{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_learngpt","slug":"learngpt","name":"LearnGPT","type":"product","url":"https://learngpt.art","page_url":"https://unfragile.ai/learngpt","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_learngpt__cap_0","uri":"capability://planning.reasoning.adaptive.learning.path.personalization","name":"adaptive-learning-path-personalization","description":"Dynamically adjusts learning content sequencing and difficulty based on user performance metrics, engagement patterns, and learning velocity. The system likely employs item response theory (IRT) or similar psychometric models to estimate learner ability and recommend appropriately-calibrated content. Tracks assessment results, time-on-task, and interaction patterns to modify subsequent learning sequences without explicit user configuration.","intents":["I want the platform to automatically adjust content difficulty as I progress so I'm neither bored nor overwhelmed","I need personalized learning paths that account for my existing knowledge gaps and learning speed","I want the system to recommend what to study next based on my performance history"],"best_for":["Self-directed learners who benefit from scaffolded, difficulty-adjusted content","Students with heterogeneous prior knowledge in a subject domain","Learners who want hands-off sequencing without manual curriculum design"],"limitations":["Adaptive algorithms require sufficient interaction history (typically 10+ assessments) before meaningful personalization begins","No public documentation of the underlying psychometric model or IRT implementation details","Adaptation quality depends on assessment validity — if questions don't accurately measure competency, personalization may be ineffective","Cold-start problem for new users: first sessions likely serve generic content until behavioral data accumulates"],"requires":["Active user account with engagement history","Completion of initial diagnostic or baseline assessment","Consistent interaction with the platform (sporadic usage reduces adaptation effectiveness)"],"input_types":["user assessment responses (multiple choice, short answer, or interactive exercises)","engagement metrics (time spent, completion rates, retry patterns)","explicit user preferences or learning goals"],"output_types":["personalized content recommendations (next lesson, topic, or exercise)","difficulty-adjusted learning materials","adaptive quiz or assessment sequences"],"categories":["planning-reasoning","personalization-adaptive-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_learngpt__cap_1","uri":"capability://text.generation.language.multilingual.content.generation.and.localization","name":"multilingual-content-generation-and-localization","description":"Generates or adapts learning content across multiple languages with language-specific pedagogical considerations. Likely uses LLM-based translation with domain-specific fine-tuning for educational terminology, combined with cultural adaptation of examples and context. Supports both interface localization and content-level language switching, allowing learners to study in their native language while maintaining semantic consistency across language variants.","intents":["I want to learn in my native language, not English, without losing content quality or accuracy","I need learning materials that use culturally relevant examples and contexts for my region","I want to switch between languages mid-session or compare explanations across languages"],"best_for":["Non-English speaking learners in underserved language communities","Multilingual learners who benefit from code-switching or cross-language reinforcement","Global organizations deploying LearnGPT across diverse linguistic regions"],"limitations":["Translation quality varies by language pair; less-resourced languages may have lower fidelity","Cultural adaptation of examples is likely manual or requires additional curation, not fully automated","No documented support for right-to-left languages (Arabic, Hebrew) or complex scripts (CJK)","Multilingual content generation increases latency and API costs compared to single-language systems"],"requires":["Language selection at account setup or per-session","Supported language from platform's language matrix (specific list not publicly available)","Internet connectivity for real-time translation or content retrieval"],"input_types":["learning topic or subject in any supported language","user language preference","cultural context or region specification (optional)"],"output_types":["localized learning content (lessons, explanations, examples)","translated assessments and quizzes","language-specific interactive exercises"],"categories":["text-generation-language","localization-internationalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_learngpt__cap_2","uri":"capability://text.generation.language.interactive.exercise.generation.with.immediate.feedback","name":"interactive-exercise-generation-with-immediate-feedback","description":"Generates contextually-relevant practice exercises (multiple choice, fill-in-the-blank, short answer) based on current learning content and learner level, with immediate correctness feedback and explanation of errors. Uses LLM-based generation to create novel exercises rather than serving static question banks, enabling unlimited practice variety. Feedback likely includes not just right/wrong signals but explanations of misconceptions and links to relevant content sections.","intents":["I want unlimited practice problems on a topic without exhausting a static question bank","I need immediate feedback on my answers with explanations of why I was wrong","I want exercises that match my current learning level and recent content"],"best_for":["Active learners who benefit from spaced repetition and varied practice","Subjects with well-defined correct answers (math, languages, science) rather than open-ended domains","Learners who need real-time feedback to correct misconceptions immediately"],"limitations":["Generated exercises may have quality variance; some auto-generated questions may be ambiguous or have multiple valid answers","Exercise generation latency (likely 1-3 seconds per exercise) may interrupt flow for rapid learners","No documented mechanism for learners to flag or report low-quality generated exercises","Feedback explanations are LLM-generated and may oversimplify complex concepts or contain errors"],"requires":["Active learning session with selected topic or content module","Sufficient context from prior content to generate relevant exercises","Real-time API access to LLM backend for exercise generation"],"input_types":["current learning topic or lesson content","learner proficiency level","exercise type preference (multiple choice, short answer, etc.)","user answer or response to exercise"],"output_types":["generated exercise prompt with options or answer field","correctness evaluation (correct/incorrect)","feedback explanation with links to relevant content","difficulty rating or next recommended exercise"],"categories":["text-generation-language","interactive-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_learngpt__cap_3","uri":"capability://data.processing.analysis.progress.tracking.and.learning.analytics","name":"progress-tracking-and-learning-analytics","description":"Aggregates user interaction data (time spent, completion rates, assessment scores, retry patterns) into learner dashboards and analytics reports. Tracks progress across topics, identifies knowledge gaps, and visualizes learning velocity over time. Likely stores learner state in a relational or document database indexed by user ID and topic, with periodic aggregation jobs computing summary statistics and trend analysis.","intents":["I want to see my progress across all topics I'm studying and identify where I'm struggling","I need to understand my learning velocity and estimate time to mastery for a subject","I want detailed analytics on my study habits (time of day, session length, retry frequency) to optimize my learning"],"best_for":["Self-directed learners who benefit from data-driven insights into their learning","Educators or parents monitoring student progress across multiple learners","Learners preparing for high-stakes assessments who need granular performance tracking"],"limitations":["Analytics are only as good as the underlying assessment quality; if exercises don't validly measure competency, metrics are misleading","No documented export of analytics data (CSV, JSON) for external analysis or integration with other tools","Privacy considerations: detailed learning analytics may be sensitive; unclear what data retention and anonymization policies apply","Aggregation latency: real-time dashboards may lag behind actual user activity by minutes to hours"],"requires":["Active user account with sufficient interaction history (typically 5+ sessions)","Completion of assessments or exercises that generate performance data","Dashboard or analytics interface access (may require premium tier)"],"input_types":["user interaction events (exercise completion, assessment score, time-on-task)","topic or subject filters for scoped analytics","date range for trend analysis"],"output_types":["progress dashboard with topic-level completion percentages","performance charts (score trends, time-to-mastery estimates)","knowledge gap reports identifying weak areas","learning habit summaries (study frequency, session duration, retry patterns)"],"categories":["data-processing-analysis","learning-analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_learngpt__cap_4","uri":"capability://text.generation.language.conversational.tutoring.with.context.awareness","name":"conversational-tutoring-with-context-awareness","description":"Enables learners to ask questions in natural language about current learning content, with the system providing explanations, worked examples, and clarifications. Uses retrieval-augmented generation (RAG) or in-context learning to ground responses in the learner's current topic and prior interactions, avoiding generic ChatGPT-style responses. Maintains conversation history within a learning session to provide contextually-aware follow-up answers.","intents":["I want to ask a tutor questions about the current lesson without waiting for a human response","I need the AI to explain a concept in a different way if I don't understand the first explanation","I want to ask follow-up questions that build on previous explanations in our conversation"],"best_for":["Learners who benefit from Socratic dialogue and iterative clarification","Students studying complex or abstract topics (math, physics, philosophy) where multiple explanations help","Learners in time zones or regions with limited access to human tutors"],"limitations":["Conversational responses are LLM-generated and may contain factual errors, especially in specialized domains","No documented fact-checking or verification mechanism for tutor responses","Context window limitations: long conversation histories may be truncated, losing earlier context","Latency: conversational responses typically require 1-3 seconds, which may feel slow for rapid learners","No human escalation path if the AI tutor cannot answer a question"],"requires":["Active learning session with selected topic","Natural language input capability (text-based chat interface)","Real-time API access to LLM backend for response generation"],"input_types":["natural language question or request for clarification","current learning topic or lesson context","prior conversation history (within session)"],"output_types":["natural language explanation or answer","worked examples or step-by-step solutions","follow-up questions to check understanding","links to relevant content sections"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_learngpt__cap_5","uri":"capability://planning.reasoning.spaced.repetition.scheduling.for.retention","name":"spaced-repetition-scheduling-for-retention","description":"Implements spaced repetition algorithms (likely Leitner system or SM-2 variant) to schedule review of previously-learned content at optimal intervals for long-term retention. Tracks when items were last reviewed, current difficulty, and learner performance to determine when each item should next appear. Integrates with the adaptive learning engine to interleave new content with scheduled reviews.","intents":["I want the system to automatically schedule reviews of older material so I don't forget what I learned","I need review intervals that adapt based on how well I remember each item","I want to balance learning new content with reviewing old content efficiently"],"best_for":["Language learners and students memorizing large amounts of factual content","Learners preparing for cumulative exams where retention of prior material is critical","Self-directed learners who want to optimize study time without manual scheduling"],"limitations":["Spaced repetition is most effective for factual recall; less applicable to conceptual understanding or problem-solving","Algorithm parameters (initial interval, difficulty multiplier) are likely fixed and not tuned per learner","No documented support for learner-specific forgetting curves or individual differences in retention","Review scheduling may feel rigid or interrupt flow if learners prefer continuous forward progress"],"requires":["Completion of initial learning of content items","Consistent engagement with the platform (sporadic usage reduces effectiveness)","Performance data from prior reviews to calibrate scheduling"],"input_types":["learned content items (vocabulary, facts, concepts)","learner performance on prior reviews (correct/incorrect, response time)","current date/time for interval calculation"],"output_types":["scheduled review items for current session","review intervals (e.g., 'review again in 3 days')","difficulty adjustments based on performance"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_learngpt__cap_6","uri":"capability://data.processing.analysis.assessment.and.mastery.evaluation","name":"assessment-and-mastery-evaluation","description":"Administers assessments (quizzes, tests, projects) to measure learner mastery of topics and generates mastery scores or proficiency levels. Uses criterion-referenced evaluation (comparing against defined learning objectives) rather than norm-referenced (comparing against peers). Likely implements item response theory or similar psychometric models to estimate true ability from noisy assessment data, accounting for question difficulty and discrimination.","intents":["I want to know if I've truly mastered a topic before moving on to the next one","I need a fair assessment that accounts for question difficulty, not just raw score","I want to understand my proficiency level (beginner, intermediate, advanced) in each topic"],"best_for":["Learners who need objective mastery verification before advancing","Educators using LearnGPT to track student competency across cohorts","Certification or credential programs requiring validated mastery evidence"],"limitations":["Assessment validity depends on question quality; auto-generated questions may not validly measure mastery","No documented accommodation for learners with disabilities (extended time, alternative formats)","Mastery thresholds (e.g., 80% = mastery) are likely fixed and not personalized","No documented security measures against cheating or unauthorized assistance during assessments"],"requires":["Completion of learning content prior to assessment","Sufficient assessment items to reliably estimate ability (typically 10+ questions)","Learner agreement to assessment terms and academic integrity policies"],"input_types":["assessment items (questions, prompts, tasks)","learner responses to assessment items","item metadata (difficulty, learning objective alignment)"],"output_types":["mastery score or proficiency level (e.g., 'Proficient', 'Advanced')","item-level performance (correct/incorrect per question)","ability estimate with confidence interval (if using IRT)","recommendations for remediation or advancement"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_learngpt__cap_7","uri":"capability://planning.reasoning.goal.setting.and.learning.plan.generation","name":"goal-setting-and-learning-plan-generation","description":"Helps learners define learning goals (e.g., 'master calculus in 8 weeks') and generates personalized learning plans with milestones, estimated time-to-completion, and recommended content sequences. Uses learner profiling (prior knowledge, available study time, learning style) to tailor plan recommendations. Integrates with progress tracking to monitor plan adherence and adjust recommendations if learner falls behind.","intents":["I want to set a specific learning goal and get a realistic timeline for achieving it","I need a structured learning plan that breaks down a large topic into manageable milestones","I want the system to adjust my plan if I'm falling behind or progressing faster than expected"],"best_for":["Goal-oriented learners who benefit from structured plans and milestone tracking","Busy professionals or students with limited study time who need efficient learning paths","Learners preparing for specific exams or certifications with defined deadlines"],"limitations":["Plan accuracy depends on learner profiling quality; if prior knowledge assessment is inaccurate, timelines will be wrong","No documented mechanism for learners to manually adjust plans or override recommendations","Learning time estimates are likely averages and don't account for individual differences in learning speed","Plans may become stale if learner's circumstances change (e.g., available study time decreases)"],"requires":["Learner goal specification (topic, target proficiency level, deadline)","Prior knowledge assessment or self-report","Estimated weekly study time availability","Access to learning plan interface (may require premium tier)"],"input_types":["learning goal (topic, target proficiency, deadline)","learner profile (prior knowledge, learning style, available time)","progress data for plan adjustment"],"output_types":["personalized learning plan with topic sequence","milestone definitions with estimated completion dates","time-to-completion estimate","recommended daily/weekly study schedule","plan adjustment recommendations based on progress"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Active user account with engagement history","Completion of initial diagnostic or baseline assessment","Consistent interaction with the platform (sporadic usage reduces adaptation effectiveness)","Language selection at account setup or per-session","Supported language from platform's language matrix (specific list not publicly available)","Internet connectivity for real-time translation or content retrieval","Active learning session with selected topic or content module","Sufficient context from prior content to generate relevant exercises","Real-time API access to LLM backend for exercise generation","Active user account with sufficient interaction history (typically 5+ sessions)"],"failure_modes":["Adaptive algorithms require sufficient interaction history (typically 10+ assessments) before meaningful personalization begins","No public documentation of the underlying psychometric model or IRT implementation details","Adaptation quality depends on assessment validity — if questions don't accurately measure competency, personalization may be ineffective","Cold-start problem for new users: first sessions likely serve generic content until behavioral data accumulates","Translation quality varies by language pair; less-resourced languages may have lower fidelity","Cultural adaptation of examples is likely manual or requires additional curation, not fully automated","No documented support for right-to-left languages (Arabic, Hebrew) or complex scripts (CJK)","Multilingual content generation increases latency and API costs compared to single-language systems","Generated exercises may have quality variance; some auto-generated questions may be ambiguous or have multiple valid answers","Exercise generation latency (likely 1-3 seconds per exercise) may interrupt flow for rapid learners","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.63,"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":"2026-04-05T13:23:42.562Z","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=learngpt","compare_url":"https://unfragile.ai/compare?artifact=learngpt"}},"signature":"ee9RlvdrXGb+U5bHk3umzDjf3HeV80CZ6XaX42FB+baUD4jWmHT5yUl0bXJOfgRtMkm6/Uac0yye0YpNPZFjAg==","signedAt":"2026-06-20T07:43:53.858Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/learngpt","artifact":"https://unfragile.ai/learngpt","verify":"https://unfragile.ai/api/v1/verify?slug=learngpt","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"}}