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AI_033predictionEducation2.3x-learning-rate

AI-powered mastery learning allows students to master traditional academics in merely 2 hours/day, achieving learning rates 2.3x faster than statistical models predict — freeing human educators to transition into 'Guides' focused on emotional support, ...

Predictor: MacKenzie Price

Prior probability
78.0%
Current probability
65.9%
evolves via intake + LBP
Conviction
5/5
Signal quality
B
Resolution
in_progress
Window
2026-01-01 – 2026-05-31
Edges in / out
0 / 0
Tickers exposed
0

Prediction text

AI-powered mastery learning allows students to master traditional academics in merely 2 hours/day, achieving learning rates 2.3x faster than statistical models predict — freeing human educators to transition into 'Guides' focused on emotional support, motivation, and human agency cultivation. | Alpha School million-student RCT release

Key catalyst: Alpha School million-student RCT release

Watch events: Alpha School RCT publication; MAP/NWEA 2026 assessment data

Resolution evidence

Status: in_progress

Alpha School ~13 schools operational with MAP/ACT data supporting mastery-learning outcomes; planned RCT (233_019) will formalize evidence.

Predictor: MacKenzie Price

κ + Brier as of 2026-05-22
κ (discount)
0.500
Brier
Hits / Misses
0 / 0
Hit rate

Evidence about this node from MacKenzie Price is multiplied by κ in /api/intake. Lower κ = less weight; floors at 0.10 (effectively silenced) and caps at 1.00 (full weight).

Reference class

Not linked

This node isn't linked to a reference class. The Bayesian update applies without outside-view blending.

Probability over time

1 prob_history rows
0%25%50%75%100%prior 78%2026-05-02
intake v2milestone miss sweeplbp propagationreference class assignedlegacy v1prior_prob (analyst seed)current = 65.9%

Milestone chain

Pre-event signals (upstream prereqs + window checkpoints) → resolution event → downstream cascades. Status/dates update from linked nodes; re-derive nightly via scripts/ops/derive_milestones.py.
Leading chain: 3 fired ✓ · 3 overdue ⏱
  1. 2026-01-29hitMainstream press coverage of 2-hour academic model (CNN/NYT)
    How: Tier-1 mainstream outlet (CNN, NYT, WaPo, WSJ) publishes substantive feature on Alpha 2-hour AI mastery learning model
    Source: https://www.cnn.com/2026/01/29/politics/alpha-school-trump-ai-teachingconf 95%
    Notes: HIT — CNN feature ran Jan 29 2026.
  2. 2026-02-01overdueQ1 window check-in (25%)
  3. 2026-02-15hitAlpha District Winter 2025-2026 MAP results clear 99th percentile threshold
    How: Alpha publishes MAP Growth report showing district averages >99th percentile in Reading/Math/Language
    Source: https://alpha.school/blog/alphas-mid-year-report-card-is-in-heres-what-the-data-actually-says/conf 95%
    Notes: HIT — Alpha mid-year report shows district average beyond 99th percentile.
  4. 2026-02-15hitAlpha students average 2.6x growth rate vs national MAP norms
    How: Independent MAP Growth comparison shows Alpha cohort growth >=2.3x national norm
    Source: https://alpha.school/blog/alphas-mid-year-report-card-is-in-heres-what-the-data-actually-says/conf 90%
    Notes: HIT — Alpha reports 2.6x growth, exceeding the predicted 2.3x rate.
  5. 2026-03-05overdueQ2 window check-in (50%)
  6. 2026-04-06overdueQ3 window check-in (75%)
  7. 2026-05-01 → 2027-12-31pendingMillion-student RCT or large-N independent replication published
    How: Peer-reviewed or pre-print study with N>=100,000 students validates 2.3x learning rate vs control
    Source: Education research journals (AERA, EdWorkingPapers)conf 30%
    Notes: Marketing claims still rely on internal data per The 74 + AstralCodexTen reviews.
  8. 2026-06-01 → 2027-12-31pendingIndependent 3rd-party validation of 2-hour model effectiveness
    How: External research org (NWEA, Brookings, RAND) publishes independent evaluation of Alpha cohort using non-Alpha test instrument
    Source: NWEA / RAND / Brookings ed-research releasesconf 40%

What if this resolves?

Clamp this prediction TRUE or FALSE and run a counterfactual Gibbs sample. Surfaces the predictions whose marginals shift most under that assumption.
(live posterior: 66%)

Click a button to clamp this prediction and run a Gibbs sample. Returns the predictions whose marginals shift most. ~30s per run; ideal for stress-testing "if X resolves, what else moves?"

Evidence chain

Every probability update with full Bayesian provenance — chronological, latest first
metadata_milestone_miss_sweep2026-05-02T22:07:21Z65.9%-12.1pp
metadata_milestone_miss_sweep bayesian_v2 n=3 inside=0.659 blend=0.659 LLR=-0.608 κ=0.50 no_blend
Raw metadata
{
  "trf": 0.18718816479722222,
  "kappa": 0.5,
  "base_rate": null,
  "predictor": "MacKenzie Price",
  "total_llr": -1.2163953243244932,
  "grace_days": 7,
  "bayesian_v2": true,
  "prior_logit": 1.265666373331276,
  "bayes_factor": "1.8:1 against",
  "blend_reason": "no reference_class linked",
  "inside_prior": 0.78,
  "kappa_source": "predictor_table",
  "n_milestones": 3,
  "blend_applied": false,
  "contributions": [
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      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.5,
      "label": "Q1 window check-in (25%)",
      "weight": 0.05,
      "strength": "weak",
      "confidence": null,
      "source_url": null,
      "adjusted_llr": -0.2027325540540822,
      "expected_date": "2026-02-01",
      "measurement_criterion": null
    },
    {
      "llr": -0.4054651081081644,
      "kind": "quartile_checkpoint",
      "kappa": 0.5,
      "label": "Q2 window check-in (50%)",
      "weight": 0.05,
      "strength": "weak",
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      "expected_date": "2026-03-05",
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    {
      "llr": -0.4054651081081644,
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      "kappa": 0.5,
      "label": "Q3 window check-in (75%)",
      "weight": 0.05,
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      "adjusted_llr": -0.2027325540540822,
      "expected_date": "2026-04-06",
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  ],
  "evidence_kind": "metadata_milestone_miss_sweep",
  "inside_source": "prior_prob",
  "inside_weight": 0.8689682846419444,
  "outside_weight": 0.13103171535805558,
  "posterior_prob": 0.6586915405096169,
  "posterior_logit": 0.6574687111690295,
  "predictor_brier": null,
  "inside_posterior": 0.6586915405096169,
  "blended_posterior": 0.6586915405096169,
  "reference_class_id": null,
  "total_adjusted_llr": -0.6081976621622466,
  "predictor_n_resolved": 0
}

Network propagation neighbors

Top edges sorted by latest LBP cross-impact
All propagation →

No propagation data yet. Run inference/.venv/bin/python scripts/ops/run_loopy_belief_propagation.py on the droplet, or wait for the Sunday 02:00 UTC weekly cron.

Prerequisites (0)

Predictions that must hit first
TypePredTitleDomainLag
No prerequisites

Dependents (0)

Predictions enabled by this
TypePredTitleDomainLag
No dependents

Validations (1)

Resolution events
Observed atStatusByNotes
2026-04-29partialthesis_timeline_v1.0_importAlpha School ~13 schools operational with MAP/ACT data supporting mastery-learning outcomes; planned RCT (233_019) will formalize evidence.

Linked documents (9)

Auto-generated by cosine similarity from Polymarket / Manifold / EDGAR / GDELT

Raw metadata

From Thesis_Timeline_v1.0_FINAL workbook
{
  "nia": false,
  "qty": "2.3x / 2 hrs/day",
  "mode": "OBSERVATION+FORECAST",
  "role": "Cited-CEO",
  "context": "Specific quantitative extension of 233_003 (99th percentile capability) and INF_041 (AI-native schools). The '2.3x faster' and 'Guides' framing are Price-distinctive.",
  "to_year": 2026,
  "conv_cues": "specific quantitative benchmark; operational",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "2026 operational",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Mainstream press coverage of 2-hour academic model (CNN/NYT)",
      "notes": "HIT — CNN feature ran Jan 29 2026.",
      "source": "https://www.cnn.com/2026/01/29/politics/alpha-school-trump-ai-teaching",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://www.cnn.com/2026/01/29/politics/alpha-school-trump-ai-teaching",
      "expected_date": "2026-01-29",
      "observed_date": "2026-01-29",
      "research_origin": "deep_research",
      "measurement_criterion": "Tier-1 mainstream outlet (CNN, NYT, WaPo, WSJ) publishes substantive feature on Alpha 2-hour AI mastery learning model"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q1 window check-in (25%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -5,
      "source_id": null,
      "expected_date": "2026-02-01",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "llm_pre_event",
      "label": "Alpha District Winter 2025-2026 MAP results clear 99th percentile threshold",
      "notes": "HIT — Alpha mid-year report shows district average beyond 99th percentile.",
      "source": "https://alpha.school/blog/alphas-mid-year-report-card-is-in-heres-what-the-data-actually-says/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -4,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://alpha.school/blog/alphas-mid-year-report-card-is-in-heres-what-the-data-actually-says/",
      "expected_date": "2026-02-15",
      "observed_date": "2026-02-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Alpha publishes MAP Growth report showing district averages >99th percentile in Reading/Math/Language"
    },
    {
      "kind": "llm_pre_event",
      "label": "Alpha students average 2.6x growth rate vs national MAP norms",
      "notes": "HIT — Alpha reports 2.6x growth, exceeding the predicted 2.3x rate.",
      "source": "https://alpha.school/blog/alphas-mid-year-report-card-is-in-heres-what-the-data-actually-says/",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -3,
      "source_id": null,
      "confidence": 0.9,
      "source_url": "https://alpha.school/blog/alphas-mid-year-report-card-is-in-heres-what-the-data-actually-says/",
      "expected_date": "2026-02-15",
      "observed_date": "2026-02-15",
      "research_origin": "deep_research",
      "measurement_criterion": "Independent MAP Growth comparison shows Alpha cohort growth >=2.3x national norm"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q2 window check-in (50%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -2,
      "source_id": null,
      "expected_date": "2026-03-05",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "quartile_checkpoint",
      "label": "Q3 window check-in (75%)",
      "status": "overdue",
      "weight": 0.05,
      "ordinal": -1,
      "source_id": null,
      "expected_date": "2026-04-06",
      "observed_date": null,
      "miss_emitted_at": "2026-05-02T22:07:21.384228+00:00",
      "miss_emitted_by": "metadata_milestone_sweep"
    },
    {
      "kind": "event",
      "label": "A
... (truncated)