Interactive classroom-ready activities for learning computer science, AI, and probability through direct manipulation, visual feedback, and concrete examples.
A beginner-friendly course with 28 interactive experiments. Explore coin tosses, dice, classroom surveys, Bayes' theorem, and probability distributions through visual practice, guided solutions, and concise takeaways.
A polished interactive graph theory activity comparing Eulerian Circuit and Hamiltonian Cycle. Students animate edge traversal, vertex visitation, Eulerian degree characterization, Hamiltonian backtracking, and the growth of search states.
A vibrant classroom lab for propositional logic. Students reveal all eight inference rules progressively, study concrete examples, and arrange proof steps through drag-and-drop practice.
A browser-based version of the prompt engineering visualization. It shows prompt growth as a trajectory through an embedding-like semantic universe, with anchor concepts, numbered prompt states, semantic rings, and gender-direction vectors.
A Module 1 sequence for explaining the AI family, neural network learning, and LLM generation through interactive, academically grounded classroom visuals.
A polished interactive visual for explaining how a general ChatGPT conversation becomes a purpose-built teaching assistant through clear purpose, instructions, knowledge, examples, rules, tools, testing, and refinement.
A standalone interactive demonstration for convolutional neural networks. It begins with a 1D cricket speed-gun convolution example, then moves into image filters, feature maps, stride, padding, output shape, 3D inputs, and pooling.
For FDPs, keynotes, classroom demonstrations, workshops, and research discussions, connect with Dr. Mahipal Jadeja, Assistant Professor at MNIT Jaipur. Email: mahipaljadeja.cse@mnit.ac.in.