Inspiration: As students, we sometimes divert from class and don't know what we have missed. Even the teacher may lose track of the syllabus completion. Students struggle to send feedback to a higher authority on the faculty.
What it does: It gives detailed feedback on the classroom, how it is being maintained, etc., and a detailed report on the attentiveness of the students, where they got distracted, syllabus completion, and how the students will engage. it solves the below mentioned problem in real life: 1.) Insufficient internet access 2.) real time data analysis 3.) Limited budgets 4.) storage of data 5.) real-time analysis to the higher officials in the institution.
How we built it: We used tagging with speech recognition in Python and compared both of the results to give a report SOFTWARE DEVELOPMENT 1.) Method of key phase tagging with the inputs from speech recognition to identify curriculum completion and the pace of the teacher according the specific classroom. Solves: completely offline method so internet access is not required, instant feedback (i.ereal-time data),one time installment and easy development so the budget will be less, analysis of the teacher to the higher officials 2.) Camera analysis of attentiveness students with an already pre-trained AISolves: since its pre-trained network access is not required, itshows wherestudents may lag in concept and informs the teacherabout it, real time data analysis.
Challenges we ran into: since we wanted to build this smart classroom management that works offline and is less expensive so even the underdeveloped institutions can guide themselves, we had to use low-end models and methods that are not that accurate but on further work and development, this can be released on a large scale and a perfect product can be made
Accomplishments that we're proud of: We were able to fill full some parts of the goal we had to solve in mind in this less time and produce a work model showing that this is possible to make a perfect product out of it in real life, proving it is highly feasible in reality.
What we learned: How a personally train AI can help in this project, but due to time constraints we were not able to do that and use already existing data sets etc
What's next for Vishwakarma; once we completely build this product, we are thinking of launching it. With the future plans indicating below : Services provided by the company: 1.) Comparison of the feedback from the students and data collected by the software methods using a data analyst to give the perfect information on the Teacher and student analysis 2.) Severs to maintain the data from the analysis and store them. These can be installed in the instituationor in the building maitainedby the company. It depends by the choice of the costumer.
Potential Societal Impact: 1.) Enhanced Educational Quality: By identifying gaps in student understanding and providing teachers with actionable feedback, this solution can improve the overall quality of education. Helps bridge the learning divide, especially in low-resource schools. 2.) Improved Teacher Effectiveness: Offers teachers a tool to adjust teaching strategies in real time, fostering better engagement and outcomes. 3.) Equity in Education: Offline functionality ensures accessibility in rural or underprivileged areas where internet connectivity is limited. Cost-effective design makes it feasible for schools with tight budgets. 4.) Data-Driven Decision-Making: Institutions can make informed decisions based on detailed performance analytics, improving resource allocation and curriculum planning. 5.) Future-Ready Students: Encourages the integration of technology into classrooms, preparing students for a digital-first world.
Technological Readiness :
- Innovative Prototype Development: A user-friendly EdTech solution has already been conceptualized, emphasizing offline functionality and integrating speech recognition with pre-trained AI. The technical infrastructure aligns with the needs of underserved schools, ensuring ease of deployment.
- Scalable Architecture: The centralized system architecture allows for seamless expansion while keeping costs low. Hybrid service models combining on-site and centralized data storage are prepared to minimize adoption barriers.
Financial Feasibility :
- Cost-Effective Design: The solution’s offline functionality and pre-trained AI lower operational costs, enabling affordable implementation. The financial model, including one-time licensing fees and tiered subscription plans, is prepared to accommodate varied budgets. 2.Break-Even Timeline: The business model anticipates achieving break-even within 12–18 months through recurring revenue streams, indicating financial readiness for deployment.
Educator Readiness :
- Future-Ready Training Modules: Training programs for teachers and administrators are designed to empower them to use AI-powered tools effectively. The focus on boosting engagement and productivity through interactive modules shows a proactive approach to overcoming resistance to new technology.
- Localized Customization: Tailoring the solution to regional languages and curricula demonstrates preparedness to address diverse educational needs.
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