Analyzes text to identify patterns consistent with AI-generated content from models like GPT-3, GPT-4, and similar large language models. Provides confidence scores and highlights suspicious sections within documents.
Combines AI detection with Turnitin's established plagiarism checking against billions of web pages, academic papers, and student submissions in a single workflow.
Seamlessly integrates with major learning management systems including Canvas, Blackboard, Moodle, and D2L Brightspace, embedding detection directly into existing assignment workflows.
Presents results as likelihood percentages rather than binary determinations, with visual indicators showing which sections of text may be AI-generated and confidence levels for each finding.
Includes educational materials, policy templates, and guidance for having productive conversations with students about AI writing and academic integrity.
Detects AI-generated content in multiple languages, though with varying accuracy levels depending on the language and available training data.
University instructors use the tool to review student papers submitted through their LMS. When grading assignments, they check both traditional plagiarism and AI writing indicators. This helps maintain academic standards while identifying students who may need additional support understanding proper source use and citation practices. The confidence scores allow instructors to have evidence-based conversations with students about their writing process.
College admissions offices screen application essays to ensure authenticity of student writing. With the rise of AI writing assistance, admissions committees use the detector to identify essays that may be substantially AI-generated. This helps maintain fairness in the admissions process and ensures that evaluated writing samples genuinely represent applicant abilities. Results inform holistic review processes rather than serving as automatic disqualifiers.
Journal editors and peer reviewers use iThenticate's professional version to screen submitted manuscripts. They check for both plagiarism and AI-generated content that might violate publication ethics. This is particularly important in fields where AI writing could introduce errors or bypass proper scholarly attribution. The tool helps maintain research integrity across the scholarly publishing ecosystem.
Middle and high school teachers use the detector as part of writing skill development. When students submit drafts, teachers can identify sections that may be AI-generated and use this information to provide targeted writing instruction. The tool helps educators distinguish between students struggling with writing concepts and those relying too heavily on AI assistance, allowing for differentiated support approaches.
Organizations offering professional certifications or training programs use the detector to verify that submitted work represents authentic learner effort. This is particularly important for online courses and remote assessments where supervision is limited. Companies ensure that certifications maintain value by verifying that credential holders have genuinely developed the skills being assessed rather than relying on AI to complete requirements.
Writing tutors and academic support centers use the tool to help students understand appropriate versus inappropriate use of AI writing assistance. When students bring drafts for review, tutors can identify AI-generated sections and discuss how to properly integrate research, develop original arguments, and maintain academic integrity. This educational approach focuses on skill development rather than punishment.
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15Five operates in the people analytics and employee experience space, where platforms aggregate HR and feedback data to give organizations insight into their workforce. These tools typically support engagement surveys, performance or goal tracking, and dashboards that help leaders interpret trends. They are intended to augment HR and management decisions, not to replace professional judgment or context. For specific information about 15Five's metrics, integrations, and privacy safeguards, you should refer to the vendor resources published at https://www.15five.com.
20-20 Technologies is a comprehensive interior design and space planning software platform primarily serving kitchen and bath designers, furniture retailers, and interior design professionals. The company provides specialized tools for creating detailed 3D visualizations, generating accurate quotes, managing projects, and streamlining the entire design-to-sales workflow. Their software enables designers to create photorealistic renderings, produce precise floor plans, and automatically generate material lists and pricing. The platform integrates with manufacturer catalogs, allowing users to access up-to-date product information and specifications. 20-20 Technologies focuses on bridging the gap between design creativity and practical business needs, helping professionals present compelling visual proposals while maintaining accurate costing and project management. The software is particularly strong in the kitchen and bath industry, where precision measurements and material specifications are critical. Users range from independent designers to large retail chains and manufacturing companies seeking to improve their design presentation capabilities and sales processes.
3D Generative Adversarial Network (3D-GAN) is a pioneering research project and framework for generating three-dimensional objects using Generative Adversarial Networks. Developed primarily in academia, it represents a significant advancement in unsupervised learning for 3D data synthesis. The tool learns to create volumetric 3D models from 2D image datasets, enabling the generation of novel, realistic 3D shapes such as furniture, vehicles, and basic structures without explicit 3D supervision. It is used by researchers, computer vision scientists, and developers exploring 3D content creation, synthetic data generation for robotics and autonomous systems, and advancements in geometric deep learning. The project demonstrates how adversarial training can be applied to 3D convolutional networks, producing high-quality voxel-based outputs. It serves as a foundational reference implementation for subsequent work in 3D generative AI, often cited in papers exploring 3D shape completion, single-view reconstruction, and neural scene representation. While not a commercial product with a polished UI, it provides code and models for the research community to build upon.
