> ## Documentation Index
> Fetch the complete documentation index at: https://docs.learningcommons.org/llms.txt
> Use this file to discover all available pages before exploring further.
# Strength Acknowledgment
> Detect whether feedback names something specific a student did well versus generic praise. Binary strength acknowledgment score with reasoning output.
export const PiiWarning = () => {
return
Inputs must be de-identified. Do not submit students' Personally
Identifiable Information (PII) or any regulated or sensitive personal
information.
;
};
[Evaluator last updated June 24, 2026.](#evaluator-release-history)
## Overview
The Strength Acknowledgment evaluator assesses whether a piece of feedback names something specific and authentic that the student did well.
The evaluator considers whether:
* Praise is authentic or reflexive
* Feedback is specific or generic
* Feedback is anchored to evidence – i.e., a feature of the student's response
* Feedback uses process-vs.-trait framing – i.e., praising what the student did rather than a fixed ability
* Acknowledgment is warranted – e.g., "IDK" should not draw false praise
## At a glance
| | |
| :------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Inputs** | Student writing response and teacher's feedback on the response |
| **Output** | Binary quality score (0/1), with reasoning |
| **Supported grades** | 8–9 |
| **Rubric** | Productive Coaching rubric developed by [Quill.org](https://www.quill.org/) ↗ and [Leanlab Education](https://www.leanlabeducation.org/) ↗, in partnership with [Anastasiya A. Lipnevich](https://www.anastasiyalipnevich.com/) ↗ |
The evaluator was built and validated using the model and temperature below (other configurations will produce different results and may have lower accuracy):
| | |
| :--------------- | :------------------------------------------------------------------------------- |
| **Model used** | GPT-5.4 (`gpt-5.4-2026-03-05`) |
| **Temperature** | 1 |
| **Optimization** | DSPy + GEPA (Genetic-Pareto) prompt optimization against expert-annotated labels |
## Getting started
Follow the [Quickstart](/evaluators/getting-started/quickstart) to start using this evaluator:
| Access method | |
| :------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Evaluators playground** | [View in Platform](https://platform.learningcommons.org/apps/evaluators/playground) ↗ |
| **GitHub** | [Python notebook](https://github.com/learning-commons-org/evaluators/blob/main/evals/feedback/ela-writing/strength-acknowledgment/example_notebook.ipynb) ↗
Prompts: [`system.txt`](https://github.com/learning-commons-org/evaluators/blob/main/evals/feedback/ela-writing/strength-acknowledgment/system.txt) ↗ and [`user.txt`](https://github.com/learning-commons-org/evaluators/blob/main/evals/feedback/ela-writing/strength-acknowledgment/user.txt) ↗ |
## Inputs
| Input | Description | Required |
| :---------------- | :------------------------------- | :------- |
| **Student text** | Student's written response | Yes |
| **Feedback text** | Teacher feedback to be evaluated | Yes |
## Output
| Field | Description |
| :---------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Quality score** | Binary judgment of 0/1
- **0** (Feedback does not name a real strength) - Generic, unanchored, or inaccurate praise
- **1** (Feedback names a real strength) - Names a specific, accurate strength tied to what the student wrote — or appropriately refrains from praise when the response warrants none
|
| **Reasoning** | High-level summary of why the feedback received this judgment |
| **Key features** | List of factors that contributed to the quality score (e.g., presence of praise, specificity, anchoring to evidence, process-vs.-trait framing), each marked met (1) / not met (0) with a justification |
| **Proposed adjustment** | How the feedback could be modified to meet the strength acknowledgment criterion (if applicable) |
```json Example output theme={null}
{
"reasoning": "The student response includes a concrete, relevant idea: AI-powered pets could help around the house. Although the phrase etc. is vague, the helping-around-the-house idea is a promising reason that could be developed with evidence. The teacher feedback explicitly acknowledges that specific idea by saying, You're right, the AI pets could help around the house. This is a direct, response-specific acknowledgment of a strength or valid point in the student's writing. The follow-up question then invites the student to strengthen the claim by adding more details from the article, which builds on that identified strength rather than ignoring it. Because the acknowledgment is specific, grounded in the student's actual wording, and framed as a next step for developing the idea, the feedback meets the criterion.",
"key_features": {
"presence_of_praise": {
"met": 1,
"justification": "The feedback explicitly affirms a specific student idea with \"You're right, the AI pets could help around the house.\" This is authentic acknowledgment rather than generic praise."
},
"specificity": {
"met": 1,
"justification": "The acknowledgment names the particular idea from the student response: that AI pets could help around the house. It is not vague or interchangeable with feedback on any essay."
},
"anchoring_to_evidence": {
"met": 1,
"justification": "The feedback is clearly tied to wording that appears in the student text, specifically the claim about helping around the house. It is grounded in the student's actual response."
},
"process_vs_trait_framing": {
"met": 1,
"justification": "The feedback focuses on the student's claim and a revision move—adding details from the article to strengthen it—rather than praising a fixed trait. This frames the strength as something to build on through writing process."
}
},
"proposed_adjustment": "No adjustment needed; the feedback already meets the criterion. If desired, the teacher could make it even stronger by naming one especially useful detail from the article as a model for expansion.",
"acknowledges_strength_score": 1
}
```
## Interpreting results
| Output | How to use it |
| :------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Acknowledges strength score + Reasoning** | Validate that your AI-generated feedback is building student awareness, not just adding polite filler. Ensure that it includes specific, earned praise. Aggregate reasoning across runs to detect drift toward generic and/or polite openers. |
| **Key features + Proposed adjustment** | Pinpoint and correct the specific failure mode. Adjust prompts to require the model to name a specific feature of the student's work before any directive; target the missing feature directly |
## Evaluator release history
| Date | Changed |
| ------------- | ------------- |
| June 24, 2026 | First release |
## Related topics
Explore other evaluators that assess the quality of coaching feedback.
Run an evaluator in the Evaluators playground, a Python notebook, or with
the SDK.