AI + Human-Centered Detection |
Combines AI insights with contextual academic patterns |
Primarily relies on AI-generated flags and scores |
Designed for Learning Institutions |
Built with student support, feedback loops, and remediation |
Focuses on detection and reporting only |
Adaptive AI Analysis |
Learns institution specific writing and subject patterns |
Uses a static/general model across all institutions |
Authorship Integrity |
Compares writing style across submissions to detect authorship inconsistency |
No authorship consistency checks; only content originality and AI detection |
Custom Policy Enforcement |
Institutions can define custom plagiarism and AI usage rules |
Limited customization; follows universal detection |
Instructor Feedback Integration |
Allows instructors to review and annotate AI/plagiarism results |
Feedback tools are minimal or unavailable |
Insight Dashboard & Analytics |
Offers detailed dashboards on writing trends and misconduct |
Offers basic analytics or limited reporting |
Ethical AI Transparency |
Clearly states how content is evaluated and scored |
Often uses black-box AI models |
Real-Time Learning Support |
Students receive educational nudges before submission |
No real-time feedback; post-submission only |
Multilingual & Context-Aware |
Detects AI use in multiple languages & disciplines |
Primarily English-focused; lacks cultural nuance |
Assessment Integration |
Supports pre-, mid-, and post-evaluation tracking |
Used only for end-product evaluations |
Platform Integration |
Fully integrates with HonestIQ’s academic ecosystem |
Integrations limited to LMS plugins or uploads |
Revision & Remediation Support |
Encourages resubmission and learning from feedback |
Penalizes flagged work; minimal learning options |
AI vs. Plagiarism Differentiation |
Distinguishes between AI assistance and true plagiarism |
Often conflates AI use with misconduct |