Noetis Learning Coach · Personal engine for app builders
Learning Coach Engine for Education Apps
Builds an individual learning plan and practice from what a learner shows they know.
Answers: "Can an education app give feedback on learner responses to a plan?" For individual learners.
Does not claim mastery or learning outcomes.
Available today
ImplementedImplemented: runs on the shared runtime with supplied material. One bounded development check on 2026-10-12 took 109.1s with 13 model calls and ended "incomplete". That is a development signal, not an evaluation. Customer access is by approved account.
What you send
- Topic and goal
- The learner's own answers or notes
- Optional source material
- Text material up to 150,000 characters per run
What comes back
- Interactive learning plan with practice exercises and checked answers.
- Findings with the located quote and a support status from claim review
- Release status: released, incomplete with reasons, or failed
- Executed roles, usage and measured cost
Sample
Authored illustration. Written by Encevra to show the workflow. Not customer data and not a recorded model output.
Help me learn fractions; here are my answers.
Q1: 1/2 + 1/3 = 2/5 (my answer). Q2: 3/4 of 20 = 15 (my answer). Q3: which is bigger, 2/3 or 3/5? I said 3/5. Goal: pass a placement test in 3 weeks.
- Feedback tied to the learner's own responses
- Next practice steps from the supplied plan
- Points to check with a teacher
The real response shape is documented in the API reference.
How the workflow operates
Each role is a responsibility in the workflow, served by a third party language model. More roles are not automatically better; depth and routing decide which run.
Built with
Models this engine uses: OpenAI (GPT), Anthropic (Claude), NVIDIA (NVIDIA). Specialists run on OpenAI models and the claim review and final brief on Anthropic models. If a provider has a temporary failure, the step can fall back to an OpenAI model, and the run records that.
Knowledge Assessor
Infer what the learner understands only from their supplied responses, and flag what is untested.
Explanation Designer
Adapt explanations to the learner's level and the supplied source material.
Practice Creator
Write exercises that target the identified gaps, in increasing difficulty.
Answer Verifier
Work each exercise and check the answer key is correct and supported.
Evidence and deployment
Evidence
This engine reads the text you send in the request. Each released finding carries the exact quote and its character span in that text, located mechanically before release. Quotes that cannot be located are labelled Insufficient evidence and never shown as source verified. Interpretation is reviewed as a separate step, and gaps such as a missing clause describe what was searched instead of citing text that is not there.
Deployment
Runs on Encevra hosted infrastructure using the third party models listed above. Every model call and fallback is checked against the deployment policy first. Private cloud and air-gapped deployment are planned; discuss deployment requirements.
What it does not handle
- Grading or certifying learners
- Classroom curriculum design (use Noetis Education)
- Uses only supplied material. No freshness guarantee beyond the documents provided.
- Required for every output during trials.
How to integrate
Send "engine": "learning-coach" with your question and material from your server. Keys stay server side.
How to evaluate it
No published result for this workflow yet. Build a small answer key from your own work and compare against a strong single model baseline. See the evaluation checklist and method.
Cost and rights: pricing and licensing. Showing outputs to your own customers needs a written agreement.
Related engines
- Source-Based Learning Content Engine
- Personal Project Planning Engine
- Writing and Communication Engine for Apps
Content reviewed 2026-10-12 by the Encevra engine team. Engine version 0.1.0 on noetis-core-1.