Andrej Karpathy on X: implications of AI to schools (twitter.com)

I couldn’t retrieve Andrej Karpathy’s original X post because the x.com page returned a JavaScript/access notice, so I can’t summarize his exact words or quotes. Below is a concise, sourced-agnostic synthesis of the core implications that experts like Karpathy typically highlight when discussing AI’s impact on schools. AI-driven models (large language models, code assistants, adaptive tutors) will force curriculum and assessment redesign: rote tasks and standardized tests become less informative, while project-based learning, coding literacy, prompt engineering, model evaluation, and critical thinking around AI outputs gain priority. Technically, schools must contend with deployment choices (on-device vs. cloud), fine-tuning/customization for local curricula, privacy-preserving training (federated learning, differential privacy), and monitoring for hallucinations, bias, and adversarial inputs. Operationally this raises compute/cost, connectivity, and IT support requirements, as well as teacher upskilling to integrate AI as an augmentation tool rather than a replacement. Equity and governance are central—ensuring access, transparent model behavior, data protection for minors, and updated policies around plagiarism and assessment integrity will determine whether AI amplifies or reduces educational disparities.