【2026年最新】AI教育・学習ツール活用ガイド：個別最適化学習からスキルアップまで

from dataclasses import dataclass, field
from typing import List, Dict
import random

@dataclass
class LearnerProfile:
    """学習者プロファイル"""
    learner_id: str
    knowledge_state: Dict[str, float] = field(default_factory=dict)
    learning_history: List[dict] = field(default_factory=list)
    preferred_style: str = "visual"  # visual, auditory, reading, kinesthetic

    def update_knowledge(self, topic: str, is_correct: bool):
        """知識状態の更新（簡易BKTモデル）"""
        current = self.knowledge_state.get(topic, 0.5)
        if is_correct:
            # 正解: 知識推定値を上昇
            learn_rate = 0.15
            self.knowledge_state[topic] = current + learn_rate * (1 - current)
        else:
            # 不正解: 知識推定値を下降
            forget_rate = 0.1
            self.knowledge_state[topic] = current * (1 - forget_rate)

        self.learning_history.append({
            "topic": topic,
            "correct": is_correct,
            "knowledge_after": self.knowledge_state[topic]
        })

class AdaptiveLearningEngine:
    """適応型学習エンジン"""

    def __init__(self, content_pool: Dict[str, List[dict]]):
        self.content_pool = content_pool  # トピック別の教材プール

    def recommend_next(self, learner: LearnerProfile) -> dict:
        """次の学習コンテンツを推薦"""
        # 最も弱いトピックを特定
        weak_topics = sorted(
            learner.knowledge_state.items(),
            key=lambda x: x[1]
        )

        if not weak_topics:
            # 初回は基礎から開始
            return self._get_beginner_content()

        target_topic = weak_topics[0][0]
        knowledge_level = weak_topics[0][1]

        # 知識レベルに応じた難易度を選択
        if knowledge_level < 0.3:
            difficulty = "beginner"
        elif knowledge_level < 0.6:
            difficulty = "intermediate"
        elif knowledge_level < 0.85:
            difficulty = "advanced"
        else:
            # このトピックは十分、次の弱点へ
            target_topic = weak_topics[1][0] if len(weak_topics) > 1 else weak_topics[0][0]
            difficulty = "intermediate"

        return self._select_content(target_topic, difficulty, learner.preferred_style)

    def _select_content(self, topic: str, difficulty: str, style: str) -> dict:
        """コンテンツ選択ロジック"""
        candidates = [
            c for c in self.content_pool.get(topic, [])
            if c["difficulty"] == difficulty
        ]
        # 学習スタイルに合う教材を優先
        style_matched = [c for c in candidates if c.get("style") == style]
        pool = style_matched if style_matched else candidates
        return random.choice(pool) if pool else {"message": "教材準備中"}

    def _get_beginner_content(self) -> dict:
        return {"topic": "introduction", "difficulty": "beginner", "type": "overview"}

# AIベースのスキルアセスメント結果例
skill_assessment = {
    "learner": "田中太郎",
    "assessment_date": "2026-02-12",
    "current_skills": {
        "Python基礎": 0.85,
        "データ構造": 0.70,
        "アルゴリズム": 0.55,
        "Web開発": 0.40,
        "データベース": 0.60,
        "クラウド": 0.25,
        "AI/ML": 0.30
    },
    "recommended_path": [
        {"order": 1, "topic": "アルゴリズム強化", "duration": "2週間", "priority": "高"},
        {"order": 2, "topic": "Web開発基礎（FastAPI）", "duration": "3週間", "priority": "高"},
        {"order": 3, "topic": "データベース設計", "duration": "2週間", "priority": "中"},
        {"order": 4, "topic": "クラウド基礎（AWS）", "duration": "4週間", "priority": "中"},
        {"order": 5, "topic": "AI/ML入門", "duration": "6週間", "priority": "低"}
    ],
    "estimated_completion": "2026-06-15",
    "weekly_study_hours": 10
}