Editing AI in Education (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Deep Knowledge Tracing for student modeling:'''
<syntaxhighlight lang="python">
import torch
import torch.nn as nn

class DKT(nn.Module):
    """Deep Knowledge Tracing: model student knowledge from exercise sequences."""
    def __init__(self, n_problems, hidden_size=128, n_layers=2, dropout=0.2):
        super().__init__()
        # Input: one-hot encoding of (problem_id, correct/incorrect) → 2*n_problems
        self.input_size = 2 * n_problems
        self.n_problems = n_problems
        self.lstm = nn.LSTM(self.input_size, hidden_size, n_layers,
                            batch_first=True, dropout=dropout)
        self.output = nn.Linear(hidden_size, n_problems)

    def forward(self, input_seqs):
        """
        input_seqs: (batch, seq_len) of encoded (problem, response) pairs
        Returns: (batch, seq_len, n_problems) predicted correctness probabilities
        """
        # One-hot encode: problem p answered correctly → index p; incorrectly → p + n_problems
        x = torch.zeros(input_seqs.shape[0], input_seqs.shape[1], self.input_size)
        x.scatter_(2, input_seqs.unsqueeze(-1), 1.0)
        h, _ = self.lstm(x)
        return torch.sigmoid(self.output(h))  # P(correct) for each concept

# Training: predict next response from history of previous responses
# Loss: binary cross-entropy on next problem's correctness prediction
# Application: given a student's history, predict P(mastery) for each concept
#   → select the problem where P(correct) ≈ 0.7 (optimal challenge)
</syntaxhighlight>

; AI in education application map
: '''Personalized practice''' → DKT + spaced repetition → Khan Academy, Duolingo, IXL
: '''Essay feedback''' → BERT/GPT fine-tuned on rubric-scored essays → Turnitin, Grammarly
: '''Dropout early warning''' → Logistic regression/GBM on LMS engagement data
: '''Question generation''' → LLM fine-tuned on domain content → automatic quiz creation
: '''AI tutoring''' → LLM with Socratic prompting → Khanmigo, Synthesis Tutor
: '''Plagiarism detection''' → Perplexity-based AI detection + semantic similarity
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