Challenge

Trace the Ace

Predict the learning gains from a tutoring session measured by quiz performance in this tutoring outcomes prediction challenge

Tasks
Tutoring
Data types
Human transcript
Tabular
$50,000 in prizes
6 weeks left model submissions
9 weeks left solution write-up
401 joined
Image generated using Gemini

Overview

Individualized tutoring is proven to be one of the best solutions to help struggling students succeed academically. A strong tutor can be the difference between a young child staying on track with learning goals, passing courses, and becoming successful in school, or falling behind. However, detecting exactly what makes a tutoring session effective is far less straightforward. The signals are often buried in nuanced, back-and-forth conversations that vary widely across students, subjects, and teaching styles, making it challenging to reliably assess what effective tutoring actually looks like.

This competition focuses on evaluating tutoring effectiveness using only the student–tutor conversation. Participants will build models that use tutoring session transcripts to predict whether a student goes on to answer a follow-up question correctly - a practical proxy for whether learning actually took place. Developing better ways of identifying effective tutoring could inform how we train educators, guide real-time support, and expand access to high-quality tutoring through AI-powered tools.

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    title Competition Timeline
    dateFormat  YYYY-MM-DD
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    section Phase 1
    Contest opens (Jun 15)              :milestone, m1, 2026-06-15, 0d
    Model submission window              :phase1, 2026-06-15, 2026-08-27
    Submissions close, leaderboard frozen (Aug 27) :milestone, m2, 2026-08-27, 0d

    section Phase 2
    Write-up window, top 15 teams        :active, phase2, 2026-08-27, 2026-09-15
    Write-ups close (Sep 15)             :active, milestone, m4, 2026-09-15, 0d

Prizes

Competition End Date:

Aug. 27, 2026, 11:59 p.m. UTC

Place Prize Amount
1st $15,000
2nd $10,000
3rd $7,000
Publication Bonus (x9) $2,000 each
Total Prize Pool $50,000

Prizes for this competition will not be awarded based on leaderboard ranking alone. Instead, the top 15 teams on the final leaderboard will be invited to submit a solution write-up describing their key insights, methodology, and results. Judges will use a combination of leaderboard performance and write-up quality to select the 1st-3rd place winners.

Publication bonus prize

Judges will invite top teams to participate in the publication bonus prize program. Invited teams will be eligible to receive a publication bonus prize by submitting a publishable-quality preprint submission. Up to 9 teams will receive a publication bonus prize of $2,000.


How to compete

  1. Click the "Compete!" button in the sidebar to enroll in the competition.
  2. Get familiar with the problem through the overview and problem description. You might also want to reference additional resources available on the about page.
  3. Download the data from the data tab.
  4. Create and train your own model.
  5. Bundle your trained model and prediction code for evaluation in our cloud runtime. See the code submission format page for more detail.
  6. Test your submission locally, and in the smoke test environment.
  7. Click “Code jobs” in the sidebar, and then “Make new code submission”. You’re in!
  8. If you are in the top 15 on the leaderboard when model submissions close, you will be eligible for a prize. Summarize your key insights and methodology as described here, then upload your PDF on the "Solution write-up" tab. Prizes will be based on a combination of leaderboard performance and write-up quality.
  9. The top submissions will also be invited to develop their write-up into a full academic paper for publication, with a chance to win an additional publication bonus prize.

The challenge rules are in place to promote fair competition and useful solutions. If you are ever unsure whether your solution meets the competition rules, ask the challenge organizers in the competition forum or send an email to k12-ai-infra@drivendata.org.

External Data and Models

External data and pre-trained models are allowed in this competition This challenge aims to support open solutions with broad social benefit and real-world applicability. To be eligible for prizes, any external data or pre-trained models used must be licensed so that the resulting model can be released for broad use, in and beyond the competition, including for commercial purposes (no NC, CC NC, or CC BY-NC licenses).

Participants may use external data provided they have the legal right to do so. While this data does not need to be shared publicly, the data must be shareable with the challenge organizers to allow for independent result verification and the development of openly licensed models in order to be eligible for prizes. See the external data section in the challenge rules for further details.

If you have questions about licensing in general or whether specific external data and models can be used, post in the competition forum or send an email to k12-ai-infra@drivendata.org.

Prize finalists will be required to declare all external data and pre-trained models used. Each team must either: (1) certify that all resources are licensed to enable commercial model use and provide documentation if requested, or (2) opt out of prize eligibility.


Prize generously supplied by our friends at The National Tutoring Observatory.

NTO Logo


Image Credit: Image generated using Gemini