Sesnei is a scalable AI-assisted learning platform tailored for CUET aspirants in India, empowering both students and educators to save time and focus on delivering impact in a modern, tech-enabled classroom environment.
Students, teachers, and mentors preparing for CUET faced fragmented tools, limited feedback mechanisms, and non-intuitive workflows. Teachers especially struggled with time-consuming grading, content generation, and delivering personalized feedback at scale.
Context
How might we create a unified, accessible platform that offers personalized remediation and real-time performance tracking for students, while reducing the operational burden on teachers?
3 major gaps
Time-intensive tasks:
Teachers spent hours grading and creating content
Individualized feedback
Difficult to scale for large classrooms
Complex flows
Non-digital journeys created friction in lesson planning and worksheet generation
My Approach
A methodology focused on building from first principles - unburdened by legacy assumptions.
This enabled us to challenge old mental models, craft tailored AI workflows, and ensure that the solution aligned with real-world classroom realities.
Strategic Ideation
Collaborated with PMs, engineers, and stakeholders to create understanding of current gaps
Benchmarking with two platforms
Compared two leading products in market with their AI-assisted workflows and UI layouts to ensure intuitive navigation
01
Enhanced user flows
Focused on cross-device usability to ensure accessibility in low-resource settings
02
Hueristic Evaluation
Heuristic evaluation of live version revealed critical usability gaps: poor error prevention, inconsistent IA, and cognitive overload—requiring immediate UX optimization
03
Low Fidelity wireframes with use cases
Creating personalized worksheets and lesson plans using AI. Responsive flows crafted for critical moments (e.g., feedback generation, content creation)
03
End Deliverables
I conducted design walkthorughs’ with PMs post wireframes to validate design solution
Crafted high-fidelity designs that brought the AI-powered assistant to life
Impact & Learning Outcomes
Designing AI-assisted workflows requires understanding both tech limitations and user context
I should design appropriate fallback strategies with microcopy to keep users informed &reduce frustration
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