Game category
Master English structures through noticing, transformation and error analysis instead of rote rules.
Tap the scrambled words into a perfect sentence. Shuffled words wait at the bottom of the screen; the learner taps them into order to build a sentence. Each word is voiced as it is tapped, and the whole sentence is read aloud once assembled — grammar and listening in one move.
Research basis: Combines the output hypothesis with the noticing hypothesis: assembling sentences forces attention to word order (Swain, 1985; Schmidt, 1990).
Every word has its drawer — find it. Words and phrases must be sorted into the right categories: countable or uncountable, formal or informal, past or present. Categorising forces the learner to think about why a form belongs where it does.
Research basis: Categorisation tasks build explicit pattern awareness; rooted in discourse and cohesion research (Halliday & Hasan, 1976).
One sentence, many shapes. The learner rewrites a given sentence following an instruction: make it negative, turn it into a question, shift it to passive voice, change the tense. The AI evaluates the open answer and explains what changed structurally.
Research basis: Based on construction grammar: mastering form-meaning pairings through manipulation (Goldberg, 2006).
Twelve English tenses, one clear head. A sentence with a missing verb: the learner chooses or types the correct tense form, guided by time markers and narrative context. Especially powerful for speakers of languages with only three tenses.
Research basis: Applies skill acquisition theory — from explicit rule to automatic use (DeKeyser, 1998); addresses the Hebrew three-tense to English twelve-tense gap.
If you play this game, your conditionals will improve. Given a conditional type (zero, first, second, third or mixed), the learner assembles or types the correct forms for both the if-clause and the main clause. Targets the classic if I will go error directly.
Research basis: Focus-on-form instruction: drawing attention to structure within meaningful context (Long, 1991).
Active or passive? Let the context decide. The learner rewrites active sentences in the passive voice and vice versa, with context hinting which voice fits. The AI checks the open answer and explains agent, patient and emphasis.
Research basis: Grounded in systemic functional linguistics: voice as a meaning-making choice, not a mechanical rule (Halliday).
Turn any statement into the right question. Given a statement or an answer, the learner forms the correct question: yes/no, wh-, tag or indirect. Trains the do-support and inversion that many first languages simply do not have.
Research basis: Follows processability theory: question formation develops in predictable stages (Pienemann, 1998).
A, an, the or nothing — decide fast. Text scrolls across the screen and the learner rapidly chooses a, an, the or no article for each highlighted gap. Speed turns conscious rules into reflexes.
Research basis: Targets article acquisition, a top difficulty for speakers of article-less or single-article languages (Master, 1990).
Give the sentence back its breath. A sentence appears stripped of all punctuation. The learner taps the slots between words and chooses a comma, full stop, question mark, dash — or nothing. Punctuation becomes audible, not arbitrary.
Research basis: Treats punctuation as visual prosody — the written trace of speech rhythm (Chafe, 1988).
The mistake is hiding in plain sight. A text contains one to three planted errors; the learner taps the exact word or phrase that is wrong. Finding errors in otherwise correct text is one of the strongest ways to sharpen grammatical intuition.
Research basis: Built on the noticing hypothesis and negative evidence research (Schmidt, 1990; White, 1991).
One precise shot at the wrong word. The learner scans a sentence and takes a precise shot at the single element that breaks it — often a spot where the first language quietly interferes. Fast rounds, sharp focus.
Research basis: Targets L1 transfer errors, where native-language patterns leak into English (Kellerman, 1983).
Find the ending that truly fits. The beginning of a sentence plus four possible endings; only one is both grammatically and logically right. Trains the learner to track structure and meaning to the very end of the sentence.
Research basis: A rational-cloze format with strong psychometric grounding (Taylor, 1953).
From beauty to beautiful to beautifully. A root word is given (beauty) and a sentence has a gap: the learner derives the form the sentence needs — beautiful, beautifully, beautify. One root unlocks a whole word family.
Research basis: Develops morphological awareness, a strong predictor of vocabulary growth (Kuo & Anderson, 2006).
Un-, dis-, -ness, -ment: small parts, big power. A word appears with a missing prefix or suffix; the learner chooses the right affix. Affix mastery multiplies vocabulary without memorising new words.
Research basis: Builds derivational morphological awareness (Carlisle, 2000) — especially valuable for speakers of root-pattern languages like Hebrew.
Investigate the language itself. The learner examines language clues — forms, structures, word choices — and reasons about why the language works the way it does. Less drill, more investigation.
Research basis: Develops metalinguistic awareness: the ability to reflect on language as an object (Bialystok, 2001).
Your mistakes have a pattern — decode it. Every error the learner makes is analysed and classified: spelling, sound, word form, word order or first-language transfer. Over time a personal error profile emerges, and practice is aimed exactly where it is needed.
Research basis: Based on Israeli diagnostic assessment research in EFL (Kahn-Horwitz & Goldstein, Oranim College).
Fast rounds that make grammar automatic. Quick-fire grammar decisions against the clock. The goal is not just accuracy but speed — turning rules the learner knows into reflexes they use.
Research basis: Built on automaticity research: timed practice converts declarative knowledge into procedural skill (DeKeyser, 2007).
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