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Illustrated portrait of Ibrahim

BARBRI GPT · AI study companion

BARBRI

Product Designer, design and front-end build · Vue 3 · TypeScript · 2025–26

A live question session. The question in the middle, the syllabus on the left, BARBRI GPT on the right.

Context

SQE1 is a multiple-choice exam of brutal breadth. Hundreds of topics across two papers, thousands of practice questions, and the same painful moment every time: you get one wrong, read a static explanation, and still don’t understand why your reasoning failed.

A new wave of AI-first prep tools in the UK proved students want something better than static question banks. BARBRI GPT is our answer: an AI study companion built around BARBRI’s question bank, where the tutor sits inside the practice session instead of in a separate chat tab. I designed it and built the front end.

Problem

You can’t just bolt a chatbot onto exam prep. Hallucinated law is worse than no help at all, and students preparing for the hardest exam of their lives don’t have spare trust to give. The real question was where AI belongs in a high-stakes study loop, and where it has to be fenced out.

That gave us hard rules. The AI must be grounded in the exact question on screen. Its answers must be labelled as AI-written and easy to check against the official answer. Students need a one-tap way to report a bad explanation. And someone human has to see those reports. Trust is a workflow, not a disclaimer.

Approach

I designed this one in code. Working in Vue 3 and TypeScript meant every screen was stress-tested by real state from day one: what happens when a session is refreshed halfway through, what the dashboard looks like with zero data, where focus goes when a modal closes. Empty states, loading states and error states were designed as first-class screens, not afterthoughts.

The AI layer went through the same discipline. Explanation formats, suggested prompts and the plain-English mode were all tested with authored legal content behind the same interface as the live model, so the tutor’s behaviour was designed and reviewed by people who know the law before a single generated answer reached a student.

Solution

The tutor sits beside the question, not over it. The session view holds three things in one glance: the syllabus tree, the SRA-style question, and BARBRI GPT. The panel opens with grounded prompts like “explain the tricky parts” and “what legislation applies?”, and it always carries the same notice: written with AI, check it against the official answer.

The dashboard. Weak-areas practice stays locked until there's real data to target.

Practice is built, not browsed.Students assemble drills by subject, length and difficulty, or sit timed mocks under FLK1 and FLK2 exam conditions. Weak-areas sessions unlock only once there’s enough answer history to be honest about what’s weak. The system never pretends to know you before it does.

The session builder: subjects, length, difficulty.
Timed mock exams under exam-realistic conditions.
Weak areas, ranked from real answer history. One click to drill them.

Everything feeds retention.Highlight any line in a question or explanation and it becomes a note or a flashcard. The flashcard engine runs spaced repetition tuned from SM-2 with a gentler first week, because new material shouldn’t punish. A planner works backwards from your exam date and turns the syllabus into a daily plan.

Flashcards on a tuned spaced-repetition engine.
The planner builds the runway to exam day.

AI with a supervisor.The admin side is the part I care most about. Alongside question-bank management and flagged-question triage, every “this explanation seems wrong” report from a student lands in a review queue in front of a human, with the full question context attached. The feedback loop closes.

Admin AI answer reports. Every questioned explanation gets human eyes.

Impact

BARBRI GPT changes how students relate to wrong answers. Instead of reading a static explanation and moving on, they interrogate the question until it makes sense, then pin what they learned as a flashcard. In testing, that loop is where the product earns its keep: the moment of failure becomes the moment of learning.

The trust architecture does its job too. The AI is usable becauseit’s checkable, and the report queue gives the legal team a live view of where explanations need work, which feeds directly back into content quality.

4

Assessment formats behind one submission pattern

30+

Screens designed and built across student and admin roles

100%

Of AI explanations reportable and human-reviewed

2

Roles served end to end: student and admin

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