AI can write an ELT lesson in seconds. But its quality is wildly variable, and you're entirely in the hands of whatever training data your AI calls on. And the chances of hallucination are high.
ELT Brain equips your AI with the knowledge and tools it needs to reliably design tasks, vocab sets, test items and lessons that hold up to scrutiny, and to edit the way a senior commissioning editor would – consistently, with SLA-cited reasoning behind every decision.
AI can now write an ELT lesson, a reading task, or a vocabulary set in seconds – and that's exactly the problem. It produces content that looks good at first glance, but falls apart on closer inspection: a "reading for gist" task that actually tests specific dates, a B1 text seeded with unglossed C1 collocations, a vocabulary set with no frequency rationale and no recycling plan, a dialogue that forces the present perfect where no fluent speaker would use it.
Catching it is the time-consuming work that an ELT pro does – work that's hard to scale and expensive in both time and money.
There's more material to check than ever, and the same number of hours to check it in. So flaws creep through: into production, where they cost a re-edit or a reprint, or into the classroom, where they result in a lesson that doesn't work with your group.
ELT Brain applies a senior editorial eye automatically – at the brief, at the writing desk, in the lesson prep, and at the editorial pass – with cited reasoning from learning science and SLA research at every step, so your time can be focused on the judgement only a human can apply.
We asked an AI to draft a B1 reading task and a vocabulary set for the same source text, with and without ELT Brain.
Connect ELT Brain to the AI you already use: Claude, ChatGPT, Codex, Cursor, or anything that supports MCP connectors and Skills.
Once you have ELT Brain connected, your ELT-related prompts are routed through the team of six specialists: the Learning Scientist, the Brief & Context elicitor, the Curriculum Architect, the Instructional Writer, the Course Doctor, and the Delivery Coach.
Tells you what the SLA research actually says, with cited sources and evidence strength. Settles design arguments like explicit vs implicit grammar, PPP vs TBLT, coursebook vs emergent curriculum without fence-sitting. Flags popular ELT myths the moment they show up.
Pushes you to provide the inputs ELT design actually needs: CEFR level, L1, target skill focus, instructional setting (EFL / ESL / EAP / ESP / business / young learners / exam prep), exam target if any. Pushes back on briefs that don't add up. Pulls relevant case studies when a principle needs concrete grounding.
Produces single-lesson skeletons and language syllabi with coherent progression. Lesson stages have timed durations, predicted comprehension and production demand, and a feedback slot designed in. Syllabi are tied to communicative performance goals, CEFR/ACTFL level, recycling, assessment evidence, and teacher workload. Refuses content-shaped goals ("cover present perfect") and aspirational ones ("become more fluent").
Writes the materials that actually go on a page. Vocabulary sets with selection rationale, full per-item depth, and a spaced-recycling plan. Reading and listening tasks where the item-type matches the construct. Graded grammar practice that moves controlled → contextualised → freer, with the expected error profile attached. Speaking tasks that have a real outcome and a real gap. Also writes language clarifications, performance evaluations, corrective feedback, and supported Cambridge English, Oxford Test of English, and IELTS exam items.
Reads a reading text, listening script, dialogue model, exercise, full coursebook page, or supported exam item and runs the audit a senior commissioning editor or assessment specialist would: naturalness, CEFR level coherence, construct validity, hidden cultural and contextual assumptions, inclusion, teach-vs-test mismatch, pedagogical coherence, and exam-spec compliance. Returns flags with location, severity, cited reasoning, and concrete suggested revisions.
Designs synchronous speaking sessions, classroom activity flow, and learner-affect support. It matches interaction patterns to class size and mode, plans setup and transitions, names monitoring focus per stage, and supports anxiety / willingness-to-communicate barriers without lowering the language demand into mere comfort.
Behind the team sits a curated knowledge base: 333 research notes drawn from the SLA and materials-design literature, plus the cross-cutting learning-science research. Every tool cites its sources.
Draft a B1 reading lesson with matched taskLesson skeleton, vocabulary set, and reading task in one flow – all level-coherent, all based on cited SLA research.
Audit a freelancer's draft before sign-offThe red-pen pass a senior editor does by hand, applied consistently in seconds.
Adapt a coursebook lesson for your groupTomorrow's page customised to mixed L1s, a specific exam target, or a level that's slightly off – the night before, not the night after.
Defend a design choice with cited evidenceWhen an editor or trainer pushes back, name the research finding behind your decision.
Design a speaking session for a real class sizeLesson plan with interaction patterns matched to mode, CCQs, monitoring focus, and a feedback slot designed in.
Write a listening task that respects connected speechBottom-up priming named explicitly. Item-types matched to listen-pass. No A2 + natural-fast.
Build a CEFR-aligned speaking rating descriptorFor formative or summative use, anchored to intelligibility rather than vague impressions.
Train new editors against a consistent rubricThe same flags the senior editor would catch, every time, with the citations behind them.
You're producing material to a publisher's brief, often under time pressure. The lesson skeletons, vocabulary sets, reading and listening tasks all run a self-check against an SLA-informed rubric before they come back to you. Things that would get picked up by an editor or reviewer (mis-pitched lexical load, aspirational goals, no recycling plan, construct-invalid items) get caught before you send anything.
And every output cites the research behind its decisions, so if your editor asks "why?" you have an answer that isn't "this felt right."
You're reviewing freelancer drafts at scale, and consistency is hard. One editor can only read so many pages a week. The audit tool runs the editorial pass you'd do manually, picking up issues and improvements, with location, severity, cited reasoning, and a concrete suggested rewrite.
The result: every page gets a consistent editorial pass; editors can then apply their judgment to the things ELT Brain has highlighted. The time required per editorial pass is dramatically reduced.
CELTA, DELTA, CertTESOL trainees tend to produce intuition-led lesson plans. Every ELT Brain output carries an explicit research trail, so trainees can read the cited findings and trace the reasoning behind each design choice.
Useful in supervision too: when a trainee writes a content-shaped goal, the tool refuses it and explains why, before you have to.
You teach a specific group with specific needs – maybe mixed L1s, a particular exam target, an awkward textbook page that doesn't quite fit, or an evening lesson with twelve learners at three different levels. Every week you adapt material, write extra tasks, or design supplementary work, often the night before. ELT Brain is a huge time-saver.
Hand it tomorrow's coursebook page plus your group's profile and ask for a customised version. Ask it to write a warmer and three extra tasks at the right level. Ask it to audit what you're about to teach and flag what won't land with this group. You have the expertise of a senior trainer available whenever you need it.
Every design decision ELT Brain makes can be traced to a specific research finding. The knowledge base covers 15 ELT-specific domains plus universal learning-science research, and every finding carries an evidence-strength rating: moderate evidence is never laundered as strong. When the research is silent on a topic, the tools say so rather than fall back to general AI guesswork (which is what your AI tool is likely to do without ELT Brain).
AI models are trained to be agreeable. They soften criticism, pass material that should fail, and tell you what you want to hear. ELT Brain pushes back on that in three ways.
Every citation comes from the ELT/SLA knowledge base, so you won't get fabricated references. Popular ELT myths ("younger always means faster," "native speakers are always better teachers," learning styles, the critical-period as a hard cliff) are flagged the moment they appear in a request.
Content-focused lesson goals ("cover present perfect"). Aspirational ones ("become more fluent"). A vocabulary set of 30 items for a single lesson. A "reading for gist" task with 8 gist items. A natural-fast listening task for A2 learners. Each refusal explains why and tells you what you should do instead.
ELT Brain checks its own work before presenting anything back to you. Fifteen ELT-specific rubrics tell the tools what the work must answer to before it comes back. Vague goals are sharpened before you see them. Mis-pitched vocabulary gets caught. Construct-invalid items get rewritten. Audits with no cited reasoning per flag are rejected.
ELT Brain sits on two layers of research. The foundation is general learning science – how people learn anything: cognitive load, retrieval, transfer, multimedia, assessment, instructional design, learner psychology. On top sits a deep ELT layer: SLA foundations (Krashen, Long, Swain, Schmidt, DeKeyser, Lantolf, Larsen-Freeman, N. Ellis), the four skills, language testing (Bachman-Palmer validity, ALTE standards, fairness and bias), the CEFR Companion Volume 2020 first-class additions plus ACTFL Proficiency Guidelines and the OPI, vocabulary acquisition (including lexical bundles), grammar instruction, discourse and pragmatics, sociolinguistic context (World Englishes, ELF, Schneider's Dynamic Model, translanguaging, native-speakerism, critical applied linguistics, MTB-MLE), specific contexts (CLIL, EMI, EAL, ESOL, young learners with story-based teaching, exam prep, business, EAP including BALEAP frameworks, ESP, BICS/CALP and Cummins), methodology and materials (including TPR), inclusion and accessibility (dyslexia, neurodiversity, refugee/migrant), and the publisher-side disciplines (scope-and-sequence, materials development, image and representation).
The foundation is publisher-neutral. It draws on Oxford 3000/5000, Cambridge English Profile, NGSL, AWL, ALTE standards, CEFR descriptors, and the wider applied-linguistics literature – without privileging any single publisher's output.
ELT Brain is a service your AI talks to when you ask it for ELT design or audit help. It supplies the SLA and materials-design expertise that general-purpose AI models don't have on their own. You still use your AI the way you usually do – ELT Brain just makes what comes back structurally sound, level-coherent, and defensible against the rubric a senior commissioning editor would apply.
It produces design artefacts: lesson skeletons, vocabulary sets, reading and listening tasks, grammar practice, speaking tasks, audit reports. It doesn't produce finished coursebook PDFs or video content – it makes the design sound so the production work doesn't get wasted.
No. You just need to be able to install a connector and a Skill in your AI tool – don't worry if you don't know what that means: simple step-by-step instructions are provided, and it takes 1-2 minutes.
ELT Brain works best with Claude or OpenAI's Codex. You'll need a paid plan. It also works with any other AI that supports MCP connectors and custom Skills. You can use it with ChatGPT, but if you have a paid ChatGPT plan, you have access to Codex which supports ELT Brain much better.
It works with the AI you already use. ELT Brain supplies the expertise; your AI supplies the writing. The simplest way to think of it is that ELT Brain supplements your own prompts automatically, providing your AI with ELT expertise in order to guide its responses.
ELT Brain is based on 333 hand-curated research notes, each written as a complete argument with primary citations and an evidence-strength rating:
It speeds up the editorial process, provides consistency and brings SLA rigour at every stage. It runs the consistent rubric pass that senior editors do by hand, in seconds, across every page. But it doesn't remove the judgment work editors do – what to push back on, negotiating revisions with freelancers, calibrating across the unit and the series, and making the calls only humans can make.
The realistic outcome: every page gets the audit pass; editors apply human judgment to what the audit surfaces; freelancers get faster, more consistent feedback; the team scales without quality drift.
This is common at publishers and large institutions. Three paths usually still work:
1. Claude Code or the Code tab inside Claude Desktop – separate policy surface from Claude chat; plugin install is usually allowed.
2. Codex if you have a paid ChatGPT account – separate OpenAI app with its own settings.
3. Claude Desktop via local config – edits a local file on your machine and bypasses the "Custom connectors" setting entirely.
See the set-up guide for all three.
The knowledge base covers SLA fundamentals plus context-specific notes for CLIL, EMI, EAL, ESOL, business English, BELF, EAP, ESP, young learners, teens, exam preparation, and vocational. The tools adapt to the CEFR level, L1, and instructional setting you specify.
If you work in a context that isn't covered – or is covered only partially – the tools say so rather than bluffing. We might be able to add it to the knowledge base. Get in touch: info@learningbrain.ai.
Good – that's a conversation worth having. The pushback is always grounded in a specific research finding you can evaluate against your contextual knowledge. Sometimes the research wins. Sometimes your practitioner judgment wins. Either way, the decision is now explicit rather than assumed.
No. The tools handle the rubric pass: level coherence, construct validity, lexical coverage, naturalness, inclusion. You handle the context: knowing your audience, calibrating your brand voice, negotiating with a freelancer, deciding what matters most for this series. No tool can guarantee a coursebook page will work in the classroom. It can guarantee the design is structurally defensible. What happens in delivery is still up to humans.
When you ask ChatGPT a question about ELT or SLA, it draws on its general training data – which includes outdated findings, popular myths (younger-always-better, native-speakerism as a default), and confident claims without evidence ratings. ELT Brain draws on a curated, evidence-rated knowledge base and validated rubrics. It also refuses to fall back to general AI knowledge when the substrate is silent on something. Honestly, if you try the same prompts in Claude or Codex with ELT Brain vs. the same prompts in vanilla ChatGPT you'll immediately see the difference.
Enter your email and you'll get access immediately. Connect to your AI tool and start designing or auditing in three minutes.