Classmates currently active.
Choose a topic shell, then open its page or deep-study scaffold.
Use scenario-based questions to practise longer written responses and applied technical reasoning.
Choose a topic and flip through quick-recall cards. Empty decks still keep the shell layout intact.
Choose a topic to be quizzed on. You need 80% to pass. Wrong answers will be explained after.
Rate each topic Red / Amber / Green to track your confidence. This affects flashcard and quiz priority.
Every technical term you need for the T Level. Search by name or filter by first letter. Tags show which topic area each term belongs to.
These files are shared so students, teachers, and independent learners can inspect the course content, create their own revision notes, or upload the source material into AI study tools such as NotebookLM, ChatGPT, Claude, LM Studio, or local LLM workflows.
Why this exists
JoGPT was made because the Pearson T Level Digital Support and Security core content is large, scattered, and hard to revise from in one place. The aim is to turn the course into structured, inspectable learning data that can power revision pages, flashcards, quizzes, practice questions, AI notebooks, and teacher resources.
The content was built across multiple stages: source collection, cleaning, topic mapping, Stage 4 consolidation, Stage 6 correction, diagram planning, shell integration, and repeated bug/audit passes. Local LLMs helped with private analysis and transformation work, Claude Code helped with larger code/content passes, Codex helped with shell implementation and guardrails, and the final direction came from my own checks, corrections, and design decisions.
Core source files
The clean Stage 6 master file for all 42 course sections. Best for NotebookLM or any AI tool because it has structured headings, tables, worked examples, exam angles, and no Mermaid code blocks to confuse simple parsers.
The authoring version of the master file. It includes the same course content plus Mermaid diagram blocks for flowcharts, DFDs, project boards, network models, security flows, and more.
A human-friendly PDF version of the master content. This is the easiest file to read offline, print, annotate, or send to someone who does not want to work directly with Markdown.
A generic preparation guide for employer-set-project tasks. It explains planning, diagnostics, needs analysis, solution development, Packet Tracer testing, and reflective evaluation.
AI and rebuilding context
Explains what Stage 6 is, why it replaced Stage 4, what was corrected, and which file should be used for each purpose.
Download READMEA briefing file for AI tools. Paste it into a new chat or notebook to give the model context about the project, source authority, known corrections, and pipeline.
Download promptThe structure used to standardise sections. Useful if you want to create new notes in the same style or ask AI to rewrite a weak section consistently.
Download templateShows how section depth was balanced. Useful when deciding which topics need heavier notes, more examples, or more practice tasks.
Download weightingMaps where diagrams are mandated, strongly recommended, or optional. Useful for making visual notes, teacher slides, or a diagram-first revision pack.
Download diagram mapJoGPT app datasets
254 Stage 6-aligned cards. Useful for importing into flashcard apps or asking AI to turn them into Anki/CSV format.
Download cards80 multiple-choice questions, 10 per topic. Useful for revision games, formative checks, or AI-assisted quiz expansion.
Download quizzes30 daily prompts across the course. Useful for warmups, retrieval practice, or short classroom starter tasks.
Download QOTDExam-style scenario questions with mark schemes. Useful for self-marking, teacher modelling, or AI feedback practice.
Download practiceA merged Pearson-focused glossary. Useful for keyword revision, definition checks, or creating terminology activities.
Download glossaryComing soon.
📁 Employer Set Project
Your graded employer-set project shell. Use this section to understand the task flow, walk through the expectations, and feel confident going into any ESP scenario.
The Employer Set Project is the applied part of the qualification. It checks whether you can use digital support knowledge in a realistic employer scenario: plan work, diagnose issues, justify choices, build or improve an artefact, and reflect on quality afterwards across whichever case-study context is set.
The tasks are separate, but they reward a joined-up way of thinking. Good planning feeds good diagnosis. Good diagnosis feeds a better needs analysis. A better needs analysis leads to a stronger build and a more convincing evaluation.
Open or download the study guide, SpringLeaf mocks, TeamTada mocks, and exam-style files from a folder-style list.
Account Workspace
Keep profile changes, progress shortcuts, and support actions in one place without leaving the shell.
Profile
Update your shell profile image and keep your account area tidy before imported data is connected.
Quick routes for common tasks.
Use the standalone sign-out page to leave this workspace.
Continue to sign out