PROBLEM STATEMENT PARTNERS

ROUND 2 - PROBLEM STATEMENTS

PS Title Description
ID
R2-01 Secure Employees in modern workplaces increasingly access
Collaborative enterprise systems through personal devices under BYOD
Workspaces in the (Bring Your Own Device) policies, creating serious security
Age of BYOD and privacy challenges. Propose a cybersecurity solution that
enables secure, auditable access to organizational resources
from unmanaged devices while preserving employee privacy.
The system should follow zero-trust principles, support
granular access control, and ensure real-time monitoring
without compromising usability or compliance.

R2-02 Secure
Cross-Device As remote and hybrid work environments become standard,
Clipboard for professionals regularly switch between devices—copying
Remote Teams sensitive data such as passwords, code snippets, and
confidential notes. Existing clipboard syncing tools offer
convenience but fall short on security, visibility, and
organizational control. Your challenge is to develop a secure,
cross-device clipboard synchronization system tailored for
distributed teams. The solution must include end-to-end
encryption, domain-specific access controls to block data from
being pasted into untrusted apps or websites, and a
tamper-proof log of all transfers. Prioritize compliance,
privacy, and usability to meet the needs of developers,
regulated industries, and security-focused organizations.
R2-03 Cross-Border Patients often face difficulties accessing their medical records
Medical Records while traveling internationally due to fragmented systems and
via Interoperable privacy regulations. Propose a blockchain-based solution that
Blockchains enables secure, cross-border sharing of medical data with
role-based access control, patient-managed consent, and
interoperability across multiple blockchain networks like
IPFS, Hyperledger, and Ethereum. The solution should
prioritize privacy, compliance, and real-world usability in
global healthcare scenarios.

R2-04 AI Companion
Quest – Smart Learners on video-based platforms often face boredom,
Gamified distraction, and a lack of emotional connection, leading to
Learning Buddy poor engagement and retention. Traditional platforms provide
static experiences that don’t adapt to individual learning
styles, pace, or mood. Your challenge is to build an
AI-powered learning companion that personalizes the
experience by responding to user emotions, interaction
patterns, and progress. The system should include gamified
features like streaks, quests, and point boosters; adjust its tone
and personality based on emotional cues; and recommend
relevant hands-on or DIY activities post-lessons to reinforce
learning and maintain engagement.

R2-05
DIY Mission Students often feel motivated to build something after learning
Engine – a new concept but struggle to decide what to create or where
Auto-Generate to start, leading to lost opportunities for hands-on exploration.
Build Tasks with Your challenge is to develop an AI-powered DIY project
AI generator that takes input such as a concept name or video
lecture transcript and generates personalized build tasks. The
system should adapt task complexity to the learner’s
background, suggest project types across domains like coding,
hardware, design, or research, and offer templates or guided
hints using large language models to help learners begin with
confidence.
R2-06
AI Gamifier – Static course content often fails to engage college students,
Convert Courses especially when compared to the immersive, rewarding nature
into Interactive of games. Most educational platforms lack built-in game
Game-Based design logic that could enhance motivation and retention.
Modules Your challenge is to build an AI-powered gamification engine
that transforms traditional video lessons or text modules into
interactive learning experiences. The system should generate
mission-style learning paths, level-up systems tied to learning
outcomes, and game-like interfaces featuring rewards,
progress tracking, and achievement mechanics to make
studying feel more like playing.

R2-07 Sentiment-Aware
AI Coach – In large-scale online courses, instructors can't provide
Real-Time real-time support, leaving many students feeling confused,
Feedback Based frustrated, or disengaged—limiting the impact of self-paced
on Emotion & digital learning. Your challenge is to build an AI-powered
Confusion feedback coach that detects emotional signals like confusion,
Detection boredom, or frustration using webcam, audio, and interaction
data (e.g., mouse movement, inactivity). Based on these cues,
the system should suggest timely resources such as videos,
mini-games, chatbots, or breaks. It should also track
emotional well-being alongside cognitive progress to create a
more personalized and responsive learning environment.

R2-08 Immersive
Pollution Despite worsening pollution in Indian cities, there's a
Exposure disconnect between environmental data, public awareness, and
Simulator for effective policy-making. Citizens lack clear insight into how
Policy Impact & pollution impacts their daily lives, while policymakers
Public Awareness struggle to act on complex data. Your challenge is to develop
an AR/VR-based interactive platform that visualizes real-time
exposure to air, water, and noise pollution. The system should
integrate real or simulated datasets, use AI to simulate
long-term human impacts (e.g., health risks from PM2.5 or
waterborne diseases), and enable “What if” urban planning
scenarios. It should feature tailored modes for citizens,
schools, and policymakers, using goal-based storytelling and
decision-driven simulations to drive education, engagement,
and action.
 R2-09 Mixed-Reality
Urban Heat Island Urban heat islands (UHIs), driven by concrete buildup,
Visualizer for deforestation, and poor urban design, raise local temperatures,
Sustainable Urban increase health risks, and strain energy resources—yet their
Planning effects remain largely invisible to citizens and planners. Your
challenge is to build an AR-based spatial visualizer that
overlays UHI data—such as temperature gradients, green
cover loss, and surface reflectivity—onto real-world
cityscapes using mobile AR or mixed-reality devices like
HoloLens. The system should pull from real-time IoT or
satellite data, simulate interventions like green roofs or
reflective pavements, and support on-site use by planners,
architects, and citizens. Optionally, include AI-driven
suggestions for cool-surface retrofits and green buffer
strategies to help mitigate UHI effects.

*Equipments will not be provided