{
  "version": "2026-04-24",
  "updated": "2026-04-24",
  "curricula": [
    {
      "id": "klassische-ml-metriken",
      "title": "Klassische ML-Metriken",
      "short": "ML-Metriken",
      "icon": "chart",
      "color": "#0891b2",
      "description": "Accuracy, Precision, Recall, F1, ROC-AUC, Cohen's Kappa, MCC — welche Metrik wann, und warum",
      "modules": [
        {
          "id": "klassifikation-metriken",
          "title": "Klassifikations-Metriken",
          "subtopics": [
            {"id": "acc-prec-rec", "title": "Accuracy vs Precision vs Recall", "objectives": ["Formeln erklären", "Wann welche priorisieren", "Class-Imbalance-Fallstricke erkennen"]},
            {"id": "f1-mcc-kappa", "title": "F1, MCC, Cohen's Kappa", "objectives": ["Harmonisches Mittel verstehen", "Wann MCC besser als F1", "Inter-Rater-Reliability"]},
            {"id": "roc-pr-auc", "title": "ROC-AUC vs PR-AUC", "objectives": ["Threshold-unabhängige Bewertung", "Imbalanced Data: warum PR-AUC besser", "Trade-offs visualisieren"]}
          ]
        },
        {
          "id": "regression-metriken",
          "title": "Regressions-Metriken",
          "subtopics": [
            {"id": "rmse-mae", "title": "RMSE vs MAE vs MAPE", "objectives": ["Skalen-Abhängigkeit", "Outlier-Sensitivität", "Relative Fehler"]},
            {"id": "r2-adjusted", "title": "R² und Adjusted R²", "objectives": ["Erklärte Varianz", "Model-Complexity-Penalty"]}
          ]
        },
        {
          "id": "nlp-metriken",
          "title": "NLP & LLM-Metriken",
          "subtopics": [
            {"id": "bleu-rouge", "title": "BLEU, ROUGE, METEOR", "objectives": ["n-gram-Matching", "Grenzen automatischer Metriken"]},
            {"id": "perplexity", "title": "Perplexity & Token-Metriken", "objectives": ["Information Theory-Grundlage", "Pro-Token-Verlust"]},
            {"id": "llm-as-judge", "title": "LLM-as-a-Judge", "objectives": ["Evaluation mit Modell-Richter", "Bias in der Evaluation"]}
          ]
        }
      ]
    },
    {
      "id": "business-kpis",
      "title": "Business-KPIs für KI",
      "short": "Business-KPIs",
      "icon": "briefcase",
      "color": "#0891b2",
      "description": "ROI, Time-to-Value, Adoption, Kosten-pro-Inference — KI im Unternehmen messbar machen",
      "modules": [
        {
          "id": "roi-tco",
          "title": "ROI, TCO, Time-to-Value",
          "subtopics": [
            {"id": "roi-formel", "title": "ROI-Berechnung für KI-Projekte", "objectives": ["FTE-Äquivalente einrechnen", "Indirekte Effekte quantifizieren", "Amortisationsdauer"]},
            {"id": "tco-hidden", "title": "TCO & versteckte Kosten", "objectives": ["Inferenzkosten vs Trainingskosten", "Operational Overhead", "Vendor-Lock-in"]}
          ]
        },
        {
          "id": "adoption-satisfaction",
          "title": "Adoption & Zufriedenheit",
          "subtopics": [
            {"id": "adoption-rate", "title": "Adoption-Rate & Active-Users", "objectives": ["WAU/MAU-Unterschied", "Benchmarks aus Praxis"]},
            {"id": "csat-nps", "title": "CSAT, NPS für KI-Features", "objectives": ["Feature-spezifische Messung", "Anti-Patterns"]}
          ]
        }
      ]
    },
    {
      "id": "operational",
      "title": "Operational Metrics",
      "short": "Operations",
      "icon": "activity",
      "color": "#0891b2",
      "description": "Latency, Throughput, Availability — KI-Systeme im Produktivbetrieb überwachen",
      "modules": [
        {
          "id": "slo-sla",
          "title": "SLO/SLA-Design",
          "subtopics": [
            {"id": "latency-percentiles", "title": "P50/P95/P99 Latency", "objectives": ["Warum nicht Average", "Percentile lesen"]},
            {"id": "throughput-tps", "title": "Throughput & Tokens-per-Second", "objectives": ["LLM-Durchsatz messen", "Batching-Effekte"]},
            {"id": "availability", "title": "Availability & Error Budget", "objectives": ["SLO-Definition", "Error-Budget-Policy"]}
          ]
        },
        {
          "id": "monitoring-tooling",
          "title": "Monitoring-Stack",
          "subtopics": [
            {"id": "mlflow-arize", "title": "MLflow, Arize, WhyLabs, W&B", "objectives": ["Tool-Landschaft verstehen", "Auswahl-Kriterien"]},
            {"id": "drift-alerts", "title": "Drift-Alerts & Incident-Playbook", "objectives": ["Schwellenwerte setzen", "False-Positive-Rate"]}
          ]
        }
      ]
    },
    {
      "id": "datenqualitaet",
      "title": "Datenqualität & Drift",
      "short": "Daten",
      "icon": "database",
      "color": "#0891b2",
      "description": "DAMA-Dimensionen, PSI, KL-Divergence — Datenprobleme vor Modell-Problemen erkennen",
      "modules": [
        {
          "id": "dama-dimensions",
          "title": "DAMA-Dimensionen",
          "subtopics": [
            {"id": "completeness-accuracy", "title": "Completeness, Accuracy, Validity", "objectives": ["Jede Dimension mit Beispiel", "Messmethoden"]},
            {"id": "timeliness-uniqueness", "title": "Timeliness, Uniqueness, Consistency", "objectives": ["Stale-Data-Risiken", "Dedup-Strategien"]}
          ]
        },
        {
          "id": "drift-detection",
          "title": "Drift-Detection",
          "subtopics": [
            {"id": "psi-kl", "title": "PSI, KL- und JS-Divergence", "objectives": ["Formel und Interpretation", "Schwellenwerte"]},
            {"id": "ks-test", "title": "Kolmogorov-Smirnov-Test", "objectives": ["Nicht-parametrischer Test", "Feature-vs-Label-Drift"]}
          ]
        }
      ]
    },
    {
      "id": "bias-fairness",
      "title": "Bias & Fairness",
      "short": "Fairness",
      "icon": "scale",
      "color": "#0891b2",
      "description": "Demographic Parity, Equal Opportunity, Disparate Impact — Diskriminierungsfreie Systeme",
      "modules": [
        {
          "id": "fairness-metriken",
          "title": "Fairness-Metriken",
          "subtopics": [
            {"id": "demographic-parity", "title": "Demographic Parity", "objectives": ["Formel", "Einschränkungen"]},
            {"id": "equal-opportunity", "title": "Equal Opportunity & Equalized Odds", "objectives": ["Unterschied zu Parity", "Praxisbeispiel HR-Tool"]},
            {"id": "disparate-impact", "title": "Disparate Impact & 80%-Regel", "objectives": ["US-EEOC-Standard", "EU-Bezug"]}
          ]
        },
        {
          "id": "fairness-tooling",
          "title": "Tooling",
          "subtopics": [
            {"id": "aif360-fairlearn", "title": "AIF360 & Fairlearn", "objectives": ["Library-Überblick", "Bias-Audits durchführen"]}
          ]
        }
      ]
    },
    {
      "id": "explainability",
      "title": "Explainability (XAI)",
      "short": "XAI",
      "icon": "eye",
      "color": "#0891b2",
      "description": "SHAP, LIME, Counterfactuals — Modell-Entscheidungen erklärbar machen (EU AI Act Art. 13)",
      "modules": [
        {
          "id": "xai-methoden",
          "title": "XAI-Methoden",
          "subtopics": [
            {"id": "shap-lime", "title": "SHAP vs LIME", "objectives": ["Shapley-Values verstehen", "Lokale vs globale Erklärung"]},
            {"id": "counterfactuals", "title": "Counterfactual & Anchor Explanations", "objectives": ["Minimal-Änderungs-Prinzip", "Praxisnutzen"]}
          ]
        },
        {
          "id": "xai-qualitaet",
          "title": "Qualität von Erklärungen",
          "subtopics": [
            {"id": "fidelity-stability", "title": "Fidelity, Stability, Comprehensibility", "objectives": ["Messgrößen für Erklärungsqualität", "Trade-offs"]}
          ]
        }
      ]
    },
    {
      "id": "robustheit-security",
      "title": "Robustheit & Security",
      "short": "Robustheit",
      "icon": "shield",
      "color": "#0891b2",
      "description": "Adversarial Robustness, Prompt-Injection, Data-Poisoning — Angriffsvektoren verstehen",
      "modules": [
        {
          "id": "adversarial",
          "title": "Adversarial Robustness",
          "subtopics": [
            {"id": "pgd-fgsm", "title": "PGD & FGSM-Attacks", "objectives": ["Perturbations verstehen", "L-infinity-Budget"]},
            {"id": "certified-robustness", "title": "Certified Robustness", "objectives": ["Formale Garantien vs empirische Tests"]}
          ]
        },
        {
          "id": "llm-security",
          "title": "LLM-Security",
          "subtopics": [
            {"id": "prompt-injection", "title": "Prompt-Injection & Jailbreaks", "objectives": ["OWASP LLM Top 10", "Mitigationen"]},
            {"id": "data-poisoning", "title": "Data-Poisoning-Erkennung", "objectives": ["Training-Set-Forensik", "Monitoring-Metriken"]}
          ]
        }
      ]
    },
    {
      "id": "governance-reifegrad",
      "title": "Governance & Reifegrad",
      "short": "Reifegrad",
      "icon": "gauge",
      "color": "#0891b2",
      "description": "Gartner AI Maturity, MIT CISR, Microsoft RAI MM — wo stehst du, wo willst du hin",
      "modules": [
        {
          "id": "reifegradmodelle",
          "title": "AI-Reifegradmodelle im Vergleich",
          "subtopics": [
            {"id": "gartner-maturity", "title": "Gartner AI Maturity Model", "objectives": ["5 Stufen", "Self-Assessment"]},
            {"id": "mit-cisr", "title": "MIT CISR & Microsoft RAI MM", "objectives": ["Unterschiede", "DACH-Anwendung"]}
          ]
        },
        {
          "id": "self-assessment",
          "title": "Self-Assessment-Dimensionen",
          "subtopics": [
            {"id": "strategy-data", "title": "Strategy, Data, Technology", "objectives": ["Fragebogen-Struktur", "Reifegrad-Score berechnen"]},
            {"id": "people-processes", "title": "People, Processes", "objectives": ["Kompetenz-Matrix", "Prozess-Reife"]}
          ]
        }
      ]
    },
    {
      "id": "eu-ai-act",
      "title": "EU AI Act Compliance",
      "short": "EU AI Act",
      "icon": "flag",
      "color": "#0891b2",
      "description": "Risikoklassen, CE-Kennzeichnung, Artikel-Pflichten — Was muss ich ab wann erfüllen",
      "modules": [
        {
          "id": "risikoklassen",
          "title": "Risikoklassen",
          "subtopics": [
            {"id": "verboten-high-risk", "title": "Verboten vs High-Risk", "objectives": ["Art. 5 vs Annex III", "Grenzfälle erkennen"]},
            {"id": "limited-minimal", "title": "Limited & Minimal Risk", "objectives": ["Transparenz-Pflichten", "Chatbots"]}
          ]
        },
        {
          "id": "high-risk-pflichten",
          "title": "High-Risk-Pflichten",
          "subtopics": [
            {"id": "risk-management", "title": "Art. 9 Risikomanagement-System", "objectives": ["Dokumentations-Anforderungen"]},
            {"id": "data-governance", "title": "Art. 10 Data Governance", "objectives": ["Trainings-, Validierungs- und Testdaten"]},
            {"id": "transparency-art13", "title": "Art. 13 Transparenz", "objectives": ["User-Information", "Logging"]},
            {"id": "human-oversight", "title": "Art. 14 Menschliche Aufsicht", "objectives": ["Override-Mechanismen"]}
          ]
        },
        {
          "id": "timeline-ce",
          "title": "Timeline & CE-Kennzeichnung",
          "subtopics": [
            {"id": "phasen-einfuehrung", "title": "Inkrafttreten-Phasen", "objectives": ["2025/2026/2027-Meilensteine"]},
            {"id": "ce-marking", "title": "CE-Kennzeichnung für High-Risk", "objectives": ["Konformitäts-Assessment", "Benannte Stellen"]}
          ]
        }
      ]
    },
    {
      "id": "nist-ai-rmf",
      "title": "NIST AI RMF",
      "short": "NIST",
      "icon": "book",
      "color": "#0891b2",
      "description": "Govern, Map, Measure, Manage — US-Framework mit Crosswalk zu EU AI Act und ISO 42001",
      "modules": [
        {
          "id": "vier-funktionen",
          "title": "Die 4 Kernfunktionen",
          "subtopics": [
            {"id": "govern", "title": "Govern", "objectives": ["Organisations-Kultur", "Policies"]},
            {"id": "map", "title": "Map", "objectives": ["Kontext & Risiken erfassen"]},
            {"id": "measure", "title": "Measure", "objectives": ["KPIs und Tests"]},
            {"id": "manage", "title": "Manage", "objectives": ["Priorisieren, Response, Recovery"]}
          ]
        },
        {
          "id": "crosswalk",
          "title": "Crosswalk zu anderen Frameworks",
          "subtopics": [
            {"id": "nist-vs-eu-ai-act", "title": "NIST vs EU AI Act", "objectives": ["Überlappungen nutzen"]},
            {"id": "nist-vs-iso", "title": "NIST vs ISO 42001", "objectives": ["Mapping-Tabellen"]}
          ]
        }
      ]
    },
    {
      "id": "iso-42001",
      "title": "ISO/IEC 42001 & 23894",
      "short": "ISO",
      "icon": "award",
      "color": "#0891b2",
      "description": "AI Management System, AI Risk Management — zertifizierbare Standards seit 2023",
      "modules": [
        {
          "id": "iso-42001-aims",
          "title": "ISO 42001 — AIMS",
          "subtopics": [
            {"id": "aims-struktur", "title": "Was ist ein AIMS", "objectives": ["Management-System-Struktur", "PDCA für KI"]},
            {"id": "zertifizierungspfad", "title": "Zertifizierungspfad", "objectives": ["Akkreditierte Stellen DACH", "Aufwand schätzen"]}
          ]
        },
        {
          "id": "iso-23894",
          "title": "ISO 23894 — AI Risk Management",
          "subtopics": [
            {"id": "risk-assessment", "title": "Risk Assessment Prozess", "objectives": ["Risiko identifizieren/analysieren/bewerten"]},
            {"id": "komplement-27001", "title": "Komplementarität zu ISO 27001", "objectives": ["Synergie mit ISMS"]}
          ]
        }
      ]
    },
    {
      "id": "scorecards",
      "title": "Scorecards & Dashboards",
      "short": "Scorecards",
      "icon": "clipboard",
      "color": "#0891b2",
      "description": "KPI-Hierarchie Exec/Operational/Technical — vom Dashboard zur Entscheidung",
      "modules": [
        {
          "id": "scorecard-design",
          "title": "Scorecard-Design",
          "subtopics": [
            {"id": "kpi-hierarchie", "title": "Exec-, Operational-, Technical-Layer", "objectives": ["Welche KPI auf welcher Ebene", "Aggregations-Logik"]},
            {"id": "vanity-metrics", "title": "Vanity-Metrics erkennen", "objectives": ["Anti-Patterns", "Actionability-Test"]}
          ]
        },
        {
          "id": "dashboarding-tools",
          "title": "Dashboarding-Tools",
          "subtopics": [
            {"id": "tableau-looker-superset", "title": "Tableau, Looker, Superset, Grafana", "objectives": ["Stärken/Schwächen", "Kosten"]}
          ]
        }
      ]
    },
    {
      "id": "okr-ki",
      "title": "OKRs für KI-Teams",
      "short": "OKR",
      "icon": "target",
      "color": "#0891b2",
      "description": "Objectives & Key Results — Balance zwischen Tech-KPIs und Business-Impact",
      "modules": [
        {
          "id": "okr-grundlagen",
          "title": "OKR-Grundlagen für AI",
          "subtopics": [
            {"id": "obj-vs-kr", "title": "Objectives vs Key Results", "objectives": ["Qualitativ vs quantitativ"]},
            {"id": "balance-tech-biz", "title": "Balance Tech vs Business", "objectives": ["Anti-Pattern: Over-Tech-Indexed"]}
          ]
        },
        {
          "id": "beispiele-dach",
          "title": "Beispiele aus DACH-Mittelstand",
          "subtopics": [
            {"id": "mittelstand-cases", "title": "5 Praxisbeispiele", "objectives": ["Formulierungs-Muster"]}
          ]
        }
      ]
    }
  ],
  "badges": [
    {"id": "erste_metrik", "title": "Erste Metrik-Analyse", "icon": "target", "description": "1. Quiz zu klassischen ML-Metriken bestanden"},
    {"id": "metrik_master", "title": "Metrik-Master:in", "icon": "trophy", "description": "10 Klassifikations-Metriken-Fragen korrekt"},
    {"id": "bias_hunter", "title": "Bias-Jäger:in", "icon": "scale", "description": "Bias & Fairness-Modul abgeschlossen"},
    {"id": "ai_act_navigator", "title": "EU-AI-Act-Navigator:in", "icon": "flag", "description": "EU-AI-Act-Modul abgeschlossen"},
    {"id": "nist_practitioner", "title": "NIST-Praktiker:in", "icon": "book", "description": "NIST AI RMF-Modul abgeschlossen"},
    {"id": "iso_expert", "title": "ISO-42001-Expert:in", "icon": "award", "description": "ISO 42001/23894-Modul abgeschlossen"},
    {"id": "scorecard_architect", "title": "Scorecard-Architekt:in", "icon": "clipboard", "description": "Scorecards-Flashcards bestanden"},
    {"id": "governance_lead", "title": "Governance-Lead", "icon": "crown", "description": "Reifegrad-Modul abgeschlossen"},
    {"id": "kpi_master", "title": "KI-Kennzahlen-Master", "icon": "star", "description": "Alle 13 Curricula abgeschlossen"},
    {"id": "night_owl", "title": "Nachteule", "icon": "moon", "description": "Nach 22 Uhr gelernt"},
    {"id": "early_bird", "title": "Frühaufsteher:in", "icon": "sun", "description": "Vor 7 Uhr gelernt"}
  ],
  "levels": [
    {"min": 0,    "title": "Einsteiger:in"},
    {"min": 50,   "title": "Analyst:in"},
    {"min": 200,  "title": "Data-Scientist:in"},
    {"min": 500,  "title": "ML-Engineer:in"},
    {"min": 1250, "title": "AI-Program-Lead"},
    {"min": 2500, "title": "Head of AI"},
    {"min": 5000, "title": "Chief Data/AI-Officer"}
  ]
}