{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_quino","slug":"quino","name":"Quino","type":"product","url":"https://quino.ai","page_url":"https://unfragile.ai/quino","categories":["research-search"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_quino__cap_0","uri":"capability://planning.reasoning.adaptive.difficulty.progression.engine","name":"adaptive-difficulty-progression-engine","description":"Dynamically adjusts content difficulty and pacing in real-time based on learner performance metrics (completion time, accuracy, engagement signals). The system likely uses a Bayesian or item-response-theory model to estimate learner mastery levels and recommends next-optimal content difficulty, reducing manual curriculum sequencing and preventing cognitive overload or boredom.","intents":["I want content to automatically get harder when a student masters concepts, without me manually creating difficulty tiers","I need the system to slow down and repeat concepts when a student struggles, rather than forcing them through a fixed curriculum","I want to understand why the system chose this specific content for this specific student"],"best_for":["Individual educators piloting personalized learning without curriculum design expertise","Small schools wanting to reduce teacher workload in content sequencing","Instructors teaching heterogeneous skill-level cohorts in a single classroom"],"limitations":["No documented ability to customize the underlying difficulty model for domain-specific knowledge structures (e.g., prerequisites in mathematics vs. language learning differ)","Likely requires minimum engagement data (5-10 interactions per learner) before adaptation becomes effective","No mention of support for non-linear learning paths or prerequisite graphs beyond simple difficulty scaling"],"requires":["Active learner account with at least one completed lesson","Instructor to have created or imported content with difficulty metadata","Browser with JavaScript enabled for real-time UI updates"],"input_types":["learner performance events (quiz scores, time-on-task, interaction logs)","content metadata (difficulty level, topic, prerequisites)"],"output_types":["recommended next content item","difficulty adjustment signal","learner mastery estimate (likely 0-1 scale)"],"categories":["planning-reasoning","adaptive-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quino__cap_1","uri":"capability://data.processing.analysis.learner.performance.analytics.dashboard","name":"learner-performance-analytics-dashboard","description":"Aggregates learner interaction data (quiz attempts, time-on-task, content engagement) and surfaces key metrics (mastery estimates, completion rates, struggle indicators) in a teacher-facing dashboard. The system likely tracks event streams and computes rolling statistics to identify at-risk learners or content bottlenecks without requiring manual data export or external analytics tools.","intents":["I want to see which students are struggling without manually reviewing each quiz","I need to identify which content topics cause the most learner confusion across my class","I want to track individual learner progress over time to inform intervention decisions"],"best_for":["Teachers managing 20-100 learners who need quick visibility into class-wide performance trends","Educators seeking to identify at-risk learners early without manual assessment review","Small institutions without dedicated data analytics teams"],"limitations":["Editorial summary notes 'minimal documentation on AI customization options' — likely no ability to define custom metrics or create predictive models for learner outcomes","No mention of export capabilities (CSV, API) for integration with institutional data warehouses","Probably lacks cohort comparison or A/B testing features needed for curriculum evaluation","Real-time analytics may have latency (5-15 minute delay) depending on event processing architecture"],"requires":["At least 5 learners with 3+ completed activities each to generate meaningful statistics","Teacher account with class/cohort setup","Modern browser (Chrome, Firefox, Safari 2022+)"],"input_types":["learner event logs (quiz submissions, content views, time-on-task)","assessment responses (multiple choice, free text, interactive)"],"output_types":["dashboard visualizations (charts, tables)","learner mastery scores","content difficulty heatmaps","at-risk learner flags"],"categories":["data-processing-analysis","learning-analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quino__cap_2","uri":"capability://text.generation.language.ai.powered.content.generation.and.curation","name":"ai-powered-content-generation-and-curation","description":"Generates or curates learning content (lessons, quizzes, explanations) using LLM-based generation, likely with prompt engineering or fine-tuning to match pedagogical standards. The system probably accepts topic/learning objective inputs and produces structured content (lesson outlines, multiple-choice questions, worked examples) that educators can review and customize before deployment.","intents":["I want to quickly generate quiz questions for a new topic without writing them manually","I need AI to create lesson explanations tailored to different difficulty levels","I want to generate practice problems that align with specific learning objectives"],"best_for":["Individual educators and small schools lacking instructional design resources","Teachers needing rapid content iteration for new or updated curricula","Instructors teaching multiple sections who want to scale content creation"],"limitations":["No documented ability to fine-tune generation models for domain-specific content (e.g., STEM vs. humanities have different pedagogical norms)","Likely generates generic content without subject-matter expert review, risking factual errors or pedagogically inappropriate difficulty","No mention of support for generating multimedia content (images, videos, interactive simulations)","Generated content probably requires manual review before deployment, negating some time-savings"],"requires":["Instructor account with content creation permissions","Topic or learning objective description (text input)","Optional: reference materials or existing content to guide generation"],"input_types":["topic/subject description (text)","learning objective (text)","difficulty level (categorical: beginner/intermediate/advanced)","content type (lesson, quiz, explanation)"],"output_types":["lesson outlines (structured text)","quiz questions (multiple choice, short answer)","worked examples (text + optional diagrams)","explanations (narrative text)"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quino__cap_3","uri":"capability://planning.reasoning.personalized.learning.path.orchestration","name":"personalized-learning-path-orchestration","description":"Constructs individualized learning sequences by combining adaptive difficulty adjustment, learner preference signals (if available), and content metadata (prerequisites, topic relationships). The system likely uses a state machine or graph-based approach to track learner progress through a curriculum and recommend next steps, rather than forcing all learners through a fixed sequence.","intents":["I want each student to follow a unique learning path based on their strengths and weaknesses","I need the system to respect prerequisite relationships so students don't skip foundational concepts","I want to allow students some choice in topic order while ensuring they master core concepts"],"best_for":["Educators teaching mixed-ability classrooms who want to avoid one-size-fits-all pacing","Self-paced or blended learning environments where learners progress asynchronously","Institutions piloting competency-based education models"],"limitations":["No documented support for complex prerequisite graphs or non-linear curriculum structures","Likely assumes a single 'optimal' path per learner rather than allowing multiple valid paths through content","No mention of learner agency or choice in path selection — system may be fully deterministic based on performance","Probably lacks support for remediation loops or spiral curriculum patterns (revisiting concepts at deeper levels)"],"requires":["Curriculum structure with content items and difficulty metadata","At least one learner interaction to initialize personalization","Instructor to have defined learning objectives or competencies"],"input_types":["learner performance history (quiz scores, completion status)","content graph (topics, prerequisites, difficulty levels)","learner preferences (if supported)"],"output_types":["recommended next content item","learning path visualization (for instructor review)","estimated time-to-mastery for remaining content"],"categories":["planning-reasoning","personalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quino__cap_4","uri":"capability://data.processing.analysis.multi.format.content.import.and.normalization","name":"multi-format-content-import-and-normalization","description":"Accepts learning content in multiple formats (likely PDF, DOCX, HTML, or LMS export formats) and normalizes it into Quino's internal content model for use in adaptive sequencing and analytics. The system probably parses document structure, extracts learning objectives, and maps content to difficulty levels, enabling educators to reuse existing materials without manual reformatting.","intents":["I want to import my existing PowerPoint slides and have them automatically converted to interactive lessons","I need to migrate content from another LMS without manually recreating everything","I want to upload a PDF textbook chapter and have it automatically split into bite-sized lessons"],"best_for":["Educators migrating from traditional LMS platforms (Canvas, Blackboard, Moodle)","Teachers with existing content libraries who want to avoid manual content recreation","Institutions with legacy learning materials in various formats"],"limitations":["No documented support for multimedia content (embedded videos, interactive simulations, animations)","Likely limited to text-based content extraction — may struggle with complex layouts, tables, or diagrams","Probably requires manual review and cleanup after import, especially for content with non-standard structure","No mention of support for proprietary LMS formats or SCORM packages","Automated difficulty level assignment likely inaccurate without subject-matter expert review"],"requires":["Content file in supported format (PDF, DOCX, HTML, or LMS export)","Instructor account with content upload permissions","Optional: metadata (subject, difficulty level, learning objectives) to guide normalization"],"input_types":["PDF documents","DOCX/Word files","HTML pages","LMS export files (format unknown)"],"output_types":["normalized lesson content (structured text)","extracted learning objectives","inferred difficulty levels","content metadata (topic, prerequisites)"],"categories":["data-processing-analysis","content-migration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quino__cap_5","uri":"capability://data.processing.analysis.learner.engagement.and.motivation.tracking","name":"learner-engagement-and-motivation-tracking","description":"Monitors learner engagement signals (session frequency, time-on-task, content completion rates, interaction patterns) and surfaces motivation indicators in the teacher dashboard. The system likely uses heuristics or simple ML models to flag disengaged learners (e.g., declining session frequency, incomplete lessons) and may provide intervention suggestions or gamification elements to boost engagement.","intents":["I want to identify students who are falling behind or losing motivation before they disengage completely","I need to understand which content types keep students engaged longest","I want to send targeted encouragement or intervention to at-risk learners"],"best_for":["Teachers managing self-paced or blended learning environments where learner disengagement is a risk","Educators seeking early warning signals for at-risk learners","Institutions focused on learner retention and completion rates"],"limitations":["Engagement metrics are likely surface-level (time-on-task, completion) without deeper behavioral analysis","No documented support for custom engagement rules or institution-specific motivation models","Probably lacks integration with external communication tools (email, SMS) for automated interventions","May not distinguish between productive struggle and genuine disengagement","No mention of support for different engagement patterns across demographics or learning styles"],"requires":["Learner account with at least 3-5 sessions of activity data","Instructor account with analytics access","Optional: learner profile data (age, prior knowledge) to contextualize engagement"],"input_types":["learner session logs (login time, duration, content accessed)","interaction events (quiz attempts, content views, time-on-task)","completion status (lessons finished, quizzes submitted)"],"output_types":["engagement score (likely 0-100 scale)","disengagement flags (boolean or risk level)","engagement trend visualization (over time)","intervention recommendations (text)"],"categories":["data-processing-analysis","learner-analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quino__cap_6","uri":"capability://automation.workflow.freemium.tier.access.and.quota.management","name":"freemium-tier-access-and-quota-management","description":"Implements a freemium business model with quota-based access control, likely limiting free-tier users to a maximum number of learners, content items, or monthly interactions. The system probably enforces quotas at the API/application layer and provides upgrade prompts when users approach limits, enabling educators to pilot the platform without upfront cost while driving conversion to paid tiers.","intents":["I want to test Quino with my class before committing to a paid plan","I need to understand pricing and upgrade paths before deciding to scale","I want to use Quino for a small pilot without financial risk"],"best_for":["Individual educators and small schools evaluating AI-powered learning platforms","Institutions piloting personalized learning before institutional adoption","Teachers with limited budgets who want to test before scaling"],"limitations":["Free tier quotas likely restrict number of learners (probably 20-50) or monthly interactions, limiting real-world testing","Freemium model may create artificial feature restrictions (e.g., analytics depth, content generation limits) to drive upgrades","No documented information on pricing tiers, feature differences, or upgrade paths","Free tier may have longer support response times or limited customer success resources"],"requires":["Email address to create account","No payment method required for free tier","Optional: institutional affiliation for educational discounts"],"input_types":["user account creation data (email, name, institution)","usage metrics (learner count, content items, monthly interactions)"],"output_types":["account tier status (free/paid)","quota usage (percentage of limit)","upgrade prompts and pricing information"],"categories":["automation-workflow","business-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quino__cap_7","uri":"capability://memory.knowledge.learner.profile.and.preference.management","name":"learner-profile-and-preference-management","description":"Maintains learner profiles capturing learning history, performance data, and optionally learner preferences (preferred content types, pacing speed, learning style indicators). The system likely uses profile data to personalize content recommendations and adapt presentation format, though the extent of preference capture and use is undocumented.","intents":["I want the system to remember each student's learning history and use it to personalize future recommendations","I need learners to be able to indicate their preferred learning pace or content format","I want to track learner progress across multiple courses or semesters"],"best_for":["Educators managing learners across multiple courses or time periods","Institutions seeking to build longitudinal learner profiles for personalization","Platforms where learner agency and preference expression are important"],"limitations":["No documented support for learner-expressed preferences — system may only use implicit signals (performance, engagement)","Likely no support for learning style assessments or preference elicitation beyond basic profile setup","Probably lacks integration with external identity systems (LDAP, OAuth, LTI) for cross-platform profile portability","No mention of learner data privacy controls or FERPA/GDPR compliance features","Profile data probably not exportable by learners or educators"],"requires":["Learner account creation with basic profile data (name, email, optional: grade level, prior knowledge)","At least one completed activity to begin building performance history","Optional: learner preference inputs (if supported)"],"input_types":["learner demographic data (name, email, grade level, institution)","performance history (quiz scores, completion status, time-on-task)","learner preferences (if supported)","engagement signals (session frequency, content type interactions)"],"output_types":["learner profile summary (for instructor view)","performance history (aggregated statistics)","personalization signals (used internally for recommendations)","learner dashboard (for learner self-view)"],"categories":["memory-knowledge","personalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quino__cap_8","uri":"capability://text.generation.language.clean.student.focused.user.interface","name":"clean-student-focused-user-interface","description":"Provides a simplified, distraction-free learner interface optimized for content consumption and interaction, likely prioritizing lesson content, progress indicators, and next-step recommendations over administrative features. The design philosophy emphasizes learner experience over teacher administrative complexity, reducing cognitive load and improving engagement.","intents":["I want my students to focus on learning without being overwhelmed by administrative features","I need a clean interface that works well on mobile devices for flexible learning","I want students to easily understand their progress and what to do next"],"best_for":["K-12 educators prioritizing learner experience and engagement","Self-paced or blended learning environments where learner autonomy is important","Institutions with diverse learner populations (varying tech literacy, ages)"],"limitations":["Simplified interface may lack advanced features that power users (instructional designers, data analysts) need","No documented support for customizing learner interface (branding, layout, feature visibility)","Likely optimized for web browsers — mobile app support unknown","Probably lacks accessibility features (screen reader support, high-contrast modes) beyond basic WCAG compliance","No mention of support for different interface languages or localization"],"requires":["Modern web browser (Chrome, Firefox, Safari 2020+)","Optional: mobile device for responsive design testing","Learner account with at least one enrolled course"],"input_types":["learner interactions (content views, quiz submissions, progress tracking)","system state (current lesson, completion status, next recommendations)"],"output_types":["rendered lesson content (HTML/CSS)","progress indicators (visual)","next-step recommendations (UI elements)","learner dashboard (summary view)"],"categories":["text-generation-language","user-experience"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active learner account with at least one completed lesson","Instructor to have created or imported content with difficulty metadata","Browser with JavaScript enabled for real-time UI 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models for learner outcomes","No mention of export capabilities (CSV, API) for integration with institutional data warehouses","Probably lacks cohort comparison or A/B testing features needed for curriculum evaluation","Real-time analytics may have latency (5-15 minute delay) depending on event processing architecture","No documented ability to fine-tune generation models for domain-specific content (e.g., STEM vs. humanities have different pedagogical norms)","Likely generates generic content without subject-matter expert review, risking factual errors or pedagogically inappropriate difficulty","No mention of support for generating multimedia content (images, videos, interactive simulations)","builder identity is not verified yet","no observed match outcomes 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