Describe your project

Problems Intervue solves

Problem: Students prepare poorly and thus waste time.
Solution It offers personalized insights and competency paths. Knowledge and skills are measured in terms of various dimensions of strength and weakness. This maximizes learning because it learned directly related to the content.

Problem: No peer interaction is allowed during learning.
Solution: Adaptive Learning Pathways are provided as personalized tools with real-time collaborative practice. Google Cloud services facilitate dynamic engagement between insights reached through community building for improved results.

Problem: Students may forge skills claims, undermining trust.
Solution The competency verification system allows safety in the verification of competencies through blockchain. This has achievements verifiably transparent, hence credible within the learning ecosystem.

Problem: Students require guidance and responsibility. Answer A dedicated 1:1 mentor matched up with the AI coach 'Jobie' that offers one-on-one personalized support. This keeps students motivated in their educational pursuit while holding them accountable and monitoring their progress.

Problem: Current students lack preparation in real-time situations.

Solution Immersive 360° VR-AI Mock Simulations create very realistic learning experiences based on practices in real environments, so there will always be instant feedback to prepare students for challenges.

Out-of-Scope

Non-academic skills: The solution doesn't allow scope for the development of skills outside the very tight curricula, such as arts or physical education.

Placement Services: There are no placement services and liaison with the employers available. Core learning is not overshadowed by programs for the graduate or certification in advanced degree programs.

Future Scope
Mobile Applications: Implement cross-platform apps for a variety of learning goals and locations.In

Challenges I ran into

Challenges I Ran Into
History-Aware LLM Mentee Context Online Agent Database Query Access
To create the context-aware LLM agent that would produce on-demand reports for mentees, the database was to be queried. That was another huge hurdle. The initial releases throttling the data retrieval speeds-it was slow and dragged the sessions. That was how I optimized the database queries by keying the key fields and getting better and more efficient algorithms for the processing of data. Another feature was the incorporation of caching mechanisms that stored frequently accessed data for a greatly improved response time and higher user experience generally.

Real-time Cursors Implementation to Sockets in Real-time Coding
Implementation of Sockets for Real-time Sharing of Cursors in Coding was not without its own challenges. The first involved cursor position lag in users or some misalignment. I ensured it overcame this by more structured data transactions thus setting the implementation of a heartbeat mechanism, constantly synchronizing all the cursor positions across different clients. In doing so, every participant could view the actions of every other participant in real time hence enhancing collaborative coding and effective peer interaction.

Skill-Gap Adaptive Resources Generation: Heat-map on the intensity basis Trying to associate adaptive resources with a heat map that would intensify skill gaps, and then integrating content from the YouTube V3 API-a very complex task-streamed diagrams, infographics, and code. No alignment in the content formats emerged with different problems in the integration. Solution came by applying Graphalogue-standardized delivery for streaming fluid content. It is very instrumental in providing diagrams and other infographics besides code snippets. It also provides rich, adaptive learning environments that learn to the needs of the students.