build skill — empathy performance declines significantly over time. The dominant teaching method, peer role-play, falls squarely into this category.

Louie et al., CHI 2026 — a randomized controlled trial (independent third-party study). The practice-hour gap and supervision shortage are structural constraints documented across counselor-training systems.

Thesis

Whether the dialogue feels human is available to everyone.
Whether the practice works, and who proves it — that is our entire engineering.

02Products

Both a flight simulator and the instruments to read it

Pilots don’t learn takeoffs and landings on a real plane, and counselors shouldn’t learn the basics on real clients. A simulator is worth only as much as two things: a controllable, reproducible environment and accurate instruments — and that’s where all of our engineering goes.

Deployed · in institutional pilot

Anamna

AI simulated-client training system

Trainees run sessions with behaviorally consistent, reproducible simulated clients; the system scores every utterance against an internationally established behavioral coding scheme (MITI) and generates four layers of structured feedback — engineering the ingredients RCTs have shown to work (practice + structured feedback).

  • "Code decides what, the LLM decides how" — a state machine controls clinical pacing, curing character drift and non-reproducibility
  • State-machine parameters calibrated on 4,785 state-transition pairs from public counseling corpora
  • Utterance-level MITI behavioral coding; every piece of feedback quotes the trainee’s own words
  • Four feedback layers: behavior counts · process trajectory · conceptualization comparison · key-moment replay
  • Three simulated clients fully modeled with cognitive conceptualization diagrams (CCD), in both text and voice modes
Try Anamna
Research project · by invitation

CBT Dialogue Study

CBT-based self-help emotional-support dialogue · for research

A self-help emotional-support dialogue system driven by structured CBT sessions, used to study whether structured, safety-first dialogue can be more helpful than a general-purpose chatbot. Not therapy, not a public product — consent-gated and limited to invited participants.

  • Structured CBT sessions that actively guide rather than passively react; cross-session continuity and a homework-practice loop
  • Per-message side-channel safety classification and crisis referral (without probing method details)
  • Every session states plainly that it is "not a substitute for professional help," holding a strict non-therapeutic boundary
Enter / join the study

Our boundaries · what we deliberately don’t do

✕

No substitute for real client hours

Simulated practice is a training bench before real hours, and a multiplier on them.

✕

No substitute for supervision

Low-level correction goes to the coding engine; scarce supervision is reserved for conceptualization and the therapeutic relationship.

✕

No public-facing commercial companionship product

The CBT study above is consent-gated, non-public, and for research only.

✕

Scores are not used for high-stakes decisions

Until transfer-validity evidence is established, system scores serve as formative feedback only.

03Research

Evidence first, efficacy second

Whether the dialogue feels human comes from the underlying model and is available to everyone; whether the practice works, and who proves it — that’s where our engineering lands. Every claim can be checked by hands-on trial or peer review.

R/01

Corpus analysis of 109,262 utterances

Behavioral-code annotation and state-transition analysis across 5 public datasets, yielding 4,785 state-transition pairs to calibrate the simulated-client state machine — clinical pacing derived from data, not prompt guesswork.

R/02

Gold-standard reliability engineering

A double-human-coded gold-standard set, inter-rater reliability (IRR) management, and a 40-case regression suite — scoring behavior is not allowed to regress across releases. Only when measurement is trustworthy does feedback earn the name.

R/03

Claims land on people

Our interpretability research found empathy representations inside LLMs are decodable but not reliably steerable. So every efficacy claim lands on trainees’ measured outcomes, not the model’s generation quality — a conclusion from our own research.

R/04

Research in progress

A behavioral-coding distribution comparison between AI simulated clients and real counseling corpora (a study type still absent from the literature); plus MITI gold-standard reliability and trainee pre/post studies, on a 12-month roadmap.

Related paper

Detection ≠ Reliable Control: Empathy Directions in LLMs Are Decodable but Not a Reliable Steering Lever

BlackboxNLP @ EMNLP 2026 (submitted)

04Team

Don’t take our word for it — verify it yourself

MyNoema was founded by a team with backgrounds in psychology and computer science at UC Berkeley and human-computer interaction at USC. What this field lacks isn’t a bigger model, but infrastructure — controllable training, trustworthy measurement, and verifiable claims.

Try it

The system is live; institutions can trial it hands-on, not just watch a demo.

simpatient.mynoema.org →
Audit it

The scoring process is transparent end to end: utterance-level records, expert-annotated reference answers, and 40 safety tests.

Calibrated on 4,785 real dialogue pairs
Review it

The research is submitted to peer review, and the architectural choices are documented.

Backed by first-party research · BlackboxNLP 2026
05Partner

Build it with us

We’re looking for: university programs with a clinical-counseling track (co-developed courses · joint research · pilot deployments), counselor-training institutions, and academic partners.

Or email us directly: [email protected] · [email protected]

Thanks for reaching out — we’ll get back to you soon.