Background

Jharkhand's PALASH Mother Tongue-Based Multilingual Education (MTB-MLE) programme has demonstrated measurable improvements in foundational literacy among tribal children. However,scaling the programme is severely bottlenecked by a shortage of teachers proficient in tribal languages including Ho, Mundari, and Santhali languages with limited digital NLP resources. The vast majority of teachers assigned to tribal-area primary schools are Hindi-medium trained and lack the linguistic tools to deliver mother-tongue-based instruction. Without a technology bridge, the pedagogical intent of MTB-MLE cannot be realised at scale, and children in over 5,000 tribal-area primary schools continue to receive instruction in a language they do not comprehend at home.

Description

Develop an Al-assisted translation and curriculum-generation software suite that enables non-nativespeaking primary school teachers to deliver mother-tongue-based instruction in Ho, Mundari, and Santhali without prior language training. The system must include an NLP engine capable of translating standard Hindi Foundational Literacy and Numeracy (FLN) curriculum content- including lesson scripts, activity instructions, and assessment prompts-into contextually accurate text and synthesised audio in target tribal languages. A real-time voice-to-voice translation feature must allow a teacher speaking Hindi to conduct interactive classroom dialogue with tribal-language-speaking students, with latency not exceeding three seconds. The system must auto-generate bilingual worksheets and visual flashcard sets aligned to the NIPUN Bharat learning outcomes framework.Given that most schools in the target deployment areas lack reliable internet, the entire application must function offline on low-cost tablets (₹2 GB RAM, Android 9+) after initial content synchronisation.

Expected Solution

A working software application demonstrating Hindi-to-tribal-language translation (minimum one tribal language at prototype stage), real-time voice translation with sub-3-second latency, autogenerated bilingual worksheet output, and full offline operation on a low-end Android tablet submitted with a demo video and GitHub repository.

Develop an AI/ML-enabled decision support platform capable of forecasting Antarctic sea-ice concentration, predicting iceberg trajectories, and identifying safe and fuel-efficient navigation routes for research vessels using satellite,oceanographic and meteorological datasets.

To develop an Artificial Intelligence (AI) / Machine Learning (ML) based system for identification, classification, and prediction of different tropical cyclone patterns using multi-source satellite data.

The solution should include secure signup and login functionality with three user roles: Trainee, Trainer, and Admin. Trainees should be able to create professional profiles with qualifications, work experience, interests, skills, and certificates, enroll in courses, access learning resources, attempt subject-wise MCQ assessments, and provide feedback on courses and training content.Trainers should be able to manage their profiles, create questionnaires with deadlines, monitor trainee participation and performance, and upload recorded lectures, presentations, and study materials in a trainer library accessible to trainees.The Admin module should provide user approval and role management

features along with dashboards for monitoring courses, enrollments, certifications,assessments, and participation statistics. Admins should also be able to publish notifications, announcements, achievements, and newly added learning content on the homepage.The platform should support competency mapping for identifying suitable trainers for various subjects and should be scalable, secure, user-friendly, and accessible across devices to promote efficient learning and organizational capacity building.

Background

The Pradhan Mantri Anusuchit Jaati Abhyuday Yojana (PM-AJAY) aims to reduce poverty among Scheduled Caste (SC) communities through livelihood promotion, skill development, and enterprise support under its Grant-in-Aid (GIA) component.

A major challenge in implementation is the identification of appropriate skill training pathways that align with both the aspirations of beneficiaries and the actual livelihood opportunities available in their local regions.

Many target beneficiaries face barriers such as low digital literacy, limited awareness of modern trades, language constraints, and difficulty navigating text-heavy digital systems.

As a result, there is often a mismatch between enrolled training programs and the beneficiary’s interests, capabilities, or local market demand, leading to high dropout rates and poor post-training employment outcomes.

To improve inclusion and effectiveness, there is a need for an AI-enabled conversational system that can interact naturally in regional languages and dialects, understand beneficiary aspirations, assess skill gaps, and recommend suitable NSQF aligned livelihood opportunities in and around the beneficiary.

Basic Issues under GIA Component

Detailed Description

The proposed solution should be an AI-driven, multilingual, voice-based virtual livelihood assistant capable of conducting conversational interviews with beneficiaries from aspirational SC communities.

Instead of relying on traditional form-filling methods, the system should use voice interactions to collect information such as: Educational background Existing or traditional family occupations Current livelihood activities Skills and interests Mobility and physical constraints Preference for self-employment or wage employment Local economic realities and opportunities The assistant should support regional languages and dialects to ensure accessibility for users with low literacy or limited digital exposure.

The interaction should feel empathetic and conversational rather than administrative.The collected information should be analyzed using AI/MLbased profiling and recommendation mechanisms to identify: Suitable NSQF-aligned training programs Relevant trades and livelihood pathways Skill gaps requiring intervention Region-specific employment or enterprise opportunities The system should also function effectively in lowconnectivity and low-tech environments through deployment channels such as: IVR-based phone calls for feature phone users WhatsApp voice-note interfaces Lightweight mobile or kiosk-based solutions.

Expected Solution

An AI-powered multilingual voice assistant application designed to help SC beneficiaries under PM-AJAY identify suitable skill training and livelihood opportunities.The app will support regional languages and local dialects, allowing users to interact through simple voice conversations instead of text-based forms.

Background

India's statistical system is undergoing rapid technology advancement with increasing adoption of Artificial Intelligence (AI), Machine Learning (ML) , Big Data Analytics, GIS, cloud computing, and modern statistical methodologies.

Officials engaged in data collection, processing, analysis, dissemination, and policy support require continuous upskilling to meet evolving technological and domain-specific requirements.

While the iGOT Karmayogi platform offers a vast repository of learning resources, officials often face challenges in identifying the most relevant courses aligned with their job roles, current competencies, and future skill requirements.

Presently, there is no intelligent mechanism that performs comprehensive skill-gap assessment and recommends personalized learning pathways specifically for professionals working in Official Statistics.

Artificial Intelligence (AI) and emerging digital technologies are rapidly transforming the way organizations operate, deliver services, and make decisions.

However, many organizations face challenges such as limited AI awareness, skill gaps, inadequate technical expertise, and the absence of structured, scalable training mechanisms.

Traditional training approaches often lack personalization, continuous assessment, and real-time learner support, making it difficult to meet diverse learning needs.

An AI enabled Learning Management System can assess learners existing competencies through learner’s profile, identify skill gaps, and recommend personalized training through integration with the iGOT Karmayogi platform based on job roles, experience levels, and organizational requirements.

Through adaptive learning modules, AI powered virtual assistants, automated assessments, and realtime feedback mechanisms, learners receive targeted training that improves engagement and learning outcomes.

The LMS supports continuous capacity building by offering structured courses, hands-on exercises, virtual labs on emerging technologies such as Artificial Intelligence, Data Science, Cloud Computing, Cybersecurity, and Automation.

AI driven analytics and dashboards enable organizations to monitor learner progress, evaluate training effectiveness, predict future skill requirements, and make informed decisions regarding workforce development.

By integrating personalized learning, competency mapping, performance monitoring, and intelligent content delivery, the AI enabled LMS ensures effective adoption of emerging technologies and helps create a future-ready, digitally skilled workforce.

Detailed Description

The proposed solution aims to develop an AI-enabled Skill Intelligence and Learning Platform that strengthens capacity building for officials engaged in India's Official Statistical System by integrating with the iGOT Karmayogi ecosystem.

The platform should leverage Artificial Intelligence to assess competencies, identify skill gaps, and recommend personalized learning pathways aligned with each official's job role, responsibilities, and career progression.

The system should automatically create a comprehensive competency profile for every official using information such as designation, department, job role, current assignment, educational qualifications, work experience, and previous trainings.

Based on this profile, the platform should evaluate the official's existing competencies against predefined competency frameworks for Official Statistics and identify knowledge and skill gaps.

The AI engine should map competencies across multiple domains, including: Statistical Competencies Survey Design, Sampling, National Accounts, Price Statistics, Labour Statistics, Agricultural Statistics, Industrial Statistics, SDG Indicators, Metadata Standards, and Data Quality Frameworks.

Technical Competencies Python, R, SQL, Stata, SPSS, SAS, GIS, Data Visualization, AI/ML, Cloud Computing, APIs, and Open Data.

Digital Governance Cybersecurity, Data Privacy, Digital Signatures, Government Cloud, and Digital Public Infrastructure.

Behavioural and Managerial Competencies Leadership, Communication, Project Management, Ethics, Decision Making, and Change Management.

Using AI techniques such as Machine Learning, Natural Language Processing (NLP), Large Language Models (LLMs), semantic search, and competency mapping, the platform should recommend personalized learning pathways from the iGOT Karmayogi course repository.

Recommendations should consider the official's current competency level, previous learning history, departmental priorities, future job requirements, emerging technologies, and career progression.

The platform should integrate seamlessly with iGOT Karmayogi APIs to retrieve course catalogues, recommend relevant courses, monitor enrolment and completion status, and update competency scores automatically.

To support continuous learning, the solution should provide AI-powered virtual assistants for learner support, adaptive assessments, interactive learning modules, virtual laboratories, quizzes, and multilingual learning resources.

The system should continuously monitor learner progress and dynamically update recommendations based on performance and newly acquired competencies.

To strengthen capacity building, the platform should support AI powered Intelligent Assessment Engine capable of generating objective type questions (MCQs), and quizzes from uploaded learning materials such as documents, presentations, videos etc.

It should provide instant evaluation, explanations for correct answers, and personalized feedback to reinforce learning outcomes.

This feature should enable trainers to automatically create assessments and quizzes, and evaluate learner understanding, and provide instant feedback, thereby enhancing continuous learning and competency assessment.

The engine should leverage Large Language Models (LLMs), Natural Language Processing (NLP), etc.

A comprehensive analytics dashboard should be provided for both employees and administrators.

The employee dashboard should display current competency levels, identified skill gaps, recommended learning paths, learning hours, and overall progress.

The administrator dashboard should provide organization-wide insights into workforce competencies, training effectiveness, competency distribution, emerging skill requirements, and predictive analytics for future capacity-building needs.

The solution should be designed as a secure, scalable, cloud-ready, and interoperable web platform capable of integrating with existing government digital ecosystems through standard APIs.

The platform should support role-based access control, Single Sign-On (SSO), and secure data exchange while ensuring compliance with government cybersecurity and data privacy guidelines.

The proposed AI-enabled Skill Intelligence Platform will enable data-driven workforce development by delivering personalized, competency-based learning recommendations, improving utilization of iGOT Karmayogi resources, and creating a future-ready statistical workforce equipped with modern statistical, analytical, and digital skills required for the evolving needs of India's Official Statistical System.

Expected Solution

The AI enabled platform for training and capacity building by providing personalized learning recommendations, improving competency levels of officials, enhancing utilization of iGOT Karmayogi resources, and creating a future-ready workforce equipped with modern statistical and digital skills.

Description

Facade-Driven Architectural Expression-Develop an innovative facade system that enhances architectural aesthetics, responds to climate (light, heat, ventilation), and blends with the surrounding urban context. Breathable & Nature-Integrated Design-Incorporate a central landscape courtyard and green interfaces (terraces, balconies) to create an airy, breathable structure that integrates nature and improves occupant well-being. Residential & Commercial Design Efficiency-Ensure functional planning for commercial activation on lower floors and well- designed residential units above, with optimal daylight, ventilation, privacy, and views. Structural Modeling & Detailing-Create 2D structural drawings for key components such as beams, columns, and slabs, including necessary detailing.The model should include all essential building elements: beams, columns, slabs, stairs, and tile flooring. Good to Have (Optional) Site Compatibility & Environmental Analysis (Using Forma Site Design)-Utilize Forma Site Design to study site orientation, sun path, wind conditions,and massing strategies, ensuring the design is environmentally responsive and contextually appropriate. (1-2 Hour) High-Quality Visual Presentation & Walkthrough-Deliver a pictorial, design-focused presentation including rendered views, facade studies, diagrams, and a 30-second walkthrough animation.Rendering quality and visual storytelling will be key evaluation criteria.

Participation Guidelines For Idea Submission

Each student team should submit Revit 3D Model of a Basement Parking + Ground + First Floor that creates a vibrant urban destination while seamlessly integrating nature, sustainability, and user well-being and a PowerPoint presentation (5-7 Slides). Models should be created using ONLY Revit and not copied or taken from any other source. AI Generated content is NOT ALLOWED.

For Grand Finale

Students must use

Autodesk Revit to design and create 3D Model of specific mixed used building within the given time period and present the following to the jury members: Design a B+G+9 mixed-use development that brings together active commercial spaces and sustainable residential living. Create a nature-integrated, climate-responsive building centered around a landscaped courtyard that enhances daylight,ventilation, and occupant well-being.

Key HEADING Deliverables

Commercial podium (Basement Parking + Ground + First Floor) with retail, cafés,and community spaces. Residential levels (2nd–9th Floor) featuring efficient layouts, balconies, natural ventilation, and privacy. Innovative climate-responsive facade with shading elements and green terraces. Central landscaped courtyard as the project's defining feature. Complete

Autodesk Revit model with architectural and structural elements,including detailed drawings. Optional Autodesk Forma studies for sun, wind, and environmental analysis. High-quality presentation with renders, diagrams, facade studies, and a 30-second walkthrough animation. PPT explaining the final project. Complete Structural reinforcement drawing of any one floor along with detailing. Complete 3D Model. Rendered images and Walkthrough video (30 secs.) of the final Model.

Note

Teams coming with pre-designed files will be disqualified. Attach Marking Criteria Table here*

Faculty (SIH SPOC) Form

Teams choosing to submit idea for Autodesk’s problem statement are required to request their faculty (SIH SPOC) to fill this 'Mandatory Form'.

Autodesk Revit

Autodesk Revit is a Building Information Modeling (BIM) software primarily used by architects, engineers, and construction professionals to design, model, and document buildings and infrastructure in 3D. Students and educators can click Here to get FREE access to Revit.

Problem Description

Training systems frequently capture enrolment, attendance, assessment and certification, but reliable information on employment, self-employment, job retention, wage progression, relevance of training and longer-term livelihood outcomes may remain incomplete.Trainees may change phone numbers or locations, employers may not report consistently, and multiple programmes may use different identifiers and definitions. Without longitudinal outcomes, it is difficult to compare providers, improve courses, target future investments or demonstrate public value. The challenge is to establish credible, low-burden and privacy conscious outcome tracking.

Expected Solution

Outcome A longitudinal skilling-outcomes and impact-measurement system that creates consent-based trainee records,links training with placement and employment signals, conducts automated and assisted follow-ups,captures self-employment and apprenticeship outcomes, validates employer information, measures wage and retention progression, and provides cohort, course, provider, district and demographic analytics. It should identify skill gaps and reasons for non-placement or attrition.

Expected Outcomes include higher-quality outcome data, better programme and provider accountability,targeted remedial actions, improved resource allocation and evidence-based policy design.

Background

Quantum computing is a transformative technology with significant impact across scientific and industrial domains. However, education in this field remains challenging due to the abstract nature of core concepts such as qubits, superposition, entanglement, and quantum algorithms. Existing learning resources are often static, heavily theoretical, and lack hands-on interaction. Limited access to real quantum hardware further restricts practical learning. There is a strong need for an integrated, interactive, and intelligent platform that combines theoretical instruction, visual circuit design, real-time simulation, and personalized AI-based guidance to accelerate quantum education and workforce development.

Description

The goal is to develop an AI-powered interactive web-based platform that enables students, researchers, and professionals to learn, design, simulate, and visualize quantum algorithms. The platform will offer structured learning modules covering quantum computing fundamentals, circuit design, and standard quantum algorithms. Users will be able to construct quantum circuits through a drag-and-drop interface or by writing code, execute them on multiple quantum simulators, and visualize quantum states and measurement outcomes. AI-assisted features will provide real-time explanations, error detection, optimization suggestions,and personalized learning paths. The system will support major quantum software development kits and promote collaborative and modular learning.

Objectives

Design and develop an interactive web-based platform for learning quantum computing and quantum algorithms. Provide graphical (drag-and-drop) and code-based quantum circuit design tools. Enable real-time execution and simulation of quantum circuits using multiple backends(Qiskit Aer, PennyLane, Cirq, qBraid, etc.). Integrate AI-assisted tutoring for concept explanation, code generation, debugging, and personalized learning recommendations. Support visualization of quantum states, Bloch spheres, measurement probabilities, and circuit execution results. Include assessment modules, coding challenges, progress tracking, and instructor dashboards.

Expected Solution

A comprehensive AI-based interactive quantum learning platform that seamlessly integrates education, programming, simulation, visualization, and intelligent tutoring. The solution will offer structured theoretical content, visual circuit builders, integrated code editors, multi-framework simulation support, AI-powered assistance, assessment tools, and progress analytics. The platform will be designed to be scalable and accessible, contributing to the development of a quantum-ready workforce.

Background

Medium-resolution satellite imagery, typically ranging from 10 to 30 meters, is widely used in change detection, agriculture, land-cover mapping, disaster monitoring, and urban planning because it offers broad coverage and frequent revisit time. However, the spatial detail is often insufficient for fine-scale analysis, such as identifying small buildings, narrow roads, field boundaries, or localized damage assessment. This creates a need for advanced deep learning based generative enhancement techniques that can extract greater value from existing Earth observation data.

Description

Medium-resolution satellite imagery, usually ranging from 10 to 30 meters, is widely used in remote sensing for agriculture monitoring, land-cover mapping, urban planning, disaster assessment, and environmental observation because it provides large-area coverage and frequent revisit capability. However, its spatial resolution is often not sufficient to clearly identify fine details such as narrow roads, small buildings, field boundaries, water edges, or localized damage. This limitation reduces the accuracy and confidence of interpretation and decision-making in applications that require detailed ground-level information. Generative AI super-resolution addresses this problem by using advanced models such as GANs, diffusion models, and deep neural networks to enhance medium-resolution satellite images into sharper and more information-rich finer outputs. These models learn spatial textures, patterns, edges, and spectral relationships from training data containing both medium-resolution and high-resolution image pairs. The goal is not simply to make the image visually clearer, but to reconstruct useful fine-scale details while preserving the original geographic and spectral consistency of the satellite data. The expected solution is a robust AI-based super-resolution framework that can take medium-resolution satellite imagery as input, perform pre-processing, apply a trained generative model, and produce an enhanced spatial resolution image, suitable for analysis. The system should improve feature visibility, support better classification, change detection, crop monitoring, urban mapping, and disaster response. At the same time, it must clearly manage uncertainty because some reconstructed details are inferred by the model and not directly observed. Therefore, validation against high-resolution reference data is essential to ensure that the enhanced outputs are scientifically reliable and useful for real-world remote sensing applications.

Expected Solution

The expected solution is a robust super-resolution framework model based on the choice of participating team (Transformers/Generative/CNN etc.) that can transform the input medium-resolution satellite imagery (10m Sentinel-2 Satellite Imagery) into sharper, information-rich products (<4m) while preserving geospatial and spectral consistency. The solution should include pre-processing, model training with paired datasets, accuracy assessment, and validation against high-resolution references. Ideally, it should support applications such as crop monitoring, urban analysis, and disaster assessment. The final outcome should improve in-terms of interpretability and analytical utility, while clearly accounting for uncertainty and error components.

Smart education, a concept that describes learning in digital age. It enables learners to learn more effectively, efficiently, flexibly and comfortably.