Best AI tools for< make exams >

Quizgecko is an AI-powered question generator that helps users create quizzes, tests, and exams from any text. It offers a range of features, including automatic question generation, multiple question types, customizable settings, and analytics and reporting. Quizgecko is designed to save time and effort for teachers, students, and businesses, and to enhance the learning experience through engaging and interactive quizzes.

Assessment Systems is an online testing platform that provides cost-effective, AI-driven solutions to develop, deliver, and analyze high-stakes exams. With Assessment Systems, you can build and deliver smarter exams faster, thanks to modern psychometrics and AI like computerized adaptive testing, multistage testing, or automated item generation. You can also deliver exams flexibly: paper, online testing unproctored, online proctored, and test centers (yours or ours). Assessment Systems also offers item banking software to build better tests in less time, with collaborative item development brought to life with versioning, user roles, metadata, workflow management, multimedia, automated item generation, and much more.

Wordjotter is an AI-powered Anki flashcards application that utilizes artificial intelligence to enhance the learning experience. It helps users create and study flashcards efficiently by leveraging AI algorithms to optimize content and improve retention. With Wordjotter, users can easily create personalized flashcards, receive intelligent recommendations, track their progress, and collaborate with others in a seamless manner. The application aims to revolutionize the traditional flashcard learning method by incorporating AI technology to make learning more effective and engaging.

PrepSup is an AI-powered platform that offers a combination of powerful flashcards, AI tutoring, and PDF analysis tools. It provides a comprehensive solution for students and professionals to enhance their learning experience, improve retention, and analyze PDF documents efficiently. With PrepSup, users can create interactive flashcards, receive personalized tutoring based on AI algorithms, and analyze PDF files for key information. The platform aims to streamline the learning process and make studying more effective and engaging.

Chatter is an AI-powered language learning chatbot that integrates with Telegram, transforming regular conversations into engaging language adventures. It offers personalized chat-based lessons tailored to each user's learning pace and style, making language mastery accessible and enjoyable. With Chatter, users can practice real-life scenarios, receive instant feedback, and immerse themselves in interactive exercises, turning language learning into a fun and effective experience.

Doctrina AI is an AI-powered education suite that provides a range of tools to enhance the learning experience for students and teachers. These tools include a class notes summarizer, an essay generator, an exam generator, a quiz generator, and a chat-based AI assistant. Doctrina AI also offers a Software Development Kit (SDK) that allows educational institutions to integrate these AI tools into their own systems.

TalkBerry is an AI-powered language learning platform that provides users with personalized language tutors. With TalkBerry, users can engage in natural conversations with AI tutors, which helps them to improve their language skills and become more confident speakers. TalkBerry offers a variety of features, including immersive speaking experiences, real-time evaluation and feedback, and a wide range of conversation scenarios. TalkBerry is a great tool for anyone who wants to improve their language skills, whether they are a beginner or an experienced learner.

PDF2Anki is an AI-powered study tool that helps students save time creating flashcards and notes. With PDF2Anki, students can convert lecture slides, textbooks, and other materials into flashcards and notes in seconds. PDF2Anki also includes a personalized tutor that can help students learn and test themselves. PDF2Anki is used by students in over 100 disciplines across 106 countries.

Memrizz is an AI-powered flashcard generator that helps you create personalized flashcards for effective learning. With Memrizz, you can easily create flashcards from text, images, or audio files, and customize them to fit your learning style. Memrizz also provides spaced repetition and gamification features to make learning more engaging and efficient.

Sociask is an AI-powered personalized learning app that uses AI to make education fun and effective. It allows users to generate custom courses for free in minutes, based on their interests, knowledge, and pace. Sociask also provides personalized tutoring, with an AI that stimulates the mind with intuitive examples, profound analogies, and deep-thinking questions. The app is designed to be accessible yet inspiring, with features such as bite-sized explanations, thought-provoking questions, and real-world examples.

Skillflow is an AI platform built for learning. Unlike ChatGPT, Skillflow only requires a single prompt to start exploring a topic. Additionally, Skillflow is structured like a video game to make the whole process a lot more fun! Skillflow uses the most advanced models to generate course content, games, lesson personalization and more. Read the content and pass the tests to continue into the next module.

DoryAI is an AI-powered flashcard generator that helps users create personalized flashcards and quizzes in seconds. It offers real-time feedback to enhance learning effectiveness and save time. With DoryAI, users can generate flashcards from various sources, including YouTube videos, PDFs, documents, and custom prompts. They can also export flashcards to popular platforms like Kahoot, Anki, and Quizlet for further learning. DoryAI's mission is to break down learning barriers and make education more accessible and personalized.

SlideNotes is a tool that helps users convert presentations and PDFs into notes automatically. It is designed to make studying and working easier by providing users with a quick and easy way to access the key points of any presentation or document. SlideNotes is easy to use, simply upload any .pptx or .pdf file and the tool will automatically generate notes that you can easily review and share.

Theoriestudio.nl is an online platform that provides a comprehensive theory course, practice tests, and mock exams to help users pass their theory exam for driving license in the Netherlands. The platform utilizes AI (Artificial Intelligence) to enhance the learning experience and improve the chances of passing the exam in one attempt. With its user-friendly interface, accessible on any device, and a supportive community, Theoriestudio.nl aims to make the process of obtaining a driving license easier and more efficient.

OmniSets is an ultimate flashcard tool that helps students learn efficiently. It combines advanced machine learning algorithms and incorporates them throughout the platform. OmniSets offers various modes, from spaced repetition to matching games and AI tools, to make studying easier. Users can create and organize study sets, search from over 250k+ public study sets, and take advantage of the spell tool to write terms and definitions. OmniSets is a community-driven platform that values user feedback and is constantly updating and improving its features.

Lingostar is an AI-powered language learning platform that provides real-time conversations with a virtual AI assistant. It offers personalized study plans, vocabulary building tools, and progress tracking to help users improve their fluency in English, Spanish, or French. Lingostar is designed to make language learning more accessible and affordable, allowing users to practice speaking and listening skills without the need for expensive tutors or language exchange partners.

PrepAI is an advanced test generator that uses AI to help educators create high-quality assessments quickly and easily. With PrepAI, teachers can save time, engage students with unique questions, and prepare them for success. PrepAI offers a variety of features to make test creation easy, including multiple content input options, various question formats, and an easy-to-use dashboard. PrepAI also offers a variety of advantages for educators, including the ability to analyze higher-order thinking skills, conduct tests effortlessly, and access unlimited question sets.

Algor Education is an online mind mapping tool that uses AI to help users create visual representations of text, images, and audio files. With Algor Education, users can quickly and easily create mind maps that can be used for a variety of purposes, including studying, brainstorming, and taking notes. Algor Education offers a variety of features that make it a powerful tool for students, teachers, and professionals alike. These features include the ability to:

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

This website provides a collection of prompts for the Stable Diffusion AI image generation model. Users can search for prompts by model, text, and tags. The website also includes articles and news about Stable Diffusion and other AI-related topics.

Examor is a website application that allows you to take exams based on your knowledge notes. It helps you to remember what you have learned and written. The application generates a set of questions from the documents you upload, and you can answer them to test your knowledge. Examor also uses GPT to score and validate your answers, and provides you with feedback. The application is still in its early stages of development, but it has the potential to be a valuable tool for learners.

The UvA Deep Learning Tutorials repository contains a series of Jupyter notebooks designed to help understand theoretical concepts from lectures by providing corresponding implementations. The notebooks cover topics such as optimization techniques, transformers, graph neural networks, and more. They aim to teach details of the PyTorch framework, including PyTorch Lightning, with alternative translations to JAX+Flax. The tutorials are integrated as official tutorials of PyTorch Lightning and are relevant for graded assignments and exams.

LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. Here is an attempt to keep track of the initiatives around LangChain. Subscribe to the newsletter to stay informed about the Awesome LangChain. We send a couple of emails per month about the articles, videos, projects, and tools that grabbed our attention Contributions welcome. Add links through pull requests or create an issue to start a discussion. Please read the contribution guidelines before contributing.

Nitro is a high-efficiency C++ inference engine for edge computing, powering Jan. It is lightweight and embeddable, ideal for product integration. The binary of nitro after zipped is only ~3mb in size with none to minimal dependencies (if you use a GPU need CUDA for example) make it desirable for any edge/server deployment.

The `sec-parser` project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. It helps in parsing SEC filings for financial and regulatory analysis, analytics and data science, AI and machine learning, causal AI, and large language models. The tool is especially beneficial for AI, ML, and LLM applications by streamlining data pre-processing and feature extraction.

Node.js bindings for OpenAI's Whisper model that automatically converts audio to WAV format with a 16000 Hz frequency to support the whisper model. It outputs transcripts to various formats, is optimized for CPU including Apple Silicon ARM, provides timestamp precision to single word, allows splitting on word rather than token, translation from source language to English, and conversion of audio format to WAV for whisper model support.

Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.

model.nvim is a tool designed for Neovim users who want to utilize AI models for completions or chat within their text editor. It allows users to build prompts programmatically with Lua, customize prompts, experiment with multiple providers, and use both hosted and local models. The tool supports features like provider agnosticism, programmatic prompts in Lua, async and multistep prompts, streaming completions, and chat functionality in 'mchat' filetype buffer. Users can customize prompts, manage responses, and context, and utilize various providers like OpenAI ChatGPT, Google PaLM, llama.cpp, ollama, and more. The tool also supports treesitter highlights and folds for chat buffers.

Langroid-examples is a repository containing examples of using the Langroid Multi-Agent Programming framework to build LLM applications. It provides a collection of scripts and instructions for setting up the environment, working with local LLMs, using OpenAI LLMs, and running various examples. The repository also includes optional setup instructions for integrating with Qdrant, Redis, Momento, GitHub, and Google Custom Search API. Users can explore different scenarios and functionalities of Langroid through the provided examples and documentation.

SimpleAICV_pytorch_training_examples is a repository that provides simple training and testing examples for various computer vision tasks such as image classification, object detection, semantic segmentation, instance segmentation, knowledge distillation, contrastive learning, masked image modeling, OCR text detection, OCR text recognition, human matting, salient object detection, interactive segmentation, image inpainting, and diffusion model tasks. The repository includes support for multiple datasets and networks, along with instructions on how to prepare datasets, train and test models, and use gradio demos. It also offers pretrained models and experiment records for download from huggingface or Baidu-Netdisk. The repository requires specific environments and package installations to run effectively.

Lego AI Parser is an open-source application that uses OpenAI to parse visible text of HTML elements. It is built on top of FastAPI, ready to set up as a server, and make calls from any language. It supports preset parsers for Google Local Results, Amazon Listings, Etsy Listings, Wayfair Listings, BestBuy Listings, Costco Listings, Macy's Listings, and Nordstrom Listings. Users can also design custom parsers by providing prompts, examples, and details about the OpenAI model under the classifier key.

Dough is a tool for crafting videos with AI, allowing users to guide video generations with precision using images and example videos. Users can create guidance frames, assemble shots, and animate them by defining parameters and selecting guidance videos. The tool aims to help users make beautiful and unique video creations, providing control over the generation process. Setup instructions are available for Linux and Windows platforms, with detailed steps for installation and running the app.

This project provides a drop-in replacement REST API for Google Cloud Vertex AI (PaLM 2, Codey, Gemini) that is compatible with the OpenAI API specifications. It aims to make Google Cloud Platform Vertex AI more accessible by translating OpenAI API calls to Vertex AI. The software is developed in Python and based on FastAPI and LangChain, designed to be simple and customizable for individual needs. It includes step-by-step guides for deployment, supports various OpenAI API services, and offers configuration through environment variables. Additionally, it provides examples for running locally and usage instructions consistent with the OpenAI API format.

Wingman AI allows you to use your voice to talk to various AI providers and LLMs, process your conversations, and ultimately trigger actions such as pressing buttons or reading answers. Our _Wingmen_ are like characters and your interface to this world, and you can easily control their behavior and characteristics, even if you're not a developer. AI is complex and it scares people. It's also **not just ChatGPT**. We want to make it as easy as possible for you to get started. That's what _Wingman AI_ is all about. It's a **framework** that allows you to build your own Wingmen and use them in your games and programs. The idea is simple, but the possibilities are endless. For example, you could: * **Role play** with an AI while playing for more immersion. Have air traffic control (ATC) in _Star Citizen_ or _Flight Simulator_. Talk to Shadowheart in Baldur's Gate 3 and have her respond in her own (cloned) voice. * Get live data such as trade information, build guides, or wiki content and have it read to you in-game by a _character_ and voice you control. * Execute keystrokes in games/applications and create complex macros. Trigger them in natural conversations with **no need for exact phrases.** The AI understands the context of your dialog and is quite _smart_ in recognizing your intent. Say _"It's raining! I can't see a thing!"_ and have it trigger a command you simply named _WipeVisors_. * Automate tasks on your computer * improve accessibility * ... and much more

LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=