{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_schoolhack","slug":"schoolhack","name":"SchoolHack","type":"product","url":"https://schoolhack.ai","page_url":"https://unfragile.ai/schoolhack","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_schoolhack__cap_0","uri":"capability://planning.reasoning.ai.driven.personalized.learning.path.generation","name":"ai-driven personalized learning path generation","description":"Generates adaptive learning sequences tailored to individual student performance and learning pace by analyzing student interactions, assessment results, and engagement patterns. The system likely uses a combination of learning analytics (tracking time-on-task, error patterns, concept mastery) and rule-based or ML-based recommendation algorithms to suggest next topics, difficulty levels, and content formats. This differs from static curriculum delivery by dynamically adjusting content sequencing based on real-time student data.","intents":["I need to automatically adjust lesson difficulty based on how individual students are performing","I want the platform to recommend which topics a struggling student should review before moving forward","I need to identify which students are progressing faster and provide them with advanced content"],"best_for":["Individual educators in under-resourced schools seeking low-cost personalization","Small schools experimenting with AI-assisted differentiation without dedicated instructional designers"],"limitations":["Underlying pedagogical model is undocumented—unclear if adaptation is based on learning science principles or simple performance metrics","No visibility into curriculum alignment—may not map to state standards or specific educational frameworks","Personalization effectiveness depends on data quality and volume; sparse interaction data may produce generic recommendations"],"requires":["Student enrollment and basic demographic data","Regular student interaction with platform (assessments, content engagement)","Internet connectivity for real-time analytics processing"],"input_types":["student assessment scores","time-on-task metrics","interaction logs (clicks, submissions)","learning objective metadata"],"output_types":["recommended next topic","suggested content format (video, text, interactive)","difficulty level adjustment","personalized learning plan (structured data)"],"categories":["planning-reasoning","educational-personalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_schoolhack__cap_1","uri":"capability://data.processing.analysis.automated.student.assessment.and.progress.tracking","name":"automated student assessment and progress tracking","description":"Automatically generates, administers, and grades assessments while tracking student progress across learning objectives. The system likely uses prompt-based question generation (leveraging LLMs to create variations of assessment items) combined with automated grading logic for multiple-choice, short-answer, or constructed-response items. Progress tracking aggregates assessment data into dashboards showing mastery levels, skill gaps, and learning velocity per student and cohort.","intents":["I need to quickly create quizzes aligned to specific learning objectives without manually writing every question","I want to automatically grade student work and identify which concepts the class hasn't mastered","I need a dashboard showing which students are at risk of falling behind"],"best_for":["Teachers managing large class sizes who need to reduce grading workload","Schools without dedicated assessment specialists or data analysts"],"limitations":["LLM-generated questions may lack pedagogical rigor or contain subtle errors; no mention of human review workflows","Automated grading for open-ended responses is unreliable—likely limited to objective items or requires teacher validation","No documented approach to handling diverse learner needs (ELL, special education, gifted students) in assessment design","Progress tracking may conflate correlation with causation—high engagement ≠ learning"],"requires":["Learning objectives or curriculum standards defined in platform","Student enrollment with assessment history","Teacher access to review and override automated grades"],"input_types":["learning objective descriptions","curriculum standards (e.g., Common Core codes)","student responses (text, multiple-choice selections)","rubric criteria (if available)"],"output_types":["generated assessment items (multiple-choice, short-answer)","automated grades and scoring","progress reports (mastery %, skill gap analysis)","cohort-level analytics (class averages, distribution)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_schoolhack__cap_2","uri":"capability://automation.workflow.administrative.task.automation.and.workflow.orchestration","name":"administrative task automation and workflow orchestration","description":"Automates routine administrative workflows such as attendance tracking, grade aggregation, report generation, and schedule management by integrating with school data systems or accepting manual input. The system likely uses rule-based automation (if-then logic for attendance thresholds triggering notifications) and template-based report generation (pulling data from assessments and attendance logs into pre-formatted documents). Workflow orchestration may involve task queuing and state management to handle multi-step processes like grade finalization or parent notification.","intents":["I need to automatically generate progress reports for parents without manually compiling grades and attendance","I want to flag students with excessive absences and trigger automated notifications to parents","I need to aggregate grades from multiple assessments into a single report card"],"best_for":["Small schools with limited administrative staff","Individual teachers managing their own grade books and attendance"],"limitations":["Integration with existing school systems (SIS, gradebook software) is undocumented—may require manual data entry or API connectors not yet built","No mention of compliance with FERPA or data privacy regulations—critical gap for handling student records","Automation rules are likely rigid; customization for school-specific policies (e.g., weighted grading, attendance thresholds) may be limited","Report generation templates may not align with district-mandated formats"],"requires":["Access to student enrollment, grades, and attendance data","Configuration of automation rules (thresholds, notification recipients)","Optional: API credentials for existing school information systems"],"input_types":["attendance records","grade data (from assessments or manual entry)","student contact information","school calendar/schedule"],"output_types":["automated notifications (email, SMS to parents/guardians)","progress reports (PDF, formatted documents)","attendance summaries","grade aggregations and report cards"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_schoolhack__cap_3","uri":"capability://text.generation.language.ai.powered.content.generation.and.lesson.planning.assistance","name":"ai-powered content generation and lesson planning assistance","description":"Generates lesson plans, instructional materials, and educational content (worksheets, discussion prompts, project ideas) based on learning objectives and grade level. The system uses LLM prompting to create content variations and likely includes templates or structured prompts that guide generation toward pedagogically sound outputs. Content generation may be constrained by curriculum standards or learning frameworks to improve alignment, though this is not explicitly documented.","intents":["I need to quickly create a lesson plan for a new unit without starting from scratch","I want to generate multiple versions of a worksheet to differentiate for different ability levels","I need discussion questions and project ideas aligned to a specific learning objective"],"best_for":["Early-career teachers or those new to a subject area","Teachers with limited time for curriculum development","Educators in schools without curriculum specialists"],"limitations":["Generated content quality is highly variable—no documented review or validation process before teacher use","No curriculum alignment verification—content may not map to state standards or district scope-and-sequence","Differentiation is likely surface-level (e.g., reading level adjustments) rather than addressing diverse learning modalities or accessibility needs","Content may reflect biases present in training data; no mention of bias detection or mitigation"],"requires":["Learning objective or topic description","Grade level and subject area","Optional: curriculum standard codes (e.g., Common Core)"],"input_types":["learning objective text","grade level","subject area","content type (lesson plan, worksheet, discussion prompts)"],"output_types":["lesson plan outline","worksheets and practice materials","discussion questions","project ideas and rubrics","instructional materials (formatted text or documents)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_schoolhack__cap_4","uri":"capability://data.processing.analysis.student.learning.analytics.and.intervention.recommendation","name":"student learning analytics and intervention recommendation","description":"Analyzes aggregated student performance data to identify at-risk learners, learning gaps, and cohort-level trends, then recommends targeted interventions. The system uses descriptive analytics (performance dashboards, trend visualization) and likely simple predictive models (e.g., logistic regression or decision trees) to flag students at risk of falling behind based on assessment scores, engagement, and attendance. Intervention recommendations are rule-based (e.g., 'if mastery < 70%, recommend remedial content') rather than sophisticated causal inference.","intents":["I need to identify which students are struggling and need extra support before they fall too far behind","I want to see which concepts the whole class is struggling with so I can reteach","I need data to justify allocating intervention resources to specific students"],"best_for":["Teachers and school leaders seeking early warning systems without dedicated data analysts","Schools implementing response-to-intervention (RTI) frameworks"],"limitations":["Predictive models are likely simplistic and may not account for socioeconomic, linguistic, or other contextual factors affecting performance","Intervention recommendations are generic and not personalized to root causes of underperformance","No causal analysis—cannot distinguish between 'student didn't understand' vs. 'student didn't engage' vs. 'assessment was poorly designed'","Risk of algorithmic bias if training data reflects historical inequities in school discipline, grading, or resource allocation"],"requires":["Minimum 2-4 weeks of student performance data (assessments, engagement logs)","Attendance and demographic data (optional but improves model accuracy)","Teacher interpretation and validation of recommendations"],"input_types":["assessment scores and item-level response data","engagement metrics (time-on-task, completion rates)","attendance records","demographic data (optional)"],"output_types":["at-risk student lists with risk scores","learning gap analysis (which concepts/skills are weak)","intervention recommendations (remedial content, tutoring, parent contact)","cohort-level trend reports and visualizations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_schoolhack__cap_5","uri":"capability://text.generation.language.multi.language.content.translation.and.localization","name":"multi-language content translation and localization","description":"Translates educational content (lessons, assessments, materials) into multiple languages to support English learners (ELL) and multilingual classrooms. The system likely uses neural machine translation (NMT) APIs or models to translate text while preserving formatting, and may include post-translation review workflows for accuracy. Localization may extend beyond translation to adapt cultural references, examples, and assessment items for different linguistic and cultural contexts.","intents":["I need to provide lesson materials in Spanish for my ELL students without hiring a translator","I want to automatically translate student assessments so ELL students can demonstrate knowledge in their home language","I need to localize math word problems to use culturally relevant examples"],"best_for":["Schools with diverse ELL populations but limited translation resources","Teachers in multilingual classrooms seeking quick content adaptation"],"limitations":["Machine translation quality varies by language pair and domain; educational terminology may be mistranslated without domain-specific models","No mention of human review workflows—translated content may contain errors that affect student learning","Localization beyond translation (cultural adaptation) is likely not implemented; content may be linguistically correct but culturally inappropriate","Supported languages are undocumented—may not include less common languages spoken by school populations"],"requires":["Source content in English or another supported language","Target language selection","Optional: human review and correction by bilingual educators"],"input_types":["lesson materials (text, documents)","assessment items","instructional content"],"output_types":["translated content in target language","localized materials with cultural adaptations","translated assessments"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_schoolhack__cap_6","uri":"capability://text.generation.language.teacher.feedback.and.grading.assistance.with.ai.suggestions","name":"teacher feedback and grading assistance with ai suggestions","description":"Assists teachers in providing feedback to students by generating suggested comments, identifying common errors, and recommending grades based on rubric criteria. The system analyzes student work (text submissions, assessment responses) and uses pattern matching or LLM-based analysis to identify common mistakes, then generates constructive feedback suggestions. Teachers retain full control and can accept, edit, or reject suggestions before providing feedback to students.","intents":["I need to provide meaningful feedback to 30 student essays but don't have time to write individual comments for each","I want to identify common misconceptions in student responses so I can address them in the next lesson","I need help assigning grades fairly and consistently across student work"],"best_for":["Teachers managing large class sizes with limited time for detailed feedback","New or early-career teachers seeking guidance on constructive feedback"],"limitations":["AI-generated feedback may be generic or miss nuanced understanding of student thinking","No evidence of alignment with research on effective feedback (specificity, actionability, growth mindset framing)","Grading suggestions are only as good as the rubric provided; poorly designed rubrics produce poor suggestions","Risk of over-reliance on AI suggestions leading to reduced teacher judgment and personalization"],"requires":["Student work submissions (text, responses)","Rubric or grading criteria","Teacher review and validation before feedback is sent to students"],"input_types":["student work (essays, short answers, constructed responses)","rubric criteria or grading scale","learning objective context"],"output_types":["suggested feedback comments","identified error patterns and misconceptions","suggested grades or rubric scores","actionable improvement suggestions"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_schoolhack__cap_7","uri":"capability://automation.workflow.parent.communication.and.engagement.automation","name":"parent communication and engagement automation","description":"Automates communication with parents/guardians by generating and sending progress updates, attendance alerts, and engagement invitations based on student data. The system uses template-based message generation (filling in student-specific data into pre-written templates) and rule-based triggers (e.g., 'send progress update every 2 weeks' or 'alert parent if attendance drops below 90%'). Communication may be delivered via email, SMS, or in-app notifications.","intents":["I need to send weekly progress updates to parents without manually writing individual emails","I want to automatically alert parents when their child's attendance or grades drop","I need to invite parents to school events and engagement opportunities"],"best_for":["Schools seeking to increase parent engagement without adding administrative burden","Teachers wanting to maintain regular communication with families"],"limitations":["Template-based messages lack personalization and may feel generic or impersonal to parents","No mention of multilingual communication—messages may only be available in English, excluding non-English-speaking families","Automated alerts may trigger false positives (e.g., flagging a student as at-risk based on a single low grade) without context","No documented opt-in/opt-out mechanisms or compliance with communication preferences"],"requires":["Parent/guardian contact information (email, phone number)","Student performance and attendance data","Configuration of communication triggers and templates"],"input_types":["student grades and assessment scores","attendance records","engagement metrics","school event calendar"],"output_types":["progress update emails/messages","attendance alerts","event invitations","engagement reminders"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_schoolhack__cap_8","uri":"capability://data.processing.analysis.curriculum.mapping.and.standards.alignment.verification","name":"curriculum mapping and standards alignment verification","description":"Maps educational content (lessons, assessments, materials) to curriculum standards (Common Core, state standards, district scope-and-sequence) and identifies alignment gaps. The system likely uses keyword matching or semantic similarity (embeddings-based) to match content to standards, then flags misalignments or gaps where standards are not addressed. This enables educators to verify that their instruction covers required standards and identify where additional content is needed.","intents":["I need to verify that my lesson plans cover all required state standards for my grade level","I want to identify which standards are not yet addressed in my curriculum so I can plan accordingly","I need to document curriculum alignment for accreditation or compliance purposes"],"best_for":["Schools implementing standards-based instruction or competency-based education","Districts needing to verify curriculum alignment across schools"],"limitations":["Alignment matching is likely surface-level (keyword or semantic similarity) and may miss implicit coverage of standards","False positives/negatives are common—a lesson may be flagged as misaligned when it actually addresses a standard indirectly","Supported standards are likely limited to Common Core and major state standards; less common or district-specific standards may not be available","No integration with actual instructional practice—alignment on paper doesn't guarantee standards are taught or learned"],"requires":["Curriculum standards database (Common Core, state standards, or district standards)","Content to be mapped (lesson plans, assessments, materials)","Manual review and validation of alignment suggestions"],"input_types":["lesson plans and instructional materials","assessment items","curriculum standards (codes, descriptions)"],"output_types":["alignment mapping (content to standards)","alignment gap reports","coverage verification (which standards are addressed)","recommendations for additional content"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Student enrollment and basic demographic data","Regular student interaction with platform (assessments, content engagement)","Internet connectivity for real-time analytics processing","Learning objectives or curriculum standards defined in platform","Student enrollment with assessment history","Teacher access to review and override automated grades","Access to student enrollment, grades, and attendance data","Configuration of automation rules (thresholds, notification recipients)","Optional: API credentials for existing school information systems","Learning objective or topic description"],"failure_modes":["Underlying pedagogical model is undocumented—unclear if adaptation is based on learning science principles or simple performance metrics","No visibility into curriculum alignment—may not map to state standards or specific educational frameworks","Personalization effectiveness depends on data quality and volume; sparse interaction data may produce generic recommendations","LLM-generated questions may lack pedagogical rigor or contain subtle errors; no mention of human review workflows","Automated grading for open-ended responses is unreliable—likely limited to objective items or requires teacher validation","No documented approach to handling diverse learner needs (ELL, special education, gifted students) in assessment design","Progress tracking may conflate correlation with causation—high engagement ≠ learning","Integration with existing school systems (SIS, gradebook software) is undocumented—may require manual data entry or API connectors not yet built","No mention of compliance with FERPA or data privacy regulations—critical gap for handling student records","Automation rules are likely rigid; customization for school-specific policies (e.g., weighted grading, attendance thresholds) may be limited","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":"2026-05-24T12:16:33.095Z","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=schoolhack","compare_url":"https://unfragile.ai/compare?artifact=schoolhack"}},"signature":"gA4ahzXJBU3khcg/c5NEngs1+GuzeW2yNMpv2y24KSZi/DpejoMIaiTDgifEaB6MEMgEhCuh+ggxdC5qBJ+HAA==","signedAt":"2026-06-20T11:59:58.324Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/schoolhack","artifact":"https://unfragile.ai/schoolhack","verify":"https://unfragile.ai/api/v1/verify?slug=schoolhack","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"}}