DidactLabs provides a browser‑based writing platform that records the full authoring workflow, separating student‑typed text, AI‑generated suggestions, and pasted sources on an interactive timeline. The system lets educators configure assignment‑level AI parameters, view usage analytics, and export reports for LMS integration while maintaining GDPR‑compliant data minimization.
Funding
Funding not disclosed
Founders
Product
Problem
Educators lack visibility into how students incorporate generative AI into their writing, making it difficult to assess academic integrity and to guide responsible AI use. Existing tools either merely flag AI‑generated text—often unreliably—or provide unrestricted AI assistance without oversight, leaving teachers guessing about the learning process.
Solution
DidactLabs offers a browser‑based platform that captures the complete writing workflow, distinguishing student‑typed content, AI‑generated suggestions, and pasted web material on an interactive timeline. Teachers can review each step, understand the purpose of AI usage (e.g., brainstorming, language refinement), and provide targeted feedback within the same environment. The AI assistant is configurable per assignment, allowing educators to set knowledge domains, tone, and permissible functions to align with lesson objectives. Students work without creating accounts; a simple shareable link grants immediate access while the platform enforces GDPR‑compliant data minimization and never reuses content for model training. All interaction data are stored securely in the EU and presented to instructors through a dashboard that supports analytics, export, and integration with existing learning management systems.
Target Audience
Primary customers are K‑12 and higher‑education educators, as well as academic institutions that need to monitor and guide AI‑augmented writing while preserving academic integrity.
Features
- Interactive timeline that logs and visually separates student input, AI output, and external paste actions for full process transparency
- Usage analytics that classify AI interactions by intent (brainstorming, drafting, editing) to inform instructional coaching
- Assignment‑level AI configuration (knowledge base, tone, functional scope) enabling lesson‑specific control of the assistant
- Real‑time, in‑context feedback to students, fostering critical evaluation of AI suggestions without leaving the writing interface
- Link‑based assignment delivery with zero‑login onboarding; students can start writing instantly in any modern browser
- Privacy‑by‑design architecture: minimal data collection, GDPR compliance, and a strict policy that prohibits any use of student data for model training
- Model‑agnostic integration with leading LLM providers (e.g., Google) allowing institutions to select compliant AI back‑ends
- Teacher dashboard with exportable reports and optional LMS API hooks for seamless workflow integration