See the Platform in Action

The MAPLES dashboard showing 46 active rubrics, 93 student groups, and 182 grading runs — a production system processing thousands of encounters.

Rubric management view: a rubric with 64 prompts across audio, video, and note modalities, organized by OSCE station. Each item is mapped to a grading mode and scoring level.

Rubric level definitions: each item has clear scoring criteria (1-4) that both human raters and AI use — ensuring consistent interpretation across graders.

Grading configuration: select a student group, map rubrics to stations, enable optional features (localizer, mosaic videos), and launch AI grading with one click.

Multimodal review: synchronized video playback with AI-generated transcript and item-level assessment. Faculty can accept the AI score or flag for manual review.

Post-encounter note grading: the student's clinical note alongside the AI assessment, rationale, and rubric scoring guide. Faculty review each item with full context.

Batch review interface: 6,780 items across an entire OSCE cohort. Faculty can filter, accept AI scores, or drill into individual items for manual review.

Pipeline cost transparency: 622 encounters graded for $26.10 total ($0.04 per encounter) using Gemini 2.5 Pro — full token-level cost breakdown for institutional budgeting.

How MAPLES fits in the full workflow: (1) OSCE encounter captured via multi-angle cameras, (2) Platform — SimRubrics for rubric design, Elephant for multimedia cataloging, MAPLES for AI grading orchestration, (3) Human review with faculty adjudication and grades reported to students.

What an OSCE looks like: medical students rotate through standardized patient scenarios (e.g., Itchy Eyes, Leg Pain, Memory Problems), each filmed from overhead cameras. Every encounter is graded — this is what MAPLES automates.

The rubrics-to-prompts workflow published in NEJM AI — translating clinical rubrics into AI grading specifications.

Production deployment model at UTSW showing the human-in-the-loop review pipeline.

End-to-end grading pipeline: data ingestion, AI assessment, quality review, and feedback delivery.

MAPLES platform architecture showing the relationship between SimRubrics, Elephant, and the grading engine.

Multi-camera OSCE room setup with AI segmentation and grading pipeline — frontal, posterior, and overhead cameras feed into Gemini for physical examination assessment.

Cohen's Kappa across modalities: native 3-camera video achieves kappa = 0.837, demonstrating that "full video is king" — outperforming audio-only, transcript, and single-camera configurations.

Macro F1 scores by rubric item and modality configuration — showing how different physical exam maneuvers benefit from different camera angles and input types.