Treemarks
Treemarks provides AI-generated conceptual feedback and proposed grades for university STEM courses, letting teaching teams review and approve every decision. The platform names the specific missing concept in each student's work—such as discounting a future salvage value—and ties feedback to course references. It maintains a shared, auditable grading standard across the entire teaching team.
- Education Technology
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Traditional grading often focuses on checking answers rather than identifying the specific concepts a student has missed, leaving students without actionable feedback on what to improve. Manual grading is also time-consuming and inconsistent across teaching teams, especially when multiple graders apply different standards to equivalent work.
Solution
Treemarks prepares rubric-based conceptual feedback and proposed grades from a course's own materials, allowing teaching staff to review, adjust, and release results. The platform names the missing concept in each student's work—for example, discounting a future salvage value—and ties feedback to course references so students know exactly what to study. Grading rules are visible and consistent across the team, with staff able to audit every decision. A class summary highlights where specific conceptual gaps recur, helping instructors decide what to revisit in teaching.
Target Audience
University and college course teams—particularly instructors, teaching assistants, and course coordinators—in STEM and quantitative fields such as engineering, physics, chemistry, mathematics, and economics who want efficient, consistent, and auditable grading with useful conceptual feedback.
Features
- AI-generated rubric criteria specific to each question, distinguishing missing concepts from arithmetic slips or transcription errors
- Conceptual feedback that names the gap and points to the relevant course reference for the student's next step
- Shared course standard with explicit criteria and reviewed decisions, keeping grading consistent even as new graders join
- Class-level pattern summaries showing where concepts are commonly missed across submissions
- De-identified submissions with recorded staff decisions, supporting privacy and full auditability
- Course staff review and approve every grade, accepting valid alternative solutions