{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_advacheck","slug":"advacheck","name":"Advacheck","type":"product","url":"https://advacheck.com","page_url":"https://unfragile.ai/advacheck","categories":["documentation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_advacheck__cap_0","uri":"capability://data.processing.analysis.document.level.plagiarism.detection.with.source.matching","name":"document-level plagiarism detection with source matching","description":"Analyzes submitted student documents against a multi-source database (academic papers, web content, student submission history) using fingerprinting and similarity algorithms to identify potential plagiarism. The system generates a similarity percentage score and highlights matched passages with source attribution, enabling educators to distinguish between properly cited material and unattributed copying. Detection operates on uploaded documents (PDF, DOCX, TXT) and processes them through a cloud-based comparison engine that maintains institutional submission archives.","intents":["I need to check if a student's essay contains plagiarized content before grading","I want to identify which sources a student copied from without proper attribution","I need to generate a report showing similarity scores and matched passages for academic integrity review"],"best_for":["educators managing 50-500 student submissions per semester","mid-sized institutions with dedicated academic integrity policies","instructors seeking per-assignment plagiarism screening without institutional licensing overhead"],"limitations":["Detection accuracy depends on source database coverage — may miss plagiarism from paywalled journals or proprietary databases not indexed","Similarity scores can produce false positives for common phrases, standard citations, or legitimate paraphrasing without proper tuning","Processing latency ranges 30-120 seconds per document depending on file size and queue load","Limited to text-based documents; cannot detect plagiarism in images, code, or multimedia content"],"requires":["Student document in PDF, DOCX, or TXT format (max file size typically 25-50MB)","Active Advacheck account with institutional or individual subscription","Internet connectivity for cloud-based processing"],"input_types":["text documents (PDF, DOCX, TXT, RTF)","document metadata (student name, assignment ID, submission date)"],"output_types":["similarity percentage score (0-100%)","originality report with highlighted matched passages","source attribution list with URLs and citation details","structured JSON or PDF report export"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_advacheck__cap_1","uri":"capability://tool.use.integration.learning.management.system.integration.with.native.submission.workflows","name":"learning management system integration with native submission workflows","description":"Embeds plagiarism detection directly into Canvas, Blackboard, and Moodle assignment submission pipelines through LMS-native plugins or API integrations. When students submit assignments through their institution's LMS, documents are automatically routed to Advacheck for analysis, and originality reports are returned and displayed within the LMS gradebook interface without requiring educators to manually upload files or switch between platforms. Integration uses OAuth 2.0 authentication and LMS-specific APIs (Canvas REST API, Blackboard Learn API, Moodle Web Services) to synchronize user rosters, assignment metadata, and submission status.","intents":["I want plagiarism checking to happen automatically when students submit assignments in Canvas/Blackboard/Moodle without extra steps","I need originality reports to appear directly in my LMS gradebook so I can review them alongside student work","I want to configure plagiarism detection settings (sensitivity, exclusions) within my existing LMS interface"],"best_for":["institutions already using Canvas, Blackboard, or Moodle as primary LMS","educators seeking zero-friction integration without manual file management","IT administrators managing institution-wide academic integrity policies across multiple courses"],"limitations":["Integration requires LMS administrator setup and OAuth credential configuration — not available for self-service by individual instructors in some LMS versions","Submission routing latency adds 5-15 seconds to assignment submission workflow due to API calls and document transfer","Limited customization of report display within LMS — some LMS platforms restrict plugin UI capabilities","Requires compatible LMS version (Canvas 2020+, Blackboard Learn 9.1+, Moodle 3.9+); older institutional deployments may not support integration"],"requires":["Active Advacheck institutional account with LMS integration license","LMS administrator credentials to install and configure plugin/API integration","Canvas 2020+, Blackboard Learn 9.1+, or Moodle 3.9+ (or equivalent)","OAuth 2.0 support enabled in institutional LMS instance"],"input_types":["student assignment submissions (PDF, DOCX, TXT) via LMS","assignment metadata (due date, course ID, assignment name)","user roster data (student names, IDs, email addresses)"],"output_types":["originality report embedded in LMS gradebook","similarity score displayed in assignment submission view","plagiarism detection status (pending, complete, flagged) in LMS workflow"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_advacheck__cap_2","uri":"capability://data.processing.analysis.originality.report.generation.with.detailed.source.attribution","name":"originality report generation with detailed source attribution","description":"Generates comprehensive originality reports that display similarity percentages, matched passages highlighted in context, and detailed source attribution including URLs, publication dates, and citation formats. Reports use color-coded highlighting (typically green for original content, yellow/orange for paraphrased matches, red for direct copies) and provide side-by-side comparison views showing student text alongside matched source material. Reports can be exported as PDF or viewed interactively within the platform, with options to exclude common phrases, citations, and quoted material from similarity calculations.","intents":["I need to see exactly which parts of a student's work match external sources and what those sources are","I want to generate a professional report to share with students explaining why their submission was flagged","I need to distinguish between properly cited material and plagiarism by reviewing matched sources in context"],"best_for":["educators conducting academic integrity investigations","institutions documenting plagiarism cases for disciplinary proceedings","instructors providing feedback to students on citation and paraphrasing practices"],"limitations":["Report generation adds 15-45 seconds processing time for complex documents with many matched sources","Source database coverage varies by region and subject area — some specialized academic journals or paywalled content may not be indexed","Exclusion filters (citations, common phrases) require manual configuration per assignment and may miss context-dependent plagiarism","PDF export formatting may not preserve interactive highlighting and comparison features available in web view"],"requires":["Completed plagiarism detection scan on student document","Advacheck account with report generation permissions","PDF export requires browser with PDF generation support (Chrome, Firefox, Safari)"],"input_types":["plagiarism detection results (similarity data, matched passages, source metadata)"],"output_types":["interactive HTML originality report with color-coded highlighting","PDF export of originality report","JSON structured data with similarity scores and source attribution","CSV export of matched sources with URLs and similarity percentages"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_advacheck__cap_3","uri":"capability://data.processing.analysis.institutional.submission.history.archiving.and.internal.plagiarism.detection","name":"institutional submission history archiving and internal plagiarism detection","description":"Maintains a searchable archive of all student submissions within an institution, indexed by course, semester, and student ID. When new documents are submitted, the system compares them against this institutional archive to detect internal plagiarism (students submitting identical or near-identical work across different courses or semesters) and collusion (multiple students submitting highly similar work in the same assignment). Archive indexing uses document fingerprinting and semantic similarity algorithms to identify matches even when text is paraphrased or reformatted. Institutions can configure retention policies (e.g., keep submissions for 3-5 years) and control which submissions are included in the archive.","intents":["I want to detect if a student submitted the same essay in multiple courses without rewriting it","I need to identify if multiple students in my class submitted nearly identical work suggesting collusion","I want to check if current submissions match work from previous semesters in my institution's archive"],"best_for":["institutions with multi-year submission histories and large student populations","departments managing core courses with high enrollment and recurring assignments","academic integrity offices investigating systematic plagiarism patterns across courses"],"limitations":["Archive search latency increases with institutional submission volume — institutions with 100k+ archived documents may experience 30-60 second query times","Semantic similarity detection for paraphrased internal plagiarism has lower precision than exact-match detection, producing false positives in legitimate cases where students reuse similar research","Archive retention policies may conflict with student privacy regulations (FERPA, GDPR) requiring careful configuration and data deletion workflows","Institutional archive is siloed per institution — cannot detect plagiarism across different schools or universities"],"requires":["Institutional Advacheck account with archive feature enabled","Minimum 6-12 months of prior submissions to build effective archive index","Archive retention policy configured by institution administrator","Student consent or institutional policy allowing submission archiving"],"input_types":["historical student submissions (PDF, DOCX, TXT)","submission metadata (student ID, course ID, semester, assignment name)"],"output_types":["internal plagiarism match list with similarity percentages","collusion detection report showing groups of similar submissions","archive search results with matched submissions and student identifiers","timeline view showing submission patterns across semesters"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_advacheck__cap_4","uri":"capability://automation.workflow.batch.document.processing.with.bulk.submission.handling","name":"batch document processing with bulk submission handling","description":"Processes multiple student submissions in a single batch operation, queuing documents for plagiarism detection and generating reports for entire assignment cohorts without requiring individual file uploads. Batch processing accepts CSV manifests with document file paths or direct folder uploads containing multiple student submissions, automatically assigns submissions to students based on filename patterns or metadata, and generates consolidated reports showing similarity scores for all submissions in a single view. The system manages queue prioritization, handles processing failures with retry logic, and provides progress tracking and completion notifications via email or webhook.","intents":["I need to check plagiarism for 200 student submissions from a single assignment all at once","I want to upload an entire folder of student work and get back a summary report with all similarity scores","I need to process submissions from multiple courses in one batch and get results organized by course"],"best_for":["educators managing large enrollment courses (100+ students)","academic integrity offices processing institution-wide plagiarism screening","administrators automating end-of-semester plagiarism checks across multiple courses"],"limitations":["Batch processing queue may have 2-8 hour latency during peak periods (end of semester) depending on system load","Filename pattern matching for student assignment requires consistent naming conventions — inconsistent filenames may result in incorrect student assignment","Batch processing does not support real-time progress updates — educators must wait for batch completion or poll API for status","Maximum batch size typically 500-1000 documents per submission; larger batches require splitting into multiple requests"],"requires":["Advacheck account with batch processing feature enabled","CSV manifest file with document paths and student metadata, OR folder containing properly named student submission files","File naming convention documented and consistent (e.g., StudentID_AssignmentName.pdf)","API key for programmatic batch submission (if using API rather than web interface)"],"input_types":["CSV manifest with student ID, document path, assignment ID, course ID","folder containing multiple student submission files (PDF, DOCX, TXT)","batch metadata (course name, assignment name, due date, instructor email)"],"output_types":["consolidated batch report with similarity scores for all submissions","per-student originality reports (PDF or HTML)","CSV export with student ID, similarity percentage, flagged status","email notification with batch completion status and summary statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_advacheck__cap_5","uri":"capability://automation.workflow.academic.integrity.policy.configuration.and.enforcement.rules","name":"academic integrity policy configuration and enforcement rules","description":"Allows institutional administrators to define custom academic integrity policies specifying similarity thresholds, exclusion rules, and automated actions triggered by plagiarism detection results. Policies can be configured per course, department, or institution-wide, with rules such as 'flag submissions with >25% similarity for manual review', 'automatically exclude citations and quoted material from similarity calculations', 'notify instructor when similarity exceeds threshold', or 'require student review of originality report before grade posting'. The system enforces these policies consistently across all submissions and provides audit logs documenting which policy rules were applied to each detection result.","intents":["I want to set a similarity threshold (e.g., 20%) above which submissions are automatically flagged for review","I need to configure different plagiarism policies for different courses (stricter for upper-level courses, more lenient for introductory courses)","I want to automatically notify students when their submission is flagged so they can review the originality report"],"best_for":["institutional academic integrity offices establishing consistent plagiarism policies","department chairs managing course-specific integrity standards","administrators automating plagiarism response workflows to reduce manual review burden"],"limitations":["Policy configuration requires institutional administrator access — individual instructors cannot override institution-wide policies","Automated actions (notifications, grade holds) depend on LMS integration; some LMS platforms have limited automation capabilities","Threshold-based rules can produce false positives if not tuned carefully — overly strict thresholds may flag legitimate work with proper citations","Audit logging adds ~5-10% overhead to plagiarism detection processing time"],"requires":["Advacheck institutional account with policy configuration permissions","Administrator role in Advacheck platform","LMS integration enabled if using automated actions (notifications, grade holds)"],"input_types":["policy configuration parameters (similarity thresholds, exclusion rules, automated actions)","course or department identifiers for policy scoping","notification templates and recipient lists"],"output_types":["policy enforcement audit log with timestamp, submission ID, policy rule applied, action taken","policy configuration export (JSON or YAML)","compliance report showing policy adherence across institution"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_advacheck__cap_6","uri":"capability://text.generation.language.student.facing.originality.report.review.and.feedback.interface","name":"student-facing originality report review and feedback interface","description":"Provides students with an interactive interface to review their originality reports, understand plagiarism detection results, and access educational resources on proper citation and paraphrasing. The student-facing report displays similarity scores, highlights matched passages, and explains why content was flagged, with options to view matched sources and understand the difference between proper citation and plagiarism. The interface includes embedded tutorials on citation formats (APA, MLA, Chicago), paraphrasing techniques, and academic integrity standards, enabling students to learn from plagiarism detection results rather than viewing them as purely punitive. Instructors can optionally require students to review their report and acknowledge understanding before grade posting.","intents":["I want students to understand why their work was flagged and learn how to properly cite sources","I need to provide educational feedback on plagiarism without being purely punitive","I want to require students to review their originality report and confirm they understand the issues before I post grades"],"best_for":["educators using plagiarism detection as a teaching tool rather than purely for enforcement","institutions with strong academic integrity education programs","first-year writing programs and general education courses where citation skills are being developed"],"limitations":["Student interface requires separate authentication and access control — not all students may have Advacheck accounts if using LMS integration","Educational resources are generic and may not align with institution-specific citation standards or writing center guidance","Student acknowledgment of report review is not legally binding and does not prevent plagiarism disputes","Interface customization is limited — institutions cannot easily add custom tutorials or institutional-specific guidance"],"requires":["Student account or LMS single sign-on access to Advacheck platform","Completed originality report on student submission","Instructor configuration enabling student report access (some institutions may restrict student access)"],"input_types":["originality report data (similarity scores, matched passages, source attribution)","student identifier and course enrollment"],"output_types":["interactive student-facing originality report with highlighting and source links","embedded citation tutorials and paraphrasing guides","student acknowledgment record (timestamp, confirmation that student reviewed report)","feedback form for students to dispute plagiarism detection results"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_advacheck__cap_7","uri":"capability://data.processing.analysis.plagiarism.detection.result.analytics.and.institutional.reporting","name":"plagiarism detection result analytics and institutional reporting","description":"Aggregates plagiarism detection results across courses, departments, and semesters to provide institutional-level analytics on academic integrity trends. Analytics dashboards display metrics such as average similarity scores by course, percentage of submissions flagged above institutional threshold, plagiarism rate trends over time, and identification of high-risk courses or departments with elevated plagiarism rates. Reports can be filtered by course, instructor, student cohort, or time period, and exported as CSV or PDF for institutional review. The system also provides comparative analytics showing how institutional plagiarism rates compare to anonymized benchmarks from similar institutions.","intents":["I need to understand plagiarism trends across my institution to identify courses or departments needing additional academic integrity support","I want to see if plagiarism rates are increasing or decreasing over time to assess effectiveness of integrity initiatives","I need to report plagiarism statistics to accreditation bodies or institutional leadership"],"best_for":["institutional academic integrity offices conducting program assessment","academic deans and provosts monitoring institutional academic integrity health","accreditation and compliance teams documenting institutional integrity metrics"],"limitations":["Analytics are based on submissions processed through Advacheck — do not include plagiarism detected through other methods or undetected plagiarism","Comparative benchmarks are anonymized and may not be directly comparable due to differences in institutional policies, student populations, and submission types","Trend analysis requires 12+ months of data to identify meaningful patterns — new institutions may not have sufficient historical data","Privacy regulations (FERPA, GDPR) may restrict ability to drill down to individual student level in reports"],"requires":["Institutional Advacheck account with analytics permissions","Minimum 6-12 months of plagiarism detection data for meaningful trend analysis","Administrator role to access institution-wide analytics"],"input_types":["plagiarism detection results (similarity scores, flagged submissions, timestamps)","course and enrollment metadata (course ID, department, semester, enrollment size)","institutional policy configuration (similarity thresholds, exclusion rules)"],"output_types":["analytics dashboard with charts and metrics (average similarity, flagged percentage, trends)","CSV export of plagiarism statistics by course/department/semester","PDF institutional report with summary statistics and comparative benchmarks","JSON API for programmatic access to analytics data"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_advacheck__cap_8","uri":"capability://data.processing.analysis.multi.language.document.plagiarism.detection","name":"multi-language document plagiarism detection","description":"Detects plagiarism in student submissions written in multiple languages (English, Spanish, French, German, Russian, Chinese, Japanese, etc.) by applying language-specific tokenization, stemming, and similarity algorithms. The system automatically detects document language and applies appropriate linguistic processing before comparing against multilingual source databases. Cross-language plagiarism detection is also supported, identifying cases where students translate plagiarized content from one language to another. Language detection uses statistical models trained on multilingual text corpora, and similarity matching accounts for language-specific variations in phrasing and grammar.","intents":["I need to check plagiarism in student submissions written in Spanish, French, or other non-English languages","I want to detect if a student translated plagiarized content from another language to hide the plagiarism","I need to support international students submitting work in their native languages"],"best_for":["institutions with international student populations","multilingual universities and language departments","courses taught in non-English languages"],"limitations":["Language detection accuracy is 95-98% for major languages but lower for minority languages or code-mixed text","Source database coverage varies significantly by language — English, Spanish, and French have extensive coverage, while less common languages may have limited indexed sources","Cross-language plagiarism detection has lower precision than single-language detection due to translation variability","Processing latency increases 20-30% for non-English documents due to additional linguistic processing"],"requires":["Advacheck account with multilingual detection enabled","Student submission in supported language (English, Spanish, French, German, Russian, Chinese, Japanese, Arabic, Portuguese, Italian, Dutch, Polish, Turkish, Korean, Thai, Vietnamese)"],"input_types":["text documents in multiple languages (PDF, DOCX, TXT)","optional language specification (if automatic detection is unreliable)"],"output_types":["similarity score with language-specific matching","originality report with language-appropriate highlighting and source attribution","language detection confidence score","cross-language plagiarism matches (if detected)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Student document in PDF, DOCX, or TXT format (max file size typically 25-50MB)","Active Advacheck account with institutional or individual subscription","Internet connectivity for cloud-based processing","Active Advacheck institutional account with LMS integration license","LMS administrator credentials to install and configure plugin/API integration","Canvas 2020+, Blackboard Learn 9.1+, or Moodle 3.9+ (or equivalent)","OAuth 2.0 support enabled in institutional LMS instance","Completed plagiarism detection scan on student document","Advacheck account with report generation permissions","PDF export requires browser with PDF generation support (Chrome, Firefox, Safari)"],"failure_modes":["Detection accuracy depends on source database coverage — may miss plagiarism from paywalled journals or proprietary databases not indexed","Similarity scores can produce false positives for common phrases, standard citations, or legitimate paraphrasing without proper tuning","Processing latency ranges 30-120 seconds per document depending on file size and queue load","Limited to text-based documents; 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