{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_leya-ai","slug":"leya-ai","name":"Leya AI","type":"product","url":"https://leyaai.com","page_url":"https://unfragile.ai/leya-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_leya-ai__cap_0","uri":"capability://planning.reasoning.adaptive.difficulty.progression.engine","name":"adaptive-difficulty-progression-engine","description":"Dynamically adjusts lesson difficulty and content sequencing based on real-time performance metrics, learner engagement patterns, and knowledge gaps. The system likely uses item response theory (IRT) or similar psychometric models to estimate learner ability and select optimal next items, skipping already-mastered material and focusing on zone-of-proximal-development concepts. This contrasts with fixed curriculum paths by continuously recalibrating difficulty thresholds after each interaction.","intents":["I want lessons to automatically get harder only when I'm ready, not on a fixed schedule","I need the system to skip content I already know and focus on my weak areas","I want learning pacing that matches my personal speed, not a one-size-fits-all progression"],"best_for":["Adult professionals with limited study time who need efficient, non-linear learning paths","Learners with uneven skill distribution (e.g., strong reading, weak speaking)","Self-directed learners who want to avoid repetition and boredom"],"limitations":["Requires sufficient historical performance data to calibrate difficulty — early lessons may not be optimally sequenced","Cold-start problem: new users get generic difficulty progression until system learns their profile","Difficulty estimation algorithms can plateau or oscillate if learner performance is highly variable","No guarantee that skipped content won't create knowledge gaps in later, dependent topics"],"requires":["Minimum 10-20 completed lessons to establish reliable learner ability estimates","Consistent user engagement (at least 2-3 sessions per week) for algorithm stability","Backend analytics infrastructure to track fine-grained performance metrics"],"input_types":["user performance data (correctness, response time, confidence indicators)","lesson completion history","engagement metrics (session duration, retry patterns)"],"output_types":["next recommended lesson or exercise","difficulty level adjustment (numeric scale or categorical)","content skip recommendations"],"categories":["planning-reasoning","personalization-engine"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_leya-ai__cap_1","uri":"capability://text.generation.language.ai.driven.pronunciation.feedback.system","name":"ai-driven-pronunciation-feedback-system","description":"Analyzes learner speech input using automatic speech recognition (ASR) and phonetic analysis to detect pronunciation errors, then generates contextual corrective feedback with specific guidance on articulation, stress, or intonation. The system likely compares learner audio against reference pronunciations (native speaker models) using acoustic feature extraction and phoneme-level alignment, providing immediate, targeted corrections rather than generic 'try again' prompts.","intents":["I want instant feedback on my pronunciation with specific guidance on what I'm doing wrong","I need to know which phonemes or stress patterns I'm mispronouncing in real time","I want to practice speaking without waiting for a human tutor to review my audio"],"best_for":["Adult learners focused on spoken fluency and accent reduction","Professionals preparing for English-language interviews or presentations","Self-paced learners who lack access to live pronunciation tutors"],"limitations":["ASR accuracy varies significantly by accent, background noise, and microphone quality — non-native accents may be misrecognized","Phonetic analysis is language-pair specific; system trained on English may struggle with learners whose L1 has very different phoneme inventories","Cannot assess suprasegmental features (intonation, rhythm) as accurately as human listeners","Requires high-quality audio input; background noise or poor microphone quality degrades feedback accuracy","No real-time streaming ASR — likely requires full utterance recording before analysis, adding latency"],"requires":["Microphone access and browser/app audio recording permissions","Internet connection for ASR API calls (likely cloud-based)","Audio codec support (WAV, MP3, or similar)","Minimum 2-3 second utterance length for reliable phoneme detection"],"input_types":["audio file (WAV, MP3, or streaming audio)","target English phrase or word to be pronounced","learner's current proficiency level (for feedback calibration)"],"output_types":["phoneme-level error annotations","confidence scores for detected errors","corrective feedback text with specific articulation guidance","comparison visualization (learner vs. reference pronunciation waveforms or spectrograms)"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_leya-ai__cap_2","uri":"capability://text.generation.language.contextual.grammar.error.detection.and.correction","name":"contextual-grammar-error-detection-and-correction","description":"Analyzes learner-written or spoken English text to identify grammatical errors and provide contextual, rule-based corrections with explanations. The system likely uses dependency parsing, part-of-speech tagging, and grammar rule engines to detect errors (subject-verb agreement, tense consistency, article usage, etc.), then generates explanations that reference the specific grammar rule violated and provide corrected examples in the learner's current lesson context.","intents":["I want to know what grammar mistakes I'm making and why they're wrong, not just that they're wrong","I need corrections that explain the rule and show me how to fix similar mistakes in the future","I want grammar feedback integrated into my lesson flow, not as a separate tool"],"best_for":["Intermediate to advanced learners who benefit from explicit grammar rule explanations","Professionals writing emails or documents in English who need immediate feedback","Learners with strong metalinguistic awareness who want to understand grammar rules"],"limitations":["Context-dependent errors (e.g., article usage, preposition selection) are harder to detect than surface-level errors; false positive rates increase with ambiguous sentences","Requires accurate POS tagging and parsing, which degrades on non-standard English (slang, colloquialisms, learner errors that obscure syntactic structure)","Cannot distinguish between intentional stylistic choices and actual errors (e.g., sentence fragments for rhetorical effect)","Grammar rule coverage is finite; rare or complex constructions may not be detected","Explanations may be too technical for beginner learners or too simplistic for advanced learners"],"requires":["Natural language processing library with POS tagging and dependency parsing (e.g., spaCy, NLTK, or proprietary NLP pipeline)","Grammar rule database or rule engine (e.g., LanguageTool, custom rule set)","Learner proficiency level metadata to calibrate explanation complexity"],"input_types":["text string (sentence or paragraph)","learner proficiency level (A1-C2 CEFR or equivalent)","lesson context or topic (optional, for contextual explanations)"],"output_types":["error annotations with character offsets","error category (e.g., 'subject-verb agreement', 'article usage')","corrected text","rule explanation with examples","confidence score for error detection"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_leya-ai__cap_3","uri":"capability://search.retrieval.personalized.content.recommendation.engine","name":"personalized-content-recommendation-engine","description":"Recommends vocabulary, phrases, grammar topics, and practice exercises based on learner proficiency level, learning goals, performance history, and engagement patterns. The system likely uses collaborative filtering, content-based filtering, or hybrid recommendation algorithms to surface relevant learning materials, prioritizing content that addresses identified knowledge gaps and aligns with learner-specified goals (e.g., business English, IELTS preparation).","intents":["I want to practice vocabulary and phrases relevant to my job or interests, not generic content","I want the system to recommend topics based on my weak areas, not just what comes next in the curriculum","I want learning materials tailored to my specific goal (e.g., passing an exam, improving business communication)"],"best_for":["Goal-oriented learners with specific English use cases (business, academic, travel)","Learners with diverse interests who want content aligned with their passions","Professionals who want to learn English vocabulary and phrases relevant to their industry"],"limitations":["Requires substantial user interaction history to generate accurate recommendations; new users get generic recommendations","Content library size and diversity directly impact recommendation quality — limited content pool reduces personalization effectiveness","Recommendation algorithms can create filter bubbles, repeatedly suggesting similar content and limiting exposure to new topics","Goal-based recommendations require explicit learner goal input; inferred goals may be inaccurate","No guarantee that recommended content aligns with learner's actual learning needs vs. stated preferences"],"requires":["User interaction history (at least 20-50 completed lessons)","Learner proficiency level and learning goals metadata","Content metadata (topic, difficulty, vocabulary list, grammar concepts covered)","Recommendation algorithm infrastructure (collaborative filtering, content-based, or hybrid)"],"input_types":["learner proficiency level","learning goals (explicit or inferred from behavior)","performance history and engagement metrics","content metadata (topic, difficulty, vocabulary, grammar concepts)"],"output_types":["ranked list of recommended lessons or exercises","recommendation rationale (e.g., 'addresses your weak area in past tense')","content preview or summary"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_leya-ai__cap_4","uri":"capability://data.processing.analysis.learner.progress.tracking.and.analytics.dashboard","name":"learner-progress-tracking-and-analytics-dashboard","description":"Aggregates learner performance data (accuracy, response times, engagement metrics, knowledge retention) and visualizes progress across multiple dimensions (proficiency level, vocabulary mastery, grammar topics, speaking fluency). The system likely tracks fine-grained metrics (e.g., per-phoneme pronunciation accuracy, per-grammar-rule error rates) and surfaces actionable insights (e.g., 'your past tense accuracy is 72% — focus on irregular verbs') to help learners understand their progress and identify areas for improvement.","intents":["I want to see detailed progress metrics across different skills (reading, writing, speaking, listening)","I want to understand which specific grammar topics or vocabulary areas I'm struggling with","I want to track my learning velocity and estimate time to reach my proficiency goal"],"best_for":["Goal-oriented learners who want quantitative progress metrics","Professionals tracking learning ROI and time investment","Learners who benefit from data-driven motivation and goal-setting"],"limitations":["Metrics can be misleading if not contextualized (e.g., high accuracy on easy content doesn't indicate true mastery)","Retention metrics require spaced repetition data collection over weeks/months; short-term metrics may not predict long-term retention","Proficiency level estimates depend on accurate ability modeling; errors in underlying algorithms propagate to dashboard visualizations","Dashboard overload: too many metrics can overwhelm learners and distract from actual learning","No causal analysis: dashboard shows correlations (e.g., 'you improved after 5 days of practice') but not causation"],"requires":["Comprehensive performance data collection across all learner interactions","Backend analytics infrastructure (data warehouse, ETL pipeline)","Visualization library (e.g., D3.js, Plotly, or custom charting)","Proficiency level estimation algorithm (IRT or Bayesian model)"],"input_types":["learner performance data (accuracy, response time, confidence)","lesson/exercise metadata (topic, difficulty, skill category)","temporal data (timestamps of interactions)"],"output_types":["proficiency level estimates (numeric or CEFR level)","skill-specific accuracy metrics (e.g., 'past tense: 72%')","progress visualizations (line charts, heatmaps, skill radars)","actionable insights and recommendations"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_leya-ai__cap_5","uri":"capability://memory.knowledge.spaced.repetition.scheduling.with.forgetting.curve.modeling","name":"spaced-repetition-scheduling-with-forgetting-curve-modeling","description":"Schedules vocabulary and grammar review based on learner forgetting curves and optimal spacing intervals, using algorithms like SM-2 (SuperMemo) or Leitner system variants to determine when to resurface previously-learned content. The system models individual forgetting rates (how quickly each learner forgets specific items) and adjusts spacing intervals dynamically based on review performance, ensuring efficient long-term retention without excessive repetition.","intents":["I want vocabulary and grammar concepts to be reviewed at optimal intervals so I remember them long-term","I want the system to adjust review frequency based on how quickly I forget specific items","I want to avoid wasting time reviewing content I already know well"],"best_for":["Learners focused on long-term vocabulary retention and fluency","Professionals who need to retain specialized vocabulary (e.g., industry jargon)","Self-directed learners who want scientifically-optimized review schedules"],"limitations":["Requires historical review data to estimate individual forgetting curves; initial spacing intervals are generic","Forgetting curve models assume consistent learning conditions; stress, sleep deprivation, or other factors can invalidate estimates","Spacing algorithms can create 'review debt' if learner skips scheduled reviews, requiring catch-up sessions","No guarantee that spaced repetition alone produces fluency; retention without active use doesn't guarantee production ability","User experience friction: optimal spacing may require reviews at inconvenient times, reducing adherence"],"requires":["Historical review data (at least 10-20 reviews per item for accurate forgetting curve estimation)","Forgetting curve model (SM-2, Leitner, or custom Bayesian model)","Scheduling algorithm to compute optimal next review time","Backend persistence for review history and scheduling state"],"input_types":["vocabulary/grammar item to be reviewed","review performance (correct/incorrect, confidence level)","learner's current proficiency level and learning goals"],"output_types":["next scheduled review time","difficulty/ease factor for the item (used to adjust future spacing)","review queue (prioritized list of items due for review)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_leya-ai__cap_6","uri":"capability://text.generation.language.conversational.dialogue.practice.with.ai.tutor","name":"conversational-dialogue-practice-with-ai-tutor","description":"Enables learners to practice English conversation with an AI tutor that generates contextually-appropriate responses, asks follow-up questions, and provides feedback on grammar, vocabulary, and fluency. The system likely uses a large language model (LLM) to generate natural dialogue, with guardrails to keep conversations on-topic and at appropriate difficulty levels, and integrates pronunciation feedback and grammar correction into the dialogue flow.","intents":["I want to practice speaking English in realistic conversations without waiting for a human tutor","I want the AI to adjust conversation difficulty and topics based on my proficiency level","I want feedback on my grammar, vocabulary, and pronunciation integrated into the conversation"],"best_for":["Learners seeking speaking practice without access to live tutors","Professionals preparing for English-language conversations (interviews, presentations, negotiations)","Introverted learners who prefer practicing with AI before speaking with humans"],"limitations":["LLM-generated responses may be unnatural or grammatically incorrect, modeling poor English for learners","Conversation context windows are limited; long conversations may lose coherence or topic consistency","AI tutor cannot assess non-verbal communication (body language, eye contact) or provide cultural context","Learners may develop bad habits (e.g., unnatural phrasing) if AI feedback is inaccurate or inconsistent","Conversation topics and scenarios are limited by training data; AI may struggle with niche or specialized domains","No real-time streaming dialogue; latency between learner input and AI response may disrupt conversation flow"],"requires":["Large language model (GPT-4, Claude, or similar) with fine-tuning for English language teaching","Conversation context management (e.g., prompt engineering, conversation history tracking)","Speech recognition for spoken input (optional, for voice-based practice)","Grammar and pronunciation feedback integration","Topic/difficulty guardrails to keep conversations on-topic and at appropriate level"],"input_types":["learner's conversational input (text or audio)","conversation topic or scenario","learner proficiency level","conversation history (for context)"],"output_types":["AI tutor's response (text or audio)","grammar/vocabulary/pronunciation feedback","conversation summary or key points","learning recommendations based on conversation performance"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_leya-ai__cap_7","uri":"capability://planning.reasoning.goal.based.learning.path.generation","name":"goal-based-learning-path-generation","description":"Generates personalized learning paths aligned with learner-specified goals (e.g., 'pass IELTS with 7.0', 'improve business English for presentations', 'prepare for job interview'). The system likely maps goals to required competencies, selects relevant content and exercises, and sequences them in a logical progression that balances skill-building with goal-specific practice. Paths are dynamically adjusted based on learner progress and performance.","intents":["I want a clear learning plan that will help me achieve my specific English goal","I want to focus on skills and vocabulary relevant to my goal, not generic content","I want to know how much time and effort my goal will require"],"best_for":["Goal-oriented learners with specific, measurable English objectives","Professionals preparing for exams, interviews, or presentations","Learners with limited time who want efficient, goal-aligned learning"],"limitations":["Goal-to-competency mapping is domain-specific; system may not handle niche or specialized goals well","Time-to-goal estimates depend on accurate learner ability modeling and content difficulty calibration; estimates may be inaccurate","Learning paths assume linear skill progression; some goals require parallel skill development (e.g., IELTS requires simultaneous reading, writing, listening, speaking improvement)","Learner motivation and consistency affect goal achievement more than learning path quality; system cannot guarantee goal attainment","Goal scope creep: learners may add or change goals mid-path, requiring path regeneration"],"requires":["Goal taxonomy and goal-to-competency mapping database","Content library with metadata (topic, difficulty, skill category, goal alignment)","Learner proficiency level and current ability estimates","Path generation algorithm (e.g., topological sort with difficulty constraints, dynamic programming)"],"input_types":["learner's goal (text description or selection from predefined list)","learner's current proficiency level","available time/effort budget (optional)","learner's learning preferences (e.g., prefer speaking practice over writing)"],"output_types":["learning path (sequence of lessons/exercises with estimated duration)","milestone checkpoints (e.g., 'reach B1 level', 'complete 50 vocabulary items')","estimated time-to-goal","progress tracking against goal milestones"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Minimum 10-20 completed lessons to establish reliable learner ability estimates","Consistent user engagement (at least 2-3 sessions per week) for algorithm stability","Backend analytics infrastructure to track fine-grained performance metrics","Microphone access and browser/app audio recording permissions","Internet connection for ASR API calls (likely cloud-based)","Audio codec support (WAV, MP3, or similar)","Minimum 2-3 second utterance length for reliable phoneme detection","Natural language processing library with POS tagging and dependency parsing (e.g., spaCy, NLTK, or proprietary NLP pipeline)","Grammar rule database or rule engine (e.g., LanguageTool, custom rule set)","Learner proficiency level metadata to calibrate explanation complexity"],"failure_modes":["Requires sufficient historical performance data to calibrate difficulty — early lessons may not be optimally sequenced","Cold-start problem: new users get generic difficulty progression until system learns their profile","Difficulty estimation algorithms can plateau or oscillate if learner performance is highly variable","No guarantee that skipped content won't create knowledge gaps in later, dependent topics","ASR accuracy varies significantly by accent, background noise, and microphone quality — non-native accents may be misrecognized","Phonetic analysis is language-pair specific; system trained on English may struggle with learners whose L1 has very different phoneme inventories","Cannot assess suprasegmental features (intonation, rhythm) as accurately as human listeners","Requires high-quality audio input; background noise or poor microphone quality degrades feedback accuracy","No real-time streaming ASR — likely requires full utterance recording before analysis, adding latency","Context-dependent errors (e.g., article usage, preposition selection) are harder to detect than surface-level errors; false positive rates increase with ambiguous sentences","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.446Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=leya-ai","compare_url":"https://unfragile.ai/compare?artifact=leya-ai"}},"signature":"SMyevZSMkMfuVpXMkZl8qm0azgLlObjHZYPNiYXFiH/8cURTf1fXKACWxzcNovzqUNoJiCJdT8RIWlp/0ovXDQ==","signedAt":"2026-06-21T12:01:16.522Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/leya-ai","artifact":"https://unfragile.ai/leya-ai","verify":"https://unfragile.ai/api/v1/verify?slug=leya-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}