2 min readfrom Machine Learning

Looking for feedback on OpenVidya: an open-source AI classroom layer for NCERT/CBSE [R]

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I invite your feedback on OpenVidya, an open-source AI classroom layer designed specifically for NCERT and CBSE curricula. Built as a fork of OpenMAIC, OpenVidya aims to transform learning by incorporating multi-agent AI tailored to Indian education, beyond generic slide or chat experiences. Current features include structured knowledge grounding, concept dependency graphs, and diverse pedagogy modes such as Teacher Narration and Exam Dojo. I seek insights on its architecture, target user focus, evaluation methods, and potential dataset additions.

The emergence of OpenVidya as an open-source AI classroom layer tailored for the Indian education system is an exciting development in the intersection of technology and pedagogy. As the project is built upon the foundation of OpenMAIC, it aims to move beyond generic learning experiences that often fail to resonate with local contexts. By grounding its features in the NCERT and CBSE curriculum, OpenVidya seeks to provide a more relevant and structured approach to education. This initiative reflects a growing trend in educational technology, akin to what we see in tools like Build AI Financial Models in Sourcetable, where the focus is on making complex tasks more manageable through tailored solutions.

One of the standout features of OpenVidya is its ability to create concept dependency graphs for prerequisite-aware lessons. This not only aids students in understanding the material but also allows educators to engage with their students more effectively. The inclusion of diverse pedagogy modes—ranging from Teacher Narration to Rapid Revision—demonstrates a keen awareness of different learning styles and needs. Such an approach contrasts sharply with the more traditional and often rigid methods of teaching, which can feel disconnected from the realities students face. This shift towards a more human-centered design in educational technology is essential for fostering an engaging learning environment, paralleling the themes discussed in the article Job has me doing a needlessly complicated task, where simplicity and clarity in processes are vital.

However, with innovation comes the need for rigorous evaluation. The author of OpenVidya rightly seeks feedback on several key aspects, including architecture and user focus. These questions are critical for ensuring that the platform not only meets current educational demands but also evolves alongside them. Understanding whether OpenVidya can truly outperform generic AI tutors hinges on robust metrics for evaluation. This is a crucial challenge; after all, the success of such an initiative relies on its ability to provide meaningful enhancements to learning outcomes. It invites educators, technologists, and stakeholders to consider how we measure success in educational technology.

In a rapidly changing landscape, the focus on localizing educational tools is more important than ever. As OpenVidya explores the nuances of the Indian educational system, it raises pertinent questions about how we can better serve diverse student populations through technology. The potential for a more tailored educational experience that understands local exam patterns, cultural contexts, and learning behaviors offers a glimpse into a future where AI not only supports but enhances education. As we look ahead, it will be intriguing to see how initiatives like OpenVidya evolve and what impact they have on traditional educational paradigms. Will they set a new standard for how we integrate technology into learning, or will they highlight the challenges and limitations that still exist in this space? The conversation around these questions is just beginning, and it’s one worth following closely as we explore the future of education together.

I’ve been experimenting with an open-source project called OpenVidya, built as a fork of OpenMAIC.

The goal is to adapt multi-agent AI classroom generation for Indian education rather than treating learning as a generic slide/chat experience.

Repo: https://github.com/dpaul0501/OpenVidya

Current features:

  • NCERT/CBSE-style knowledge grounding using structured JSON registries
  • Concept dependency graphs for prerequisite-aware lessons
  • Board-style questions with difficulty, traps, and explanations
  • NCERT lab experiment registry with apparatus, objectives, and mistakes
  • Five pedagogy modes:
    • Teacher Narration
    • Story Quest
    • Exam Dojo
    • Lab Without Walls
    • Rapid Revision
  • Mode-specific prompting across outline generation, slide generation, and runtime narration

The thesis is that an AI tutor for India should not just translate content. It should understand exam patterns, local examples, curriculum structure, and how students revise, practice, and get stuck.

I’m looking for critique on:

  • Architecture: is this the right way to ground curriculum into lesson generation?
  • Product: which user should I focus on first — students, teachers, coaching centers, or edtech builders?
  • Evaluation: how would you measure whether this is actually better than a generic AI tutor?
  • Dataset: what open Indian curriculum/question resources should be added?
  • README/demo: what is unclear or missing?

Stars are appreciated if you think the direction is worth building, but I’m mainly looking for honest feedback from people who care about AI + education.

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