Language Learning App Development

Vocabulary and phrase scheduling using the SM-2 algorithm, which calculates the optimal inter-repetition interval for each card based on the learner's historical performance. Items answered correctly with high confidence are pushed further into the future; items answered with difficulty come back sooner. This compounding effect means each learner's review queue is shaped by what they specifically struggle with, not a generic curriculum schedule.

Content is stored as JSON card objects with a target word or phrase, native-language translation, example sentence in context, audio pronunciation file recorded by a native speaker, and an optional image for visual association. Exercise types cycle through cloze deletion (fill the gap), multiple-choice translation, and active recall translation prompts within a single session to vary cognitive demand. Decks are organised by topic domain (travel, business, daily life, academic) and CEFR level (A1 through C2) so learners have a clear sense of where they are in the language and what vocabulary they are expected to know at each level. A placement test at onboarding assigns the initial CEFR level and sets the starting deck -- so a learner with B1 Spanish does not repeat vocabulary they already know. Performance data from every session feeds back into the scheduling algorithm, tightening or loosening review intervals in real time as accuracy improves or declines. Learning records are persisted to an xAPI-compatible LRS so the data is yours to analyse and report on regardless of the front-end platform.

Speech recognition integration using Azure Cognitive Services Speech SDK or Google Cloud Speech-to-Text API, both of which support phoneme-level output rather than just word-level transcription. The API returns a phoneme alignment map for the learner's utterance, which the backend compares against the reference phoneme sequence for the target phrase. The resulting per-phoneme accuracy score (typically expressed as a 0-100 confidence value per sound) is what drives the visual feedback -- a phoneme grid or waveform overlay that highlights exactly which sounds the learner mispronounced, rather than a binary pass or fail.

Record and playback comparison plays the learner's recording alongside a native speaker reference audio file so they can hear the difference directly. This side-by-side comparison is more effective for self-correction than a score alone. Progressive pronunciation exercises start with isolated phonemes and vowel sounds, then move to syllables, words, and full sentences as the learner's accuracy improves -- matching the same graduated structure used in formal pronunciation coaching. For languages where Azure Cognitive Services provides stronger phoneme coverage (Mandarin, Arabic, Japanese), we use Azure; for languages where Google Cloud Speech-to-Text gives better results, we use that. Azure's pronunciation assessment API covers word error rate, fluency score, completeness score, and pronunciation score as separate metrics, giving the frontend enough signal to offer specific coaching rather than a single composite number. All audio data for EU learners is handled in accordance with GDPR, with explicit consent collected before recording starts.

LLM-based conversation partner built on GPT-4o via the OpenAI API, with a system prompt persona that sets the AI's language level, topic domain, response length, and correction style. A tutor persona for beginners uses simple vocabulary and short sentences; a discussion partner persona for advanced learners engages naturally and only corrects when errors are frequent or communicatively significant. The system prompt is configurable per scenario so your content team can define as many conversation contexts as your curriculum requires, without additional engineering for each one.

Grammar correction is contextual: the AI identifies the error, explains what rule was broken in plain language, and shows the corrected version in sentence context -- not just underlined red with no explanation. Vocabulary suggestion appears when the learner uses a circumlocution or an approximate word, offering the more precise target-language term with a brief meaning note. Topic-based conversation scenarios -- airport check-in, job interview, hotel complaint, dinner reservation, medical appointment -- are pre-loaded with scenario context so the AI knows the register and domain vocabulary to model. Difficulty and response speed are adjustable by the learner: beginners get shorter sentences and a brief wait for the AI response to simulate a patient interlocutor; advanced learners get native-speed natural responses that require active comprehension. Session transcripts are saved to the learner profile and optionally fed back into the spaced repetition deck as new vocabulary items encountered in context. All conversation data for EU-based learners is stored and processed in GDPR-compliant infrastructure with appropriate data retention controls.

Lesson content structured according to CEFR level bands (A1, A2, B1, B2, C1, C2) with a placement test at onboarding that assigns the learner to the correct entry point. Grammar concepts are sequenced so each lesson builds on material from the previous one -- present tense before past tense, simple vocabulary before idiomatic expressions -- and a prerequisite check prevents learners from accessing lessons that depend on foundations they have not yet completed.

Each lesson combines all four language skills within a single session: reading comprehension, listening exercises, writing prompts, and speaking tasks. Audio lesson content is playable at variable speed (0.75x, 1x, 1.25x, 1.5x) so learners adjust to natural speech gradually rather than being thrown in at full speed. Audio and video content is delivered with full transcripts for listening comprehension exercises, allowing learners to check meaning and replay specific segments. Lesson content is structured as SCORM 2004-compliant packages or custom JSON objects depending on your distribution requirements -- SCORM for integration with third-party LMS platforms, custom JSON for maximum flexibility in the native app. Grammar explanations use worked examples drawn from the lesson's topic context rather than abstract rule statements. Offline lesson download allows learners to download a lesson module to their device for practice on planes, commutes, or anywhere without reliable data. Downloaded content is protected with application-level DRM to prevent redistribution. Learning events (lesson start, lesson complete, exercise answer, score) are logged to an xAPI-compatible LRS (Learning Record Store), giving you a complete learner activity trail for analysis, certification, and compliance reporting.

Daily streak tracking counts consecutive days on which the learner completes a qualifying practice session -- a minimum activity threshold rather than simply opening the app. Streak freeze mechanics allow learners to protect an active streak for a defined number of missed days, configurable per subscription tier so premium learners get more protection. One missed day without a freeze ends the streak; the streak count resets to zero, which is a powerful psychological motivator but also a churn driver if not managed with a recovery mechanic.

XP points are awarded on lesson completion, exercise accuracy, and daily goal achievement. Level progression unlocks new content tiers and cosmetic rewards (avatar items, profile badges). XP weighting is calibrated so harder exercises and higher-difficulty lessons award more points -- grinding easy A1 reviews does not substitute for real forward progress. Leaderboards are scoped to friend groups (invite-based) and a weekly global league so new learners are not permanently outranked by early users; weekly resets create regular competition cycles and re-engagement moments for lapsed learners who rejoin. Achievement badges for milestone moments: first 100 words learned, 7-day streak, 30-day streak, first AI conversation completed, first tutor session booked, first CEFR level completed. Push notification reminders sent via APNs and FCM at the learner's preferred daily practice time, with smart cadence that reduces frequency for learners who consistently practice without prompting and increases frequency for learners whose engagement is dropping. Streak recovery offers (a one-time streak restore at the moment of loss) reduce churn from the most common reason learners disengage -- a single missed day breaking a long-running habit.

Tutor marketplace with profiles showing the languages taught, teaching specialisms (business, exam prep, conversation, pronunciation), price per session, learner level range, and verified reviews. Filtering by language, specialism, CEFR level, and availability so learners find the right tutor without scrolling a generic list. Calendar-based booking with full timezone detection and display -- learner and tutor see their own local time throughout the booking and reminder flow so a Tokyo learner booking a Madrid tutor at 9am Madrid time sees 5pm Tokyo time in every confirmation and reminder they receive.

Video session delivery via WebRTC in a custom session interface, or via a third-party provider such as Daily.co or Whereby for managed video infrastructure. Session tools include shared whiteboard, vocabulary card display, and document upload so the session has a structured workspace rather than a bare video call. Session notes written by the tutor are saved to the learner's profile after the session ends. New vocabulary items introduced during the session are added to the learner's spaced repetition deck by the tutor or automatically extracted from the session transcript, so live tutor input feeds directly into the self-study workflow. Rating and review submission after each session, with verified-learner reviews only so ratings reflect genuine session experience. Payout to tutors handled via Stripe Connect with configurable platform commission. Community peer matching for language exchange -- learners at the same level in opposite directions (A2 French speaker, B2 English speaker) can be connected for free conversation swap sessions, reducing the cost of speaking practice without replacing paid tutoring.