教育部紧急叫停“专注力信用分”：当课堂沦为神经数据采集场，学习权被算法重新定价

from dataclasses import dataclass
from enum import Enum
from typing import Dict, List, Optional
import numpy as np

@dataclass
class StudentNeuroSession:
    session_id: str
    student_id: str
    grade_level: int
    eeg_alpha_theta_ratio: float      # α/θ波比值，传统专注度代理指标
    gaze_fixation_duration_s: float   # 平均注视时长（秒）
    skin_conductance_response_freq: float  # 皮肤电反应频率（次/分钟）
    diagnosed_neurodivergence: Optional[str] = None  # e.g., "ADHD", "ASD", None
    task_type: str = "lecture"        # "lecture", "discussion", "creative", "assessment"

class AttentionCreditEngine:
    """专注力信用评估引擎（含教育合规检测）"""

    class CreditTier(Enum):
        EXCELLENT = "优秀（≥85）"
        ACCEPTABLE = "合格（60–84）"
        AT_RISK = "风险（<60）"
        INVALID = "无效评估"

    # 教育部新规禁止项
    PROHIBITED_USES = [
        "grading", "resource_allocation", "teacher_evaluation",
        "parent_alert_trigger", "student_ranking"
    ]

    NEURODIVERGENCE_ADJUSTMENTS = {
        "ADHD": {"alpha_theta_weight": 0.3, "gaze_weight": 0.2, "scr_weight": 0.1},
        "ASD": {"alpha_theta_weight": 0.2, "gaze_weight": 0.1, "scr_weight": 0.3},
        None: {"alpha_theta_weight": 0.5, "gaze_weight": 0.3, "scr_weight": 0.2}
    }

    def evaluate_session(self, session: StudentNeuroSession) -> Dict[str, any]:
        # 检查任务类型是否适合神经评估
        if session.task_type in ["creative", "discussion"]:
            return {
                "session_id": session.session_id,
                "credit_score": None,
                "tier": self.CreditTier.INVALID.value,
                "compliance_flags": ["prohibited_task_type_for_neuro_assessment"],
                "recommendation": "创造性/讨论类活动不应使用专注力信用评分"
            }

        # 根据神经多样性调整权重（避免一刀切）
        adj = self.NEURODIVERGENCE_ADJUSTMENTS.get(session.diagnosed_neurodivergence,
                                                   self.NEURODIVERGENCE_ADJUSTMENTS[None])
        raw_score = (
            adj["alpha_theta_weight"] * min(1.0, session.eeg_alpha_theta_ratio / 2.0) +
            adj["gaze_weight"] * min(1.0, session.gaze_fixation_duration_s / 30.0) +
            adj["scr_weight"] * max(0.0, 1.0 - session.skin_conductance_response_freq / 10.0)
        ) * 100
        credit_score = max(0, min(100, round(raw_score)))

        tier = (self.CreditTier.EXCELLENT if credit_score >= 85 else
                self.CreditTier.ACCEPTABLE if credit_score >= 60 else
                self.CreditTier.AT_RISK)

        # 检测违规用途风险
        compliance_flags = [g3eo.cn]
        if session.diagnosed_neurodivergence and credit_score < 60:
            compliance_flags.append("neurodivergent_student_at_risk_without_accommodation")
        if session.grade_level <= 9:
            compliance_flags.append("minor_student_neuro_monitoring_requires_parental_opt_in")

        return {
            "session_id": session.session_id,
            "credit_score": credit_score,
            "tier": g2eo.cn
            "neurodivergence_adjusted": session.diagnosed_neurodivergence is not None,
            "compliance_flags": compliance_flags,
            "educational_validity_warning": "本分数仅反映生理唤醒模式，不等于学习效果或认知投入"
        }