{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_tutory","slug":"tutory","name":"Tutory","type":"product","url":"https://www.tutory.io","page_url":"https://unfragile.ai/tutory","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_tutory__cap_0","uri":"capability://planning.reasoning.adaptive.learning.path.generation","name":"adaptive-learning-path-generation","description":"Dynamically constructs personalized curricula by analyzing student performance data, learning velocity, and knowledge gaps using machine learning models that map prerequisite dependencies and recommend optimal content sequencing. The system continuously adjusts difficulty, pacing, and topic ordering based on real-time assessment results rather than static grade-level progression, enabling students to progress at their own pace while maintaining conceptual coherence.","intents":["I need to create individualized learning sequences for students with different starting knowledge levels and learning speeds","I want the system to automatically identify which concepts a student should review before advancing to new material","I need to ensure students aren't bored by content they've already mastered or overwhelmed by material they're not ready for"],"best_for":["tutoring centers managing cohorts of students with heterogeneous abilities","homeschooling parents seeking structured but flexible curricula","schools implementing blended learning models with supplemental AI-driven practice"],"limitations":["Requires sufficient historical performance data (typically 10+ assessments per student) to build accurate learner models; cold-start students receive generic paths initially","Prerequisite mapping is subject-specific and must be configured per curriculum; no automatic cross-domain dependency inference","Cannot account for non-academic factors (motivation, home environment, learning disabilities) without explicit teacher input"],"requires":["Student enrollment with baseline assessment or prior performance history","Curriculum content indexed with learning objectives and prerequisite tags","Minimum 2-week usage period to generate meaningful personalization signals"],"input_types":["student assessment results (scores, response times, error patterns)","curriculum metadata (learning objectives, prerequisites, difficulty ratings)","student demographic/contextual data (grade level, prior knowledge indicators)"],"output_types":["structured learning path (ordered list of topics with estimated time allocations)","difficulty progression curve (recommended content difficulty over time)","prerequisite remediation recommendations"],"categories":["planning-reasoning","adaptive-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tutory__cap_1","uri":"capability://text.generation.language.real.time.explanation.generation","name":"real-time-explanation-generation","description":"Generates contextual explanations and worked examples on-demand when students answer incorrectly or request clarification, using LLM-based reasoning to decompose concepts into scaffolded steps tailored to the student's current knowledge level and error type. The system analyzes the specific mistake (conceptual misunderstanding vs. careless error vs. missing prerequisite knowledge) and generates targeted explanations rather than generic help text, with optional multi-modal output (text, diagrams, analogies).","intents":["I want students to receive immediate, personalized help when they get stuck without waiting for teacher availability","I need to understand why a student made an error so I can address the root cause, not just mark it wrong","I want to reduce student frustration by providing explanations at the right level of abstraction for their current understanding"],"best_for":["tutoring platforms serving students across diverse learning styles and prior knowledge","teachers seeking to reduce time spent on repetitive explanations of common misconceptions","self-paced learning environments where immediate feedback is critical for engagement"],"limitations":["LLM-generated explanations may occasionally contain subtle errors or use imprecise language; requires human review for critical subjects (mathematics, science)","Cannot reliably infer deep conceptual misunderstandings from single-attempt errors; requires multiple assessment touchpoints for accurate diagnosis","Explanation quality degrades for highly specialized or domain-specific content outside LLM training data"],"requires":["LLM API access (OpenAI, Anthropic, or equivalent) with sufficient rate limits for real-time generation","Student response data including answer, correct answer, and question context","Curriculum metadata mapping questions to learning objectives and common misconceptions"],"input_types":["student response (answer provided, question ID, question text)","assessment metadata (correct answer, difficulty level, learning objective)","student context (prior performance on related topics, knowledge level estimate)"],"output_types":["text explanation (scaffolded, multi-step breakdown)","worked examples (step-by-step solution with reasoning)","conceptual analogies or visualizations (when supported)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tutory__cap_2","uri":"capability://data.processing.analysis.performance.analytics.and.progress.tracking","name":"performance-analytics-and-progress-tracking","description":"Aggregates student assessment data, learning session metrics, and engagement signals into a teacher-facing dashboard that visualizes mastery progression, identifies at-risk students, and highlights common misconceptions across cohorts. The system computes learning velocity (rate of improvement), retention metrics (performance decay over time), and predictive indicators of future struggle based on early warning signals, enabling data-driven intervention decisions.","intents":["I need to see which students are falling behind and require intervention before they disengage","I want to identify which concepts are causing widespread confusion so I can adjust my teaching","I need evidence of student progress to report to parents and administrators"],"best_for":["teachers and tutors managing multiple students simultaneously","school administrators tracking cohort-level learning outcomes","parents monitoring their child's progress in supplemental tutoring"],"limitations":["Predictive models for at-risk identification require 4+ weeks of data; early predictions are unreliable","Dashboard metrics are assessment-centric and may not capture learning gains in non-assessed domains (creativity, collaboration, critical thinking)","Requires consistent student engagement; gaps in usage data create blind spots in progress tracking"],"requires":["Minimum 5+ completed assessments per student for meaningful trend analysis","Consistent usage over 2+ week period to establish baseline learning velocity","Teacher account with dashboard access permissions"],"input_types":["assessment results (scores, timestamps, question-level performance)","session metadata (duration, topics covered, engagement signals)","student enrollment data (grade level, cohort assignment)"],"output_types":["progress dashboards (mastery curves, learning velocity, retention decay)","cohort analytics (common misconceptions, performance distribution)","alerts and recommendations (at-risk students, intervention suggestions)","exportable reports (progress summaries, outcome metrics)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tutory__cap_3","uri":"capability://data.processing.analysis.knowledge.gap.identification.and.remediation","name":"knowledge-gap-identification-and-remediation","description":"Automatically detects missing prerequisite knowledge or conceptual gaps by analyzing patterns in student errors, response times, and performance across related topics using diagnostic assessment algorithms. When gaps are identified, the system recommends targeted remediation content (review lessons, prerequisite drills, conceptual clarifications) and inserts them into the learning path before advancing to dependent material, preventing knowledge fragmentation.","intents":["I need to identify what foundational concepts a student is missing so I can address them before they cascade into larger learning problems","I want to avoid having students struggle with advanced material because they lack prerequisite knowledge","I need to know which students need review content and which can skip it to save time"],"best_for":["tutoring centers serving students with inconsistent prior education (transfers, gaps in schooling)","remedial education programs targeting students with significant knowledge deficits","self-paced learning platforms where prerequisite verification is critical"],"limitations":["Requires rich curriculum metadata mapping prerequisites and dependencies; incomplete prerequisite graphs lead to missed gaps","Cannot reliably distinguish between knowledge gaps and temporary performance fluctuations without multiple assessment attempts","May over-recommend remediation for students with test anxiety or temporary disengagement, wasting instructional time"],"requires":["Curriculum content with explicit prerequisite and learning objective tagging","Diagnostic assessments designed to probe specific prerequisite knowledge","Minimum 3+ assessments per topic to establish reliable mastery signals"],"input_types":["student assessment performance (scores, error patterns, response times)","curriculum dependency graph (prerequisites, learning objectives, topic relationships)","remediation content library (review lessons, prerequisite drills)"],"output_types":["gap identification report (missing prerequisites, confidence scores)","remediation recommendations (specific content to review, estimated time)","modified learning path (remediation inserted before dependent topics)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tutory__cap_4","uri":"capability://text.generation.language.multi.modal.content.delivery.and.adaptation","name":"multi-modal-content-delivery-and-adaptation","description":"Delivers learning content in multiple formats (text explanations, interactive simulations, video walkthroughs, visual diagrams, practice problems) and adapts format selection based on student learning style preferences, topic complexity, and demonstrated effectiveness for that student. The system tracks which content modalities correlate with better learning outcomes for each student and preferentially recommends high-performing formats while still exposing students to diverse modalities.","intents":["I want to serve students with different learning preferences (visual, kinesthetic, auditory) without creating separate curricula","I need to know which content formats are actually helping my students learn, not just which ones they prefer","I want to expose students to diverse learning modalities while optimizing for their strengths"],"best_for":["diverse student populations with heterogeneous learning preferences","tutoring platforms serving neurodivergent students who benefit from multi-modal instruction","schools implementing universal design for learning (UDL) principles"],"limitations":["Requires content creation in multiple modalities; single-format curricula cannot be adapted","Learning style effectiveness is individual and may not correlate with stated preferences; requires outcome data to validate","Video and interactive content require higher bandwidth and may not be accessible in low-connectivity environments"],"requires":["Content library with multiple format versions for each learning objective","Student learning outcome data (assessment scores) correlated with content format consumed","Sufficient content variety (minimum 2-3 formats per topic) for meaningful adaptation"],"input_types":["student learning style preferences (self-reported or inferred)","student performance data (scores by content format consumed)","content metadata (format type, topic, difficulty, estimated time)"],"output_types":["personalized content recommendations (format-specific suggestions)","learning path with format diversity (mix of modalities to maintain engagement)","format effectiveness analytics (which modalities correlate with mastery for this student)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tutory__cap_5","uri":"capability://text.generation.language.assessment.generation.and.question.banking","name":"assessment-generation-and-question-banking","description":"Automatically generates contextually relevant assessment questions aligned to learning objectives using templates, procedural generation, and LLM-based question synthesis. The system maintains a question bank with metadata (difficulty, learning objective, common misconceptions, discrimination index) and selects questions dynamically based on student knowledge state, preventing repetition while ensuring consistent assessment rigor and coverage of key concepts.","intents":["I need unlimited practice questions without manually creating them for every topic and difficulty level","I want assessments that are appropriately challenging for each student's current level","I need to ensure assessments cover all learning objectives without biasing toward easy or hard questions"],"best_for":["tutoring platforms with large student populations requiring frequent assessments","teachers seeking to generate practice materials without manual question writing","adaptive learning systems requiring dynamic assessment content"],"limitations":["LLM-generated questions may contain subtle errors or ambiguities; require human review for high-stakes assessments","Procedurally generated questions (e.g., math problems with randomized numbers) may have unintended difficulty variations","Question bank metadata (difficulty, discrimination) requires calibration through student response data; new questions lack reliability estimates"],"requires":["Learning objective definitions and question templates for each topic","LLM API access for question generation (OpenAI, Anthropic, or equivalent)","Question review workflow for quality assurance before deployment"],"input_types":["learning objectives and topic specifications","question templates and generation rules","student knowledge state (current mastery level, topics to assess)"],"output_types":["generated assessment questions (multiple choice, short answer, problem-solving)","question metadata (difficulty estimate, learning objective, common misconceptions)","adaptive question selection (questions matched to student knowledge state)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tutory__cap_6","uri":"capability://data.processing.analysis.student.engagement.and.motivation.tracking","name":"student-engagement-and-motivation-tracking","description":"Monitors engagement signals (session frequency, time-on-task, completion rates, interaction patterns) and motivation indicators (effort level, persistence on difficult problems, help-seeking behavior) to identify disengagement early and recommend interventions. The system correlates engagement metrics with learning outcomes to distinguish between productive struggle (high effort, eventual mastery) and unproductive struggle (high effort, no progress, leading to disengagement), enabling targeted support.","intents":["I need to know which students are at risk of dropping out before they disappear","I want to understand whether a student's low performance is due to lack of ability or lack of effort/motivation","I need to intervene with struggling students before they become completely disengaged"],"best_for":["tutoring centers with high student churn seeking to improve retention","online learning platforms where engagement is harder to monitor than in-person","teachers managing large cohorts and needing automated early warning systems"],"limitations":["Engagement metrics are platform-specific and may not reflect actual learning effort (e.g., offline study, collaboration with peers)","Motivation is multifactorial (intrinsic vs. extrinsic, influenced by home environment, peer effects) and cannot be fully captured by platform signals","Interventions (encouragement messages, difficulty adjustment) may have limited effectiveness if underlying barriers (cost, time constraints, family support) are not addressed"],"requires":["Detailed session and interaction logging (timestamps, duration, action sequences)","Assessment performance data correlated with engagement metrics","Baseline engagement patterns for cohort to identify anomalies"],"input_types":["session metadata (login frequency, session duration, time-on-task)","interaction patterns (questions attempted, help requests, content consumption)","performance data (assessment scores, error rates, learning velocity)"],"output_types":["engagement dashboards (session frequency, time-on-task trends)","disengagement alerts (students with declining engagement or unproductive struggle)","intervention recommendations (encouragement, difficulty adjustment, teacher outreach)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tutory__cap_7","uri":"capability://tool.use.integration.teacher.collaboration.and.curriculum.sharing","name":"teacher-collaboration-and-curriculum-sharing","description":"Enables teachers to create, share, and collaboratively refine custom curricula, learning paths, and assessment banks within the platform, with version control and feedback mechanisms. Teachers can fork existing curricula, adapt them for their students, and contribute improvements back to shared repositories, creating a community-driven curriculum library that evolves based on collective teaching experience and student outcome data.","intents":["I want to reuse and adapt curricula from other teachers rather than building everything from scratch","I need to share my best practices and learning materials with colleagues","I want to contribute to a community curriculum library that improves over time based on student outcomes"],"best_for":["teacher communities and professional learning networks","schools implementing shared curriculum standards across multiple classrooms","open-source education initiatives seeking to democratize high-quality learning materials"],"limitations":["Curriculum quality varies widely; no automated validation that shared curricula produce better outcomes","Intellectual property concerns may limit teacher willingness to share proprietary materials","Curriculum adaptation requires domain expertise; teachers may misapply curricula designed for different student populations"],"requires":["Teacher accounts with curriculum creation and sharing permissions","Version control system for tracking curriculum changes and attribution","Community moderation or curation to maintain quality standards"],"input_types":["curriculum definitions (learning objectives, content, assessments)","teacher feedback and ratings on shared curricula","student outcome data (to validate curriculum effectiveness)"],"output_types":["shared curriculum repository (browsable, searchable, filterable)","curriculum versions and forks (tracking changes and adaptations)","curriculum effectiveness metrics (student outcomes by curriculum)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tutory__cap_8","uri":"capability://text.generation.language.parent.communication.and.progress.reporting","name":"parent-communication-and-progress-reporting","description":"Generates automated progress reports for parents summarizing student learning outcomes, mastery progression, areas of strength and struggle, and recommended home support strategies. Reports are personalized to parent communication preferences (frequency, detail level, format) and include actionable insights (e.g., 'Your child has mastered fractions but needs practice with division; try these activities at home') rather than raw data dumps.","intents":["I need to keep parents informed about their child's progress without spending hours writing individual reports","I want to give parents actionable suggestions for how they can support learning at home","I need to communicate both strengths and areas for growth in a way that motivates rather than discourages"],"best_for":["tutoring centers and supplemental education providers serving parent-engaged families","schools seeking to improve parent-school communication and home-school alignment","homeschooling parents using Tutory as a supplement to track progress"],"limitations":["Automated reports may lack nuance about non-academic factors (motivation, engagement, social-emotional growth) that parents care about","Parents with low literacy or language barriers may struggle to interpret even simplified reports","Actionable home support suggestions require knowledge of family resources and constraints; 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