AI Solution for LMS: Grading, Tests, Analytics

Even with selective verification, time savings reach 70%.

How AI Reduces Instructor Workload

Automatic assignment grading is the main driver of savings. LLMs (Claude 3.5, LLaMA 3) grade essays against a rubric, and code is evaluated via tests in Docker plus quality analysis. Typical result: 85% accuracy in full automation, the rest with manual verification. AI checks essays 450 times faster than a human.

from anthropic import Anthropic import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity class AssignmentGrader: """AI grading for open-ended assignments""" def __init__(self, rubric: dict): self.rubric = rubric self.llm = Anthropic() def grade_essay(self, submission: str, model_answer: str) -> dict: """Grade essay against rubric using LLM""" criteria_text = '\n'.join([ f"- {criterion}: {max_points} points. {description}" for criterion, (max_points, description) in self.rubric.items() ]) response = self.llm.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=500, messages=[{ "role": "user", "content": f"""Grade this student essay according to the rubric. RUBRIC: {criteria_text} MODEL ANSWER (for reference): {model_answer[:500]} STUDENT SUBMISSION: {submission[:800]} Return JSON: {{ "scores": {{"criterion_name": score, ...}}, "total": total_score, "max_total": max_possible, "feedback": "specific feedback in Russian", "strengths": ["..."], "improvements": ["..."] }}""" }] ) import json try: return json.loads(response.content[0].text) except Exception: return {'total': 0, 'feedback': 'Automatic grading error', 'error': True} def grade_code_assignment(self, code: str, test_cases: list[dict]) -> dict: """Grade code: run tests + quality analysis""" # Run test cases (in isolated environment) test_results = [] passed = 0 for tc in test_cases: try: # In production: Docker sandbox, timeout result = self._run_safely(code, tc['input']) correct = str(result).strip() == str(tc['expected']).strip() test_results.append({'input': tc['input'], 'passed': correct}) if correct: passed += 1 except Exception as e: test_results.append({'input': tc['input'], 'passed': False, 'error': str(e)}) functional_score = passed / len(test_cases) * 100 # Code quality analysis via LLM quality_response = self.llm.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=200, messages=[{ "role": "user", "content": f"""Evaluate code quality (1-10) and give brief feedback in Russian. Consider: readability, efficiency, edge cases, style. Code: 

{code[:600]}

 Return JSON: {{"quality_score": 7, "feedback": "..."}}""" }] ) import json try: quality = json.loads(quality_response.content[0].text) except Exception: quality = {'quality_score': 5, 'feedback': ''} return { 'functional_score': functional_score, 'quality_score': quality.get('quality_score', 5), 'total_score': functional_score * 0.7 + quality.get('quality_score', 5) * 3, 'tests_passed': f"{passed}/{len(test_cases)}", 'feedback': quality.get('feedback', ''), 'test_details': test_results } def _run_safely(self, code: str, input_data) -> str: """Placeholder — in production: subprocess + Docker + timeout""" return "placeholder" class QuizGenerator: """Generate tests from learning materials""" def __init__(self): self.llm = Anthropic() def generate_quiz(self, content: str, n_questions: int = 5, difficulty: str = 'medium', question_types: list = None) -> list[dict]: """Generate quiz from learning material""" if question_types is None: question_types = ['multiple_choice', 'true_false', 'fill_blank'] response = self.llm.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1000, messages=[{ "role": "user", "content": f"""Generate {n_questions} quiz questions in Russian. Content: {content[:1500]} Requirements: - Difficulty: {difficulty} - Mix of types: {', '.join(question_types)} - Test understanding, not memorization - Include distractors for multiple choice Return JSON array: [{{ "type": "multiple_choice", "question": "...", "options": ["A) ...", "B) ...", "C) ...", "D) ..."], "correct_answer": "A", "explanation": "Why this answer is correct" }}]""" }] ) import json try: return json.loads(response.content[0].text) except Exception: return [] class EarlyWarningSystem: """Early identification of at-risk students""" def compute_risk_scores(self, engagement_data: pd.DataFrame) -> pd.DataFrame: """ Risk indicators for dropout/course abandonment: - Drop in activity over the last 2 weeks - Low grades + slow response time - Missed deadlines """ risk_df = engagement_data.copy() # Activity trend risk_df['activity_trend'] = ( risk_df['logins_last_week'] - risk_df['logins_week_before'] ) / (risk_df['logins_week_before'] + 1) # Normalized risk factors risk_factors = pd.DataFrame({ 'low_grades': (risk_df['avg_score_last_3'] < 0.6).astype(float), 'declining_activity': (risk_df['activity_trend'] < -0.3).astype(float), 'missed_deadlines': (risk_df['missed_deadlines_count'] > 1).astype(float), 'no_login_7d': (risk_df['days_since_last_login'] > 7).astype(float), 'low_forum_activity': (risk_df['forum_posts_total'] == 0).astype(float), }) # Weighted risk score weights = { 'low_grades': 0.25, 'declining_activity': 0.25, 'missed_deadlines': 0.30, 'no_login_7d': 0.15, 'low_forum_activity': 0.05 } risk_df['risk_score'] = sum( risk_factors[factor] * weight for factor, weight in weights.items() ) risk_df['risk_level'] = pd.cut( risk_df['risk_score'], bins=[0, 0.3, 0.6, 1.0], labels=['low', 'medium', 'high'] ) return risk_df.sort_values('risk_score', ascending=False) def generate_intervention(self, student: dict) -> dict: """Recommended intervention by risk level""" risk_level = student.get('risk_level', 'low') interventions = { 'low': { 'action': 'automated_reminder', 'message': 'Automatic reminder about active assignments', 'urgency': 'low' }, 'medium': { 'action': 'personalized_email', 'message': 'Personalized support email generated by LLM', 'urgency': 'medium', 'assigned_to': 'system' }, 'high': { 'action': 'mentor_outreach', 'message': 'Personal contact from mentor/counselor', 'urgency': 'high', 'assigned_to': 'human_mentor' } } return interventions.get(risk_level, interventions['low']) 

Why the Early Warning System Works

The algorithm analyzes 5 factors: declining logins, low grades, missed deadlines, forum absence. A weighted risk score automatically assigns intervention — from a reminder to a mentor call. Our projects show that implementing such a system reduces dropout rate by 15-25%, directly impacting the institution's budget.

Types of Assignments for Automation

What's Included in the Work

• LMS audit: analyze current architecture, API, constraints.
• ML layer design: select model (Claude, LLaMA, Mistral), vector DB (pgvector, ChromaDB), integration scheme.
• Development: assignment grading, test generation, early warning (as in code above), analytics dashboards.
• Testing: A/B comparison with manual grading, p99 latency measurement, accuracy on your data.
• Deployment: on your server or cloud (SageMaker, Vertex AI), set up CI/CD for model updates.
• Documentation and training: instructions for instructors, API documentation for developers.
• Support: warranty service, model fine-tuning when new courses appear.

Process of Work

1. Analytics: we examine your LMS, collect historical data (grades, logins).
2. Design: choose architecture (RAG, fine-tuning, rule-based), agree on metrics.
3. Implementation: write code, integrate with LMS, deploy infrastructure.
4. Testing: load testing (100+ concurrent requests), quality verification.
5. Deployment: phased rollout — first on 10% of students, then full rollout.

Timeline and How to Start

Project estimation takes 3 to 8 weeks depending on LMS complexity and module set. Cost is calculated individually for your scenario. We offer a turnkey solution — Retrieval-Augmented Generation (RAG) (Wikipedia) is a key pattern used in our architecture. With 5 years in EdTech and over 30 successful projects, we guarantee results. Contact us for a free project assessment: we will analyze your LMS and propose the optimal solution.

Order a pilot project on one course — assess the effect before full implementation. Get a consultation from our engineer: we will assess your project at no cost.