🎯 ai_rubric_grader

It's a small, dependency-light, pure-Dart package — no Flutter, no HTTP client baked in — so you can run it server-side (a Cloud Function, a backend, a CLI), which is where your API key belongs.

Why not just ask the model for marks?

Asking an LLM to "grade this and give me the score" and parsing the free text is fragile: a stray line or markdown fence silently becomes a 0, and the model can hand out more points than a rubric allows. ai_rubric_grader fixes that:

Install

dependencies:
  ai_rubric_grader: ^0.1.1

or dart pub add ai_rubric_grader.

Usage

import 'package:ai_rubric_grader/ai_rubric_grader.dart';

final questions = [
  Question(
    id: 'q1',
    prompt: 'What is the derivative of x²?',
    answer: '2x',                       // plain text, LaTeX, transcribed handwriting…
    rubrics: const [
      Rubric(id: 'r1', description: 'Correct derivative', requirement: 'equals 2x', points: 5),
      Rubric(id: 'r2', description: 'Shows working',      requirement: 'steps shown', points: 3,
             autoGraded: false),         // left for a human
    ],
  ),
];

final grader = RubricGrader(myLlmClient);
final result = await grader.grade(questions);

print('${result.awarded}/${result.possible} (${result.percentage}%)');
for (final g in result.questionGrades) {
  for (final s in g.rubricScores) {
    print('${s.rubricId}: ${s.awarded}/${s.possible} — ${s.rationale}');
  }
}

Bring your own LLM

Implement the one-method LlmClient for any provider. Request deterministic output (temperature: 0) and JSON mode where available.

import 'dart:convert';
import 'package:ai_rubric_grader/ai_rubric_grader.dart';
import 'package:http/http.dart' as http;

class OpenAiClient implements LlmClient {
  OpenAiClient(this.apiKey, {this.model = 'gpt-4o-mini'});
  final String apiKey;
  final String model;

  @override
  Future<String> complete({required String system, required String user}) async {
    final res = await http.post(
      Uri.parse('https://api.openai.com/v1/chat/completions'),
      headers: {
        'Content-Type': 'application/json',
        'Authorization': 'Bearer $apiKey',
      },
      body: jsonEncode({
        'model': model,
        'temperature': 0,
        'response_format': {'type': 'json_object'},
        'messages': [
          {'role': 'system', 'content': system},
          {'role': 'user', 'content': user},
        ],
      }),
    );
    return (jsonDecode(res.body)['choices'][0]['message']['content']) as String;
  }
}

🔒 Security: never embed an LLM API key in a mobile/web client — it ships in the binary and can be extracted. Run the grader behind your backend.

API

All models are immutable and JSON-serializable (toJson / fromJson).

Related packages

Part of the Flutter Grading Toolkit:

• rubric_builder — author rubrics in a Flutter UI.
• handwriting_answer_pad — capture handwritten answers.
• image_white_background — prep images for vision models.

Contributing

Issues and PRs welcome. Run dart test before submitting.

License

MIT © 2026 Muhammad Ali