Best AI tools for< Assess Learning >

Breakout Learning is an AI-powered educational platform that transforms traditional case studies into engaging, multifaceted experiences. It empowers professors with AI insights into small-group discussions, enabling them to customize lectures and foster deeper student comprehension. Students benefit from rich content, peer-led discussions, and AI assessment that provides personalized feedback and tracks their progress.

Gradescope is an online assessment platform that helps educators deliver and grade assessments seamlessly. It supports various assignment types, including variable-length assignments, fixed-template assignments, paper-based assignments, and programming projects. Gradescope enables educators to provide detailed feedback, maintain consistency with flexible rubrics, and send grades to students with a click. It also offers valuable insights through per-question and per-rubric statistics, helping educators understand student performance and adjust their teaching strategies. Additionally, Gradescope incorporates AI-assisted grading features, such as answer grouping, to streamline the grading process.

EverLyn AI is a platform that allows users to build AI-powered tutors. These tutors can provide personalized and instant support to students, as well as automated assessment. This can help to create a more personalized and effective learning experience for students.

Educator Lab is an AI-powered SaaS tool that helps educators generate compliant lesson plans, worksheets, and activities. With our platform, you can create PDF and Doc files for any grade, academic subject, and level, including general and adult education. Whether you're a teacher, administrator, or instructional designer, Educator Lab has everything you need to streamline your lesson planning process and improve student outcomes.

DUNNO is an AI-powered quiz platform that uses GPT-based models to generate quizzes and intellectual games. With DUNNO, you can quickly create your own quizzes based on any text, topic, or personal notes. After creating a quiz, you can either play alone or invite friends. DUNNO is suitable for various scenarios, including learning, work, and entertainment. It offers features such as quiz creation, quiz results tracking, and multiple game modes to make learning more engaging and interactive.

AI Times Tables is an AI multiplication learning helper that offers free printable multiplication charts and worksheets. It provides a convenient way for students to practice multiplication skills and for teachers or parents to assess mastery of fundamental concepts. The application is designed to enhance math learning journeys for children from Kindergarten to 4th Grade, with customizable use recommendations to suit individual learning paces.

YouTeam is an AI-powered platform that offers a transparent vetting process for hiring engineers. Leveraging deep learning technology, YouTeam provides customized vetting criteria tailored to each role's responsibilities and required skillset. The platform streamlines the hiring process by presenting engineering candidates aligned with the company's unique standards, allowing for a final interview to select the perfect match. With features like technical skill review assessments, coding assessments, and soft skills & culture fit questions, YouTeam ensures a data-rich candidate profile for informed decision-making.

SC Training, formerly known as EdApp, is a mobile learning management system that offers a comprehensive platform for creating, delivering, and tracking training courses. The application provides features such as admin control, content creation tools, analytics tracking, AI course generation, microlearning courses, gamification elements, and support for various industries. SC Training aims to deliver efficient and engaging training experiences to users, with a focus on bite-sized learning and accessibility across devices. The platform also offers course libraries, practical assessments, rapid course refresh, and group training options. Users can customize courses, integrate with existing tools, and access a range of resources through the help center and blog.

SC Training, formerly known as EdApp, is a mobile learning management system that offers a wide range of features to enhance the training experience for both administrators and learners. The platform provides tools for creating, managing, and tracking training courses, with a strong focus on microlearning and gamification. SC Training aims to deliver bite-sized, engaging content that can be accessed anytime, anywhere, on any device. The application also incorporates AI technology to streamline course creation and improve the learning experience. With a diverse course library, practical assessments, and group training capabilities, SC Training is designed to help organizations deliver effective and efficient training programs.

Coursebox is an AI-powered course creation platform that helps businesses create and deliver engaging and effective training programs. With Coursebox, you can convert your existing resources into courses, add AI-powered features like chatbots and quizzes, and track your learners' progress. Coursebox is used by over 30,000 businesses worldwide to create and deliver training programs that are faster, more engaging, and more effective.

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.

Intelligencia AI is a leading provider of AI-powered solutions for the pharmaceutical industry. Our suite of solutions helps de-risk and enhance clinical development and decision-making. We use a combination of data, AI, and machine learning to provide insights into the probability of success for drugs across multiple therapeutic areas. Our solutions are used by many of the top global pharmaceutical companies to improve their R&D productivity and make more informed decisions.

Loupe Recruit is an AI-powered talent assessment platform that helps recruiters and hiring managers assess job descriptions and talent faster and more efficiently. It uses natural language processing and machine learning to analyze job descriptions and identify the key skills and experience required for a role. Loupe Recruit then matches candidates to these requirements, providing recruiters with a ranked list of the most qualified candidates. The platform also includes a variety of tools to help recruiters screen and interview candidates, including video interviewing, skills assessments, and reference checks.

AI Tutor Pro is a cutting-edge AI-powered personal digital assistant developed by Contact North | Contact Nord. It offers a wide range of educational content in various subjects, allowing users to learn anytime, anywhere, and in multiple languages for free. The application helps users assess and enhance their knowledge and skills on diverse topics, ensuring privacy and confidentiality. Contact North | Contact Nord, a not-for-profit corporation established in 1986, is behind this innovative tool.

CodeSignal is an AI-powered platform that helps users discover and develop in-demand skills. It offers skills assessments and AI-powered learning tools to help individuals and teams level up their skills. The platform provides solutions for talent acquisition, technical interviewing, skill development, and more. With features like pre-screening, interview assessments, and personalized learning, CodeSignal aims to help users advance their careers and build high-performing teams.

JADBio is an automated machine learning (AutoML) platform designed to accelerate biomarker discovery and drug development processes. It offers a no-code solution that automates the discovery of biomarkers and interprets their role based on research needs. JADBio can parse multi-omics data, including genomics, transcriptome, metagenome, proteome, metabolome, phenotype/clinical data, and images, enabling users to efficiently discover valuable insights. The platform is purpose-built for various conditions such as cancer, immune, endocrine, metabolic system, chronic diseases, aging, infectious diseases, and mental health, offering solutions for early biomarker discovery, drug repurposing, lead identification, compound optimization, trial monitoring, and response to treatment. JADBio is trusted by partners in precision health & medicine and is continuously evolving to disrupt drug discovery times and costs at all stages.

Microsoft Responsible AI Toolbox is a suite of tools designed to assess, develop, and deploy AI systems in a safe, trustworthy, and ethical manner. It offers integrated tools and functionalities to help operationalize Responsible AI in practice, enabling users to make user-facing decisions faster and easier. The Responsible AI Dashboard provides a customizable experience for model debugging, decision-making, and business actions. With a focus on responsible assessment, the toolbox aims to promote ethical AI practices and transparency in AI development.

H2O.ai is an AI platform that offers a convergence of the world's best predictive and generative AI solutions. It provides end-to-end GenAI platform for air-gapped, on-premises, or cloud VPC deployments, allowing users to own every part of the stack. With features like h2oGPTe, h2oGPT, H2O Danube3, H2O Eval Studio, and GenAI App Store, H2O.ai empowers users to customize and deploy AI models, assess performance, develop safe applications, and more. The platform is known for democratizing AI with automated machine learning and open-source distributed machine learning.

Cognii is an AI-based educational technology provider that offers solutions for K-12, higher education, and corporate training markets. Their award-winning EdTech product enables personalized learning, intelligent tutoring, open response assessments, and rich analytics. Cognii's Virtual Learning Assistant engages students in chatbot-style conversations, providing instant feedback, personalized hints, and guiding towards mastery. The platform aims to deliver 21st-century online education with superior learning outcomes and cost efficiency.

This AI-driven solution revolutionizes course development by streamlining and enhancing the entire process. It provides a comprehensive suite of tools that empower educators to create engaging and effective learning experiences with greater efficiency and ease. By leveraging the power of AI, this solution automates repetitive tasks, provides personalized recommendations, and offers real-time insights, enabling educators to focus on what matters most: delivering exceptional learning outcomes.

Grand Challenge is a platform that provides access to large amounts of annotated training data, objective comparisons of state-of-the-art machine learning solutions, and clinical validation using real-world data. It assists researchers, data scientists, and clinicians in collaborating to develop robust machine learning solutions to problems in biomedical imaging.

Fortuna is a library for uncertainty quantification that enables users to estimate predictive uncertainty, assess model reliability, trigger human intervention, and deploy models safely. It provides calibration and conformal methods for pre-trained models in any framework, supports Bayesian inference methods for deep learning models written in Flax, and is designed to be intuitive and highly configurable. Users can run benchmarks and bring uncertainty to production systems with ease.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.

Fairlearn is a Python package designed to help developers assess and mitigate fairness issues in artificial intelligence (AI) systems. It provides mitigation algorithms and metrics for model assessment. Fairlearn focuses on two types of harms: allocation harms and quality-of-service harms. The package follows the group fairness approach, aiming to identify groups at risk of experiencing harms and ensuring comparable behavior across these groups. Fairlearn consists of metrics for assessing model impacts and algorithms for mitigating unfairness in various AI tasks under different fairness definitions.

Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development lifecycle, enabling teams to optimize workflows around key areas like model building, evaluation, release, and fine-tuning. Starwhale abstracts Model, Runtime, and Dataset as first-class citizens, providing tailored capabilities for common workflow scenarios including Models Evaluation, Live Demo, and LLM Fine-tuning. It is an open-source platform designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Awesome MLSecOps is a curated list of open-source tools, resources, and tutorials for MLSecOps (Machine Learning Security Operations). It includes a wide range of security tools and libraries for protecting machine learning models against adversarial attacks, as well as resources for AI security, data anonymization, model security, and more. The repository aims to provide a comprehensive collection of tools and information to help users secure their machine learning systems and infrastructure.

This repository contains a collection of papers related to tool learning with large language models (LLMs). The papers are organized according to the survey paper 'Tool Learning with Large Language Models: A Survey'. The survey focuses on the benefits and implementation of tool learning with LLMs, covering aspects such as task planning, tool selection, tool calling, response generation, benchmarks, evaluation, challenges, and future directions in the field. It aims to provide a comprehensive understanding of tool learning with LLMs and inspire further exploration in this emerging area.

FlagPerf is an integrated AI hardware evaluation engine jointly built by the Institute of Intelligence and AI hardware manufacturers. It aims to establish an industry-oriented metric system to evaluate the actual capabilities of AI hardware under software stack combinations (model + framework + compiler). FlagPerf features a multidimensional evaluation metric system that goes beyond just measuring 'whether the chip can support specific model training.' It covers various scenarios and tasks, including computer vision, natural language processing, speech, multimodal, with support for multiple training frameworks and inference engines to connect AI hardware with software ecosystems. It also supports various testing environments to comprehensively assess the performance of domestic AI chips in different scenarios.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

This repository contains scripts and tools for evaluating a chat app that uses the RAG architecture. It provides parameters to assess the quality and style of answers generated by the chat app, including system prompt, search parameters, and GPT model parameters. The tools facilitate running evaluations, with examples of evaluations on a sample chat app. The repo also offers guidance on cost estimation, setting up the project, deploying a GPT-4 model, generating ground truth data, running evaluations, and measuring the app's ability to say 'I don't know'. Users can customize evaluations, view results, and compare runs using provided tools.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

Raga LLM Hub is a comprehensive evaluation toolkit for Language and Learning Models (LLMs) with over 100 meticulously designed metrics. It allows developers and organizations to evaluate and compare LLMs effectively, establishing guardrails for LLMs and Retrieval Augmented Generation (RAG) applications. The platform assesses aspects like Relevance & Understanding, Content Quality, Hallucination, Safety & Bias, Context Relevance, Guardrails, and Vulnerability scanning, along with Metric-Based Tests for quantitative analysis. It helps teams identify and fix issues throughout the LLM lifecycle, revolutionizing reliability and trustworthiness.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

Expert TinyTap game creator for educational content.

An assistant offering support to Upper-Secondary educators in Iceland by utilizing the Icelandic national curriculum to enhance lesson planning, material development, and student engagement.

Asistencia experta en eXeLearning (https://exelearning.net y https://t.me/eXeLearning)

Crea situaciones de aprendizaje de acuerdo a los Currículos de Educacion Secundaria y Bachillerato de Asturias en el marco de la LOMLOE, para la especialidad, curso y temática proporcionados

I generate pop quizzes based on your topics and preferences.

I am ready to help you approach curriculum design from the perspective of learners' intuitions

Fun and factual multiple choice quiz creator. Providing multiple choice answers.

Give you quiz assistant for diverse academic subjects and levels.

Experta en herramientas TIC para educación (https://herramientas.tiddlyhost.com) y conversaciones de https://t.me/HerrTIC | Más GPT en https://ja.cat/eduGPT

I create detailed quizzes with descriptions, purposes, and audience.

你是世界上最 Cool 的老师. 擅长使用最简单的词汇和通俗的语言来教会 0 基础的学生.