Teach faster. Assess smarter.Research with AI.
Intangible gives professors a single place to manage classes, generate assessments, access shared resources, and collaborate on research—powered by AI.
- Classroom + assessments in one flow
- AI to create quizzes, rubrics, and learning material
- Research collaboration + publication support
Professors are expected to move at industry speed—without industry tools.
Content creation, quizzes, grading, and tracking takes too much time
Resources are scattered: drives, PDFs, WhatsApp, old notes
Updating curriculum to match new tools/roles is hard
Research moves slowly when collaboration and workflows are unstructured
High responsibility. Low infrastructure.
Scattered Workflow
PDFs
Sheets
WhatsApp Groups
Emails
One workspace. Four superpowers.
Classroom Management
- Manage classes, announcements, attendance-style tracking (if needed)
- Share material in one place
- Keep everything organised semester-wise
AI for Quizzes, Assessments & Rubrics
- Generate quizzes, assignments, case prompts
- Create rubrics + evaluation criteria in minutes
- Build assessments mapped to skills (not just memory)
One Resource Hub (All Subjects)
- Single access point for notes, case studies, references, past papers
- Department-wise and subject-wise resource organisation
- Easy sharing across professors and batches
Research Collaboration + Requests
- Discover professors working on similar themes
- Send collaboration requests + form research groups
- Track research progress and tasks inside the platform
C
Class Management
Q
Quiz Generator
R
Resource Library
🔬
Research Collab
Research with AI support—without compromising academic rigor.
Focus on your insights while we handle the heavy lifting of discovery, formatting, and organization.
Literature discovery + quick summaries (with citations)
Help structuring papers: outline → draft → refinement
Submission readiness: checklists, formatting, references
Track versions + collaborate with co-authors
AI
Research AI Workspace
Query
"Machine learning applications in educational assessment"
Summary
Machine learning has shown significant potential in transforming educational assessment through adaptive testing, automated grading, and personalized feedback systems. Key applications include predictive analytics for student performance and intelligent tutoring systems.
1. Smith, J. (2023). "ML in Education" - Journal of EdTech
2. Chen, L. (2022). "Adaptive Assessment Systems" - AI Review
3. Kumar, R. (2023). "Personalized Learning" - Ed Research Quarterly
When professors get better tools, students and colleges win too.
Better learning experience (more relevant, structured assessments)
Faster feedback loops for students
Stronger academic reputation through research output
Colleges become more "execution-ready" with consistent systems
👨🏫
Professors
- Better tools
- Faster workflows
- More research output
→
🎓
Students
- Better learning
- Faster feedback
- Structured assessments
→
🏛️
College Outcomes
- Stronger reputation
- Execution-ready
- Consistent systems