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README.md

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+# BERT-based Multi-label Cognitive Load Classifier
+
+This model is a fine-tuned `bert-base-uncased` transformer trained to classify **students' cognitive and psychological states** (e.g., cognitive load, confidence, anxiety) from naturalistic **human-AI educational dialogues** in K-12 settings.
+
+## 🧠 What does the model do?
+
+The model performs **multi-label classification** on student-AI conversations, identifying whether a given interaction reflects one or more of the following cognitive and affective states:
+
+- Math Confidence / Math Anxiety
+- AI Confidence / AI Concerns
+- Intrinsic Cognitive Load
+- Extraneous Cognitive Load
+- Germane Cognitive Load
+
+Each input text (a single conversation) may correspond to **multiple labels simultaneously**.
+
+---
+
+## 📚 Training Data
+
+The model was trained on a custom dataset collected from a large-scale empirical study involving **160 K-12 students** interacting with an AI-powered teachable agent in a math learning platform (ALTER-Math, name anonymized for review).
+
+- **Dialogues**: 1,440 student-agent interactions over 10 days
+- **Labels**: Derived from pre- and post-questionnaires grounded in Cognitive Load Theory and affective constructs
+- **Label types**: Binary indicators (0/1) per psychological factor
+- **Preprocessing**: Tokenized using Hugging Face's `AutoTokenizer`, padded to max length of 128
+
+---
+
+## 🏋️‍♂️ Training Setup
+
+- Model: `bert-base-uncased`
+- Task: Multi-label text classification
+- Loss: BCEWithLogitsLoss
+- Optimizer: AdamW
+- Batch Size: 16
+- Epochs: 5
+- Learning Rate: 1e-5
+- Evaluation Strategy: Hold-out test set (20%)
+
+---
+
+## 🚀 Intended Use
+
+This model is designed to support **AI-based unobtrusive assessment of cognitive load** in education, enabling:
+
+- Researchers to monitor how students respond cognitively and emotionally to AI tutors
+- Developers to build more adaptive, trustworthy AI learning agents
+- Teachers to gain insight into student engagement and overload without invasive devices
+
+---
+
+## 📌 Limitations
+
+- The dataset size is modest (N=160), and model generalization to other domains or age groups is not guaranteed.
+- Labels are inferred from questionnaire-aligned criteria, which may include subjectivity.
+- The model does not currently handle out-of-distribution input or code-switching effectively.