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Mage Duel Serious Game - TACFI

AFWERX · AFWERX TACFI · AFWERX

AI-Readiness Score
16/25
Pathway Speed
4/5
Timeline Realism
3/5
Problem Framing
3/5
AI / ML Fit
3/5
Award + Transition
3/5

Award

$1,499,922
Award ceiling
TUTORWORKS, INC.
Awardee
Posted June 11, 2024

Description

Military linguists acquire an enormous volume of vocabulary each day either through DoD basic acquisition courses or through continuation training for DoD language professionals, and struggle for years to achieve the optimum balance of speed versus accuracy in their translation work. This leads to high attrition rates in DoD basic language courses, long ramp-up times for operational language analysts and atrophy of perishable language skills for DoD language professionals. Acquisition and retention of specialized and highly perishable language skills are critical, given world events and Human Language Technology (HLT) which have resulted in high proficiency demands on military language professionals, while the disappearance of public school and university foreign language programs inhibits DoD-wide language professional recruitment efforts. Given these critical needs, USAF and DoD language analysts require an individualized cradle-to-grave solution for vocabulary acquisition and translation proficiency. Our serious game Mage Duel will solve this critical problem aided by bleeding edge technologies. Mage Duel is a serious video game that combines language learning concepts, learning science, and HLT to autonomously accelerate vocabulary acquisition and train timely and accurate translation through adaptive individualized learning activities.

Score Rationale

TACFI is a legitimate fast-track OTA instrument under AFWERX, earning a near-top pathway score, but the unknown response deadline and lack of explicit prototype milestones cap timeline realism at 3. The problem framing is genuine — attrition, ramp-up time, and skill atrophy are real, measurable DoD pain points — but success criteria (e.g., target vocab acquisition rates, translation accuracy benchmarks) are not concretely specified in the description, and data availability for adaptive learning models is unconfirmed. AI/ML fit is meaningful but secondary: the core deliverable is a serious game, with adaptive learning and HLT as supporting components rather than the primary innovation surface, making this more of an edtech build with AI hooks than a pure AI-native problem. The ~$1.5M ceiling with TACFI's implicit transition pathway to STRATFI or program-of-record lands this squarely in the middle award tier.

Source

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