Introduction: Why automated feedback is crucial today
Teachers today face growing class sizes and limited time for grading. Timely, personalized feedback is key to student success, but traditional methods require hours of manual work. Modern Large Language Models (LLMs), equipped with tool-calling capabilities and specialized training in education, can automate this process without sacrificing quality. Let’s explore how to choose the right AI tool and implement it effectively and ethically.
Modern LLMs: Tool-calling and contextual learning
Key features to look for
- Native support fortool-calling(e.g., functions to extract evaluation criteria, connect to an LMS, or generate certificates)
- Specialization ineducational domains(including Italian, science subjects, and coding)
- Transparency andprivacy controls(compliant with GDPR and Italian regulations on student data protection)
- Integration with existing systems via APIs or plugins for platforms like Moodle, Google Classroom, or Office 365
How to choose the right AI tool for your school
Assessing needs
- Types of feedbackrequired (grammatical corrections, improvement suggestions, project evaluations, motivational comments)
- Volume of processingneeded (from a few dozen to thousands of essays per day)
- Technical compatibilitywith current systems (single sign-on, single-tenant vs. multi-tenant, offline options)
A practical approach is to test ademowith a small group of students, measuring the relevance of comments, response time, and acceptance by both students and teachers.
Practical example: A prompt for structured feedback
Imagine we want to generate feedback for an Italian essay. The following prompt uses the model’s tool-calling ability to extract evaluation criteria from an external database.
Code snippet: Integration with an LMS
Here’s a minimal Python example showing how to call a tool-aware LLM to evaluate an assignment and automatically record the result in an LMS database.
import requests
import json
# 1. Configure the model endpoint (e.g., a private API hosting a fine-tuned XYZ-Eagle-SFT model)
API_URL = "https://api.tuo-llm-edu.com/v1/eval"
HEADERS = {"Authorization": "Bearer TUO_TOKEN", "Content-Type": "application/json"}
# 2. Define the tool (function) that the model can call to get evaluation criteria
TOOL_DEFINITION = {
"name": "estrai_criteri_valutazione",
"description": "Returns evaluation criteria for a given subject and task type",
"parameters": {
"discipline": "Italian",
"level": "high school",
"task_type": "argumentative_essay"
}
}
# 3. Send the assignment and tool to the model
payload = {
"input_text": "La scuola digitale... (user's text)",
"tools": [TOOL_DEFINITION],
"max_tokens": 500,
"temperature": 0.3
}
response = requests.post(API_URL, headers=HEADERS, json=payload)
result = response.json()
# 4. The model may have called the tool; extract evaluation and comments
if "tool_calls" in result:
criteri = result["tool_calls"][0]["output"]
# Evaluate the user's text based on the criteria
valutazione = calcola_valutazione(payload["input_text"], criteri)
# Register the result in the LMS database via another API call
registra_in_lms(student_id="123", task_id="t001", score=valutazione["punteggio_composito"], feedback=valutazione["commenti"])
else:
print("No tool call: use the generated text directly as feedback")
registra_in_lms(student_id="123", task_id="t001", feedback=result["text"])Best practices and ethical considerations
- Human supervision:AI-generated comments must always be reviewed by a teacher before being sent to students, to ensure accuracy and sensitivity.
- Personalization:Adapt the tone and level of detail to students' age, abilities, and learning styles.
- Transparency:Inform students and parents that part of the feedback is generated by AI, in compliance with Italian regulations on transparency in data use.
- Privacy:Use on-premise or school-level solutions to keep student data within the educational system.
Conclusion: From reactive to predictive feedback
AI tools based on LLMs are rapidly evolving from simple spell checkers to true pedagogical partners. Thanks to tool-calling capabilities, integration with existing systems, and the continuous evolution of specialized models, teachers can now provide in-depth and personalized feedback almost instantly, freeing up time for more creative and interactive activities. Choosing the right solution, following best practices, and maintaining human oversight ensures that technology enhances teaching without replacing it, preparing students to face the challenges of the future with confidence.
IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (August 2026): Here are some recent trends and news to inspire you: - Novo Nordisk and AWS bring agentic AI into drug discovery: Novo Nordisk is expanding its use of AWS artificial intelligence tools across drug discovery, including AI agents for target identification, therapy d... [2026-08-11] - Developing an End-to-End Document Intelligence Pipeline with docTR for OCR, Layout Analysis, KIE, Benchmarking, and Searchable PDFs: Develop a complete document intelligence pipeline with docTR, integrating OCR, layout analysis, and KIE for production-oriented extraction and searcha... [2026-08-17] - Google tests AMIE for clinical video consultations: Google's research medical AI system, AMIE (Video), conducted synchronous video consultations with professional patient actors and received clinical ... [2026-08-12] Use this current information as inspiration to create an original and relevant prompt for 2026.