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Best AI Tools for Students Learning Development and Engineering

Best AI Tools for Students Learning Development and Engineering
Best AI Tools for Students Learning Development and Engineering.

Our world is rapidly changing, and AI is a big part of that change. Students in development and engineering (and developers already in their careers) need to become proficient in AI tools. Perhaps not experts, but at least enough to understand how AI might or might not apply to a project.

These best AI tools for students can enhance your learning process and productivity, either in school or on the job. The first section describes each tool, and the second section describes how they could fit together into a streamlined workflow. The third section discusses the factors that affect your choice of AI tools for developers.

10 Useful AI Tools for Students (and Developer Advancement)

These ten free AI tools for students are actually free for everyone. They provide a solid foundation to work on a wide range of artificial intelligence (AI) and machine learning projects. By exploring these tools and their documentation, you can gain hands-on experience, develop your skills, and tackle real-world challenges. They are the best AI tools for students and for developers who want to open new job opportunities or prepare for coding interviews.

The AI tools used by students, such as toolkits, notebooks, libraries, and frameworks, are the same tools used by developers in their careers. Each of the top AI tools for students plays a different but complementary role in software development. Their meanings sometimes overlap, and the specific roles may vary depending on the context and technology used.

Imagine you are an architect designing houses in a new subdivision. Frameworks are the pre-defined models, like a bungalow or a two-story, that provide a basic structure and guide your planning. You use a notebook to create your blueprints and sketches, where you experiment and plan the house.

There are two types of components you can use. Toolkits provide prefabricated components such as walls, doors, and windows. They speed up construction, but they may limit design freedom. In contrast, Libraries provide individual building blocks like bricks, pipes, and electrical components. You can use them independently or combine them into larger components.

Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.

  1. Jupyter Notebook: Jupyter Notebook allows you to create documents called notebooks that combine code, text, and visualizations in a single interface. It supports multiple programming languages, including Python, R, and Julia. Jupyter Notebook is widely used in data analysis and exploration, machine-learning prototyping, and educational settings. It promotes reproducibility by capturing code, its output, and your textual explanations in a single file that can be shared with others.
  2. TensorFlow: TensorFlow is an open-source machine-learning framework developed by Google. It provides a wide range of tools and libraries for building, training, and deploying various types of machine learning models, with a focus on deep learning. TensorFlow offers both high-level and low-level APIs, allowing users to choose between ease of use and flexibility. It supports distributed computing, enabling efficient training on multiple machines or GPUs.
  3. PyTorch: PyTorch is an open-source machine-learning framework primarily developed by Facebook’s AI Research lab. It is known for its dynamic computational graph, which provides flexibility in model development and debugging. PyTorch has gained popularity in the research community due to its simplicity, strong support for neural network architectures, and its ability to seamlessly integrate with Python libraries.
  4. Scikit-learn: Scikit-learn is a Python library that provides a robust set of tools for machine learning and data mining. It offers a wide variety of AI algorithms for classification, regression, clustering, and dimensionality reduction, along with utilities for data preprocessing and evaluation. Scikit-learn is designed with a consistent API, making it easy to experiment with different models and compare their performance.
  5. Git: Git is a storage and distributed version control system that tracks changes to files and directories over time. It allows multiple developers to collaborate on a project, merging their changes efficiently. Git provides features such as branching and merging, which enable developers to work on separate features or experiment with code without affecting the main codebase. Platforms like GitHub and GitLab host Git repositories and provide additional features like issue tracking and pull requests.
  6. Docker: Docker is an open-source containerization platform that allows you to package applications and their dependencies into lightweight, isolated containers. Containers provide a consistent and reproducible environment, ensuring that your code runs the same way across different systems. Docker allows you to define the dependencies and configurations of your application in a Dockerfile, making it easy to share and deploy your code on different machines or in the cloud.
  7. OpenAI Gym: OpenAI Gym is a popular toolkit for developing and benchmarking reinforcement-learning algorithms. It provides a collection of environments, ranging from simple text-based games to complex control and robotics tasks. OpenAI Gym offers a simple and unified API to interact with these environments, making it easier to develop and compare different reinforcement-learning algorithms. It also includes evaluation metrics and tools for visualizing agent performance.
  8. Pieces for Developers: Pieces is an AI-powered software tool designed to assist developers throughout their workflow. Its AI copilot offers real-time suggestions and AI assistance, ranging from code generation and debugging to exploring different approaches and improving code quality. Its plugins for browsers, IDEs, Obsidian, and other software tools access the same desktop repository. Consequently, Pieces stays context-aware and persists your conversation across all contexts with integrated AI. Its suggestions include relevant code snippets, code refactoring, alternative approaches, identifying potential errors, and providing explanations for complex concepts. Even if Pieces was treated only as a closed-source or open-source AI chatbot for students to help write code, its ability to add context to your conversations and leverage many of the best LLMs for coding would be very helpful.
  9. Gradio: Gradio provides a rapid-prototyping visual interface for building interactive web demos for machine learning models. It is easy to share demos with instructors and peers, fostering collaboration and knowledge sharing within the classroom or project teams to facilitate communication and instant feedback exchange.
  10. Hugging Face: Hugging Face is a community-driven online platform with a large library of pre-trained transformers and powerful deep-learning models for natural language processing (NLP) tasks. Like the other tools in this list, its resources, such as NLP tools, datasets, and tutorials, are freely available for individual use. These include exploring NLP tasks such as text classification, sentiment analysis, summarizing information, and question answering.

Workflow for an Example Project

I asked the Gemini LLM to define a streamlined workflow that combined these best free AI tools for students. The suggested workflow includes Jupyter Notebook, PyTorch, TensorFlow, Git, Scikit-learn, Docker, OpenAI Gym, and Pieces for Developers. Gradio and Hugging Face could also be included.

Project Setup:

  1. Version Control: Use Git to initialize a repository for your project. This allows you to track changes, collaborate with others, and revert to previous versions if needed.
  2. Environment  Management: Consider using Docker to create isolated environments for your project. This ensures consistent dependencies and avoids conflicts across different machines. Define Dockerfiles specifying the necessary libraries (PyTorch, TensorFlow, Scikit-learn, OpenAI Gym) and their versions.
  3. Experimentation & Documentation: Use Jupyter Notebook with the Pieces plugin as your primary development environment. It allows you to write, execute, and visualize both written and AI generated code interactively, and keep track of your results in a clear and organized manner. You can use it to explore ideas and document your work with rich markdown cells. For example, you can rapidly test and prototype functionalities with TensorFlow or PyTorch. You can visualize and analyze data generated by your agent’s interactions with the Gym environment. It also puts the full power of Pieces at your command as your personal coding assistant.

Development Workflow:

  1. Data Preprocessing & Analysis: Use Scikit-learn for data preprocessing tasks like cleaning, scaling, and feature engineering. You can also use it for exploratory data analysis and model evaluation.
  2. Model Development & Training: Import and use these libraries within your Jupyter Notebook cells to define your model architecture, train it on Gym data, and evaluate its performance. Both offer flexible and powerful tools for neural network creation and optimization. As the core libraries for building your reinforcement learning model, PyTorch might be preferred for its dynamic computational graph and ease of use, while TensorFlow offers scalability and production-ready AI features. If your project involves reinforcement learning, leverage OpenAI Gym to create and interact with simulated environments. It provides various environments for testing and training your reinforcement learning agents.
  3. Version Control & Collaboration: After making changes in your notebook or code, commit them to your Git repository regularly. This allows you to track progress, revert to previous versions, and collaborate with others. The information is also saved in Pieces, which keeps a historical track of your workflow for going back in the past. Pieces can suggest who to ask questions about the code they provided.
  4. Continuous Integration & Deployment: Consider setting up continuous integration and deployment pipelines to automate testing, building, and deployment of your project. This ensures consistency and streamlines the development process.

General Tips:

  • Start small and modular: Break down your project into smaller, manageable tasks and modules. This makes the development process more manageable and easier to debug. You can ask Pieces to troubleshoot your code and debug it for you.
  • Utilize community resources: Take advantage of online communities, forums, and tutorials for each tool and library. They offer valuable support and online learning opportunities. Pieces is open to questions in your IDE (such as Jupyter), your browser, and on the desktop so you can get answers and ask it about research while you continue coding, providing workflow integration across your entire toolchain.
  • Document your work: Use Jupyter notebooks to document your thought processes, code snippets, and results. This will be helpful for you and your collaborators in the future. You can use Pieces’ on-device AI to document your code and add your annotations to its enrichment of the code with its automatic explanations.
  • Test and iterate: Regularly test your code and models to identify and fix issues early on. Be prepared to iterate and adapt your approach based on your findings. Pieces records your workflow and can provide the materials you had been using that relate to the code.

Remember, this is just a general guideline, and the specific way you combine these tools will depend on your project’s unique requirements and goals.

Factors to Consider when Choosing

Choosing the right AI tools for student use depends on several factors. Here are some key considerations to keep in mind when selecting student AI tools for your projects:

  • Project Requirements: Consider the specific requirements of your project. What are you trying to accomplish? Are you working on a machine-learning task, data analysis, natural language processing, or computer vision? Different AI tools for students specialize in different areas, so choose the ones that align with your project goals.
  • Learning Curve and Documentation: Evaluate the learning curve associated with each tool. Consider how easy it is to get started and whether there are comprehensive documentation and tutorials available. Beginner-friendly tools with extensive community support can help you quickly grasp the concepts and start implementing your ideas.
  • Programming Language: Consider the programming language you are comfortable with or wish to learn. Many AI tools for students are available in Python, which is widely used in the AI community. However, there are also tools available in other languages such as R, Julia, or C++. Choose tools that are compatible with your preferred programming language or those that align with your academic program's requirements when learning new languages.
  • Community and Support: Assess the size and activity of the community surrounding the AI tools. Larger communities tend to offer more resources, tutorials, and active forums for seeking help and guidance. Robust community support can be invaluable, especially when you encounter challenges or have specific questions.
  • Integration and Compatibility: Consider how well the AI tools integrate with other libraries and frameworks you may want to use. For example, if you are working with data analysis, check if the tool integrates well with NumPy, Pandas, or SciPy. Compatibility with other tools ensures smooth workflow and enables you to leverage the strengths of multiple libraries.
  • Scalability and Performance: If you anticipate working on large-scale or computationally intensive projects, evaluate the scalability and performance of the AI-powered tool. Some frameworks offer distributed computing capabilities or support for GPUs, which can significantly speed up training and inference processes.
  • Industry Relevance: Consider the relevance of AI capabilities in industry applications and job market demand. Tools that are widely adopted in industry settings can provide you with valuable skills and enhance student employability. Staying updated with popular tools can also give you insights into current trends and advancements in the field.
  • Personal Interest and Future Goals: Lastly, consider your personal interests and long-term goals. Explore future AI tools that align with your interests and career aspirations. If you have a specific area of AI you wish to specialize in, choose tools that are commonly used in that domain to gain relevant expertise.

Considering these factors helps you make informed decisions about which emerging AI technologies to explore. However, only experimentation and hands-on experience with different tools will ultimately help you determine which of the different AI tools for students work best for you.


Other tools could have been included as the best AI tools for engineering students or students who write code for any reason. I focused on Python as the common programming language, but you can input code into Pieces and have it translated into any of the 40+ languages it supports.

Pieces’ ability to translate between languages is one of the main reasons I consider it the best free AI tool for students. Your lesson plans may have different requirements and may use different programming languages. Pieces is your personal intelligent tutor who can move between different languages and explain the code in ways the professor or the textbook didn't tell you, saving you time and making you more efficient.

It's worth noting that while these AI for college students may have prerequisites for learning, they also provide extensive documentation, tutorials, and resources to help beginners get started and learn the necessary concepts.

These resources provide a solid foundation for beginners to understand and apply neural networks and deep learning concepts. They offer a mix of theoretical explanations, practical examples, and hands-on programming exercises to help you gain a deeper understanding of the subject.

Exploring these resources will give you a strong starting point for your learning about AI tools. You can also reference our tips for software engineering students, and explore how Kyle Goben uses Pieces in our University Student User Story.

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