Proseable

Enables real-time two-way conversation between learner and AI language model, simulating natural dialogue without human tutors. The system maintains conversation context across multiple turns, adapts difficulty based on learner responses, and generates contextually appropriate follow-up prompts to sustain engagement. Uses LLM-based turn-taking with conversation state management to track dialogue history and learner proficiency signals.

Analyzes learner text input for grammatical errors, syntax violations, and structural mistakes in the target language, providing immediate corrective feedback with explanations. The system identifies error type (tense, agreement, word order, etc.), highlights the problematic phrase, and explains the grammatical rule violated. Uses NLP-based error detection (likely dependency parsing or rule-based grammar checkers) combined with LLM-generated explanations to contextualize corrections within the learner's current dialogue.

Analyzes learner speech input to assess pronunciation accuracy, identify accent patterns, and provide corrective guidance on phoneme production. The system likely uses speech-to-text conversion to capture phonetic output, compares against target language phoneme inventory, and generates feedback on specific sounds requiring improvement. May employ acoustic feature analysis or phoneme-level error detection to pinpoint mispronunciations beyond simple transcription errors.

Dynamically adjusts conversation complexity, vocabulary level, and grammatical structures based on real-time assessment of learner performance during dialogue. The system monitors response accuracy, response latency, vocabulary recognition, and grammar correctness to infer proficiency level, then modulates AI tutor prompts to maintain optimal challenge level (zone of proximal development). Uses learner signal classification (error rate, response time, vocabulary coverage) to trigger difficulty adjustments without explicit learner input.

Identifies unfamiliar vocabulary in AI tutor responses and learner input, provides on-demand definitions with contextual usage examples, and tracks vocabulary exposure across dialogue sessions. The system integrates vocabulary lookup (dictionary API or embedded lexicon) with dialogue context to provide definitions that match the specific usage in conversation. May track vocabulary frequency and learner exposure to identify high-value vocabulary for focused study.

Evaluates learner language proficiency across multiple dimensions (speaking, writing, listening comprehension, grammar, vocabulary) through dialogue interaction and generates proficiency level assessment aligned to CEFR or equivalent framework. The system aggregates performance signals from multiple dialogue exchanges (error rates, vocabulary coverage, grammatical complexity, response latency) to infer overall proficiency and skill-specific strengths/weaknesses. May use rule-based scoring or ML-based proficiency classification.

Enables learners to select target language and optionally native language for instruction, supporting multiple language pairs with language-specific NLP pipelines (grammar rules, pronunciation phoneme inventories, vocabulary lists). The system routes learner input to language-specific processors for grammar checking, pronunciation analysis, and vocabulary lookup. Supports both major languages (Spanish, French, German, Mandarin) and potentially less common language pairs depending on available NLP tooling.

Provides free access to core conversational practice features without subscription paywall, removing financial barriers to language learning. The free tier includes unlimited dialogue sessions, real-time feedback, and proficiency assessment without usage limits or time restrictions. Monetization likely relies on optional premium features (advanced analytics, structured curriculum, human tutor integration) rather than restricting core practice access.

Maintains conversation context within individual dialogue sessions, enabling the AI tutor to reference previous exchanges, track topic continuity, and provide coherent multi-turn responses. The system stores conversation history (learner messages, AI responses, feedback) within session scope and uses this history as context for subsequent LLM prompts, enabling natural dialogue flow. Context retention is session-scoped; conversations reset between sessions without persistent cross-session memory.