language: en
license: apache-2.0
tags:
- education
- trustworthy-ai
- reinforcement-learning
- llama-3
- large-language-model
- alignment
- k12
- ai-education
datasets:
- custom-k12-student-teacher-dialogues
- safe-generated-reverse-data
model-index:
- name: Llama 3 - RLRGD Educational Trustworthy AI
results:
- task: text-generation
dataset: custom-k12-student-teacher-dialogues
metrics:
- name: EvaluLLM (Edu-Facilitativeness)
type: custom
value: 0.87
- name: BLEU
type: bleu
value: 21.3
- name: ROUGE-L
type: rouge
value: 43.8
📘 Llama 3 - RLRGD Educational Trustworthy AI
This model is a fine-tuned version of Meta's Llama 3, developed using our novel RLRGD (Reinforcement Learning from Reverse-Generated Data) framework to meet the demands of trustworthy AI in K-12 education. It is designed to provide pedagogically appropriate, safe, and engaging responses in the context of educational dialogue—particularly in math education scenarios involving student-teacher Q&A.
🧠 Model Details
- Base Model: Llama 3 (13B)
- Training Method: RLRGD (custom reinforcement learning pipeline with contrastive learning from reverse-generated data)
- Data Sources:
- Real K-12 student-teacher dialogues from the ALTER-Math dataset (anonymized and preprocessed)
- Reverse-generated unsafe/inadequate samples for contrastive feedback
- Safe-LLMs generated synthetic augmentations
- RL Objective: Encourage the model to maximize alignment with teacher-like facilitative responses and minimize unsafe, irrelevant, or overly verbose outputs
✅ Intended Use
This model is intended for use in AI-powered educational applications, such as:
- Interactive math tutors
- AI-powered teacher assistants
- Educational chatbot environments for K-12 students
It is ideal for scenarios requiring trustworthiness, personalization, and pedagogy-aware feedback.
❗ Limitations
- Although the model has been fine-tuned for safety and educational soundness, it should not be used without teacher supervision in high-stakes learning environments.
- The model may still produce hallucinated or out-of-scope answers when prompted with out-of-domain queries.
- Performance may vary outside of math education or K-12 contexts.
🛡️ Safety and Alignment
The model was trained with safety and alignment in mind:
- Reverse-generated contrastive training to suppress unsafe or unhelpful outputs
- Human-in-the-loop reinforcement using expert-rated teacher dialogues
- Use of Safe-LLMs during generation and evaluation for toxicity filtering
We apply strict preprocessing:
- Removal of personal identifiers
- Filtering of inappropriate language
- Scaffolding principles from ZPD, Constructivism, and Scaffolding Theory
