Check2Learn
Visit Check2LearnCheck2Learn is a production-grade AI-powered learning platform that puts instant, personalised feedback at the centre of the classroom. It connects teachers, students, and AI into a single workflow — from task creation and answer submission to automated evaluation, performance tracking, and adaptive learning.
What is Check2Learn?
Check2Learn is a full-stack AI-powered education platform built for the modern classroom. Teachers create tasks, students submit answers in any format — typed, document, or handwritten — and AI evaluates responses instantly, identifies learning gaps, and re-presents content in alternative formats to reinforce understanding. The platform covers the complete classroom workflow: task and session management, real-time feedback, multi-format answer submission, performance history, intelligent content organisation, and in-platform messaging. Built with LangChain and LangGraph at its core, the AI layer is not a chatbot bolted onto a standard LMS — it is the evaluation engine that drives every student interaction. PieStack built the platform end-to-end, from system architecture through frontend, backend, AI workflow implementation, and cloud deployment.
Services Provided
Objectives
- ◇Replace manual grading with an AI evaluation engine that returns instant, criteria-based feedback the moment a student submits an answer.
- ◇Support every answer format — typed responses, uploaded documents, and photos of handwritten work.
- ◇Give teachers a complete session and task management system that works with materials they already have, including Excel and PDF files created outside the platform.
Approach
The core design decision was to build AI into the evaluation loop rather than treating it as a supplementary feature. LangChain handles the prompt orchestration and tool calls that power instant answer evaluation. LangGraph manages the agentic workflow — the multi-step reasoning chain that determines whether a student's response meets the task criteria, what the specific gaps are, and how content should be re-presented to address them. This is not a wrapper around a generic AI chat interface. It is a purpose-built evaluation engine. The teacher-side and student-side interfaces were designed independently so that each role sees only what is relevant to them. Teachers manage sessions, tasks, class organisation, and student performance from one interface. Students work through tasks and receive feedback from another. The two surfaces share the same underlying data and AI layer but are architecturally separate. Multi-format answer submission — typed, document, and handwritten photo — required a dedicated ingestion and normalisation pipeline so that the AI evaluation layer could process every format consistently regardless of how the answer was submitted.
The Problem: Grading Was Blocking Learning
In a traditional classroom, the feedback loop is broken by time. A student submits an essay. The teacher takes days to return it. By the time the student sees the feedback, the lesson has moved on and the gap has widened. Scale that across 30 students and multiple subjects and it becomes a structural problem, not a workload problem.
- ◇Manual grading of essays and free-text answers took days per class — feedback arrived too late to influence the next lesson.
- ◇Teachers had no reliable view of which students were struggling with which concepts until test results arrived, by which point the gap had already grown.
- ◇Students had no mechanism for knowing whether their understanding was accurate between submissions.
- ◇Existing platforms either scored only multiple-choice questions or required teachers to configure complex rubrics before AI evaluation could work.
- ◇Teachers could not bring their existing materials — Excel question banks, PDF task sheets — into a new system without manual re-entry.
AI Evaluation Engine
The core of Check2Learn is an agentic AI evaluation pipeline built on LangChain and LangGraph. When a student submits an answer — in any format — the pipeline evaluates the response against the task criteria, identifies specific gaps in understanding, generates instant feedback, and determines whether supplementary content should be re-presented in an alternative format to reinforce the concept.
- ◇LangChain orchestrates the prompt pipeline and tool calls that drive evaluation — no hardcoded scoring rules, no simple keyword matching.
- ◇LangGraph manages the multi-step agentic workflow: evaluate, identify gaps, decide on re-presentation, generate feedback, update performance record.
- ◇Feedback is returned the moment the student submits — not after a teacher review queue.
- ◇The AI layer handles essays, free-text answers, and handwritten submissions through the same evaluation pipeline regardless of input format.
- ◇Every evaluation feeds the student's performance history, creating a continuously updated picture of understanding across topics and tasks.
Multi-Format Answer Submission
Students learn differently. Some type. Some prefer to write by hand. Some work from scanned documents. Check2Learn accepts all of them through the same submission interface and routes every format through the same AI evaluation pipeline.
- ◇Typed responses submitted directly in the platform.
- ◇Document uploads — Word, PDF, and common file formats — parsed and evaluated without manual extraction.
- ◇Handwritten answer photos — students photograph their written work and upload the image; the system processes and evaluates it through OCR and the AI pipeline.
- ◇Teacher-side handwriting upload: teachers can upload a photo of a handwritten answer on behalf of a specific student, selecting session, student, and task type (essay or free-text) before submission.
- ◇Every submission format produces the same output: instant AI feedback and an updated performance record — no format creates a second-class experience.
Task Creation & Session Management
Teachers manage their classes through a session and task interface designed to handle the full scope of classroom organisation — without requiring them to rebuild their existing materials from scratch.
- ◇Create and manage classes, sessions, and tasks from a single workspace.
- ◇Upload existing task documents — Excel question banks and PDF task sheets created outside the platform — directly into Check2Learn without manual re-entry.
- ◇Assign tasks to individual students or entire classes with session-level control.
- ◇Configure task type (essay, free-text, multiple choice) and evaluation parameters per task.
- ◇Archive completed classes, sessions, and tasks into searchable categories for reference and reuse.
Performance History
Both teachers and students have access to a longitudinal view of performance — not just a score on a single task but a running record of understanding across sessions, topics, and time.
- ◇Student view: personal performance history across all tasks and sessions, showing progress over time and highlighting areas where understanding has improved or stalled.
- ◇Teacher view: class-level and individual student performance history, enabling early identification of students who are falling behind on specific concepts.
- ◇Performance data is updated in real time after every AI evaluation — no manual data entry or delayed reporting.
- ◇History is filterable by session, task type, date range, and student, giving teachers the precision to act on specific gaps rather than general impressions.
- ◇Performance records persist across archived sessions so teachers retain a complete history even after a class formally ends.
Intelligent Content Organisation & Archiving
As classes, sessions, and tasks accumulate over time, the platform organises and archives them into a searchable, filterable structure so teachers can find, reuse, and reference materials without digging through manually managed folders.
- ◇Classes and tasks can be archived into named categories defined by the teacher.
- ◇Search and filter across archived and active content by category, date, task type, and student.
- ◇Archived sessions remain fully accessible — performance history, submitted answers, and AI feedback are all retained.
- ◇Repeat sessions can reuse existing tasks without duplication.
- ◇Content organisation is managed from within the platform — no external file management required.
In-Platform Messaging
Teachers and students communicate inside the platform rather than across external email or messaging tools, keeping all communication tied to the sessions and tasks it relates to.
- ◇Direct messaging between teachers and students within the platform.
- ◇Read and unread message status so teachers can track which communications have been seen.
- ◇Chat archiving for reference and record-keeping.
- ◇Messages are contextually linked to sessions and tasks — conversations stay organised alongside the work they relate to.
- ◇No external communication tool required — the full interaction between teacher and student happens within one system.
Results
Feedback turnaround
Answer formats supported
Teacher workload
Conclusion
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