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Computer programming represents a rapidly evolving and sought-after career path in the 21st century. Nevertheless, novice learners may find the process intimidating for several reasons, such as limited and highly competitive career opportunities, peer and parental pressure for academic success, and course difficulties. These factors frequently contribute to anxiety and eventual dropout as a result of fear. Furthermore, research has demonstrated that beginners are significantly deterred by the fear of failure, which results in programming anxiety and and a sense of being overwhelmed by intricate topics, ultimately leading to dropping out. This project undertakes an exploration beyond the scope of conventional code learning platforms by identifying and utilising effective and personalised strategies of learning. The proposed solution incorporates features such as AI-generated challenging questions, mindfulness quotes, and tips to motivate users, along with an AI chatbot that functions as a motivational aid. In addition, the suggested solution integrates personalized roadmaps and gamification elements to maintain user involvement. The project aims to systematically monitor the progress of novice programmers and enhance their knowledge of coding with a personalised, revised curriculum to help mitigate the fear of coding and boost confidence.

Microsoft and non-profit educational organization Khan Academy have formed a partnership that will allow all K-12 educators in the US to access the pilot version of Khanmigo for Teachers at no cost. Khanmigo is an AI-powered teaching assistant that can help teachers find ways to make lessons more fun and engaging. The tool can also quickly create lesson plans and suggest student groups for team activities. Khan Academy says Khanmigo can save teachers an average of five working hours every week. The service previously cost educators 4 a month, but Khan Academy has dropped those fees since its Microsoft partnership allows it to use the Azure OpenAI Service to power Khanmigo for free.

Knowledge tracing--where a machine models the knowledge of a student as they interact with coursework--is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has many inherent challenges. In this paper we explore the utility of using Recurrent Neural Networks (RNNs) to model student learning. The RNN family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge, and can capture more complex representations of student knowledge.

Last month, educational psychologist Ronald Beghetto asked a group of graduate students and teaching professionals to discuss their work in an unusual way. As well as talking to each other, they conversed with a collection of creativity-focused chatbots that Beghetto had designed and that will soon be hosted on a platform run by his institute, Arizona State University (ASU). The bots are based on the same artificial-intelligence (AI) technology that powers the famous and conversationally fluent ChatGPT. Beghetto prompts the bots to take on various personas to encourage creativity -- for example, by deliberately challenging someone's assumptions. One student discussed various dissertation topics with the chatbots. Lecturers talked about how to design classes.

In the year since OpenAI released ChatGPT, high school teacher Vicki Davis has been rethinking every single assignment she gives her students. Davis, a computer science teacher at Sherwood Christian Academy in Georgia, was well-positioned to be an early adopter of the technology. She's also the IT director at the school and helped put together an AI policy in March: the school opted to allow the use of AI tools for specific projects so long as students discussed it with their teachers and cited the tool. In Davis' mind, there were good and bad uses of AI, and ignoring its growing popularity was not going to help students unlock the productive uses or understand its dangers. "It's actually changed how I design my projects because there are some times I want my students to use AI, and then there are times I don't want them to," Davis said.

Recently, OpenAI, an AI research laboratory based in San Francisco, announced the launch of its latest AI chatbot, GPT-4. This advanced chatbot has the capability of handling both text and image input, making it more technologically advanced than its predecessor, GPT-3.5. The launch of GPT-4 is expected to usher in a new era of artificial intelligence and its impact on the world. According to OpenAI, GPT-4 is more creative and collaborative than its predecessor, ChatGPT, which was released in 2022. It can handle multiple tasks, such as generating, editing, and collaborating with users on various technical and creative writing tasks, including composing songs, creating screenplays, and analyzing the writing style of a user.

General mathematical reasoning is computationally undecidable, but humans routinely solve new problems. Moreover, discoveries developed over centuries are taught to subsequent generations quickly. What structure enables this, and how might that inform automated mathematical reasoning? We posit that central to both puzzles is the structure of procedural abstractions underlying mathematics. We explore this idea in a case study on 5 sections of beginning algebra on the Khan Academy platform. To define a computational foundation, we introduce Peano, a theorem-proving environment where the set of valid actions at any point is finite. We use Peano to formalize introductory algebra problems and axioms, obtaining well-defined search problems. We observe existing reinforcement learning methods for symbolic reasoning to be insufficient to solve harder problems. Adding the ability to induce reusable abstractions ("tactics") from its own solutions allows an agent to make steady progress, solving all problems. Furthermore, these abstractions induce an order to the problems, seen at random during training. The recovered order has significant agreement with the expert-designed Khan Academy curriculum, and second-generation agents trained on the recovered curriculum learn significantly faster. These results illustrate the synergistic role of abstractions and curricula in the cultural transmission of mathematics.

Becoming a machine learning engineer still isn't quite as straightforward as becoming a web or mobile engineer, as we discussed in Part 1 of this series. This is despite all of the new programs geared toward machine learning both inside and outside of traditional schools. If you ask many people with the title of "Machine Learning Engineer" what they do, you'll often get wildly different answers. The goal of this post is to help you put together the beginnings of a mental semantic tree (Khan Academy's example of such a tree) for learning machine learning (à la Elon Musk's now famous method). As such, this post is probably going to have a bit more lists and hyperlinks than previous (or future) posts in this series. So, based on my own experiences, as well as reaching out to hundreds of machine learning engineers in both academia and industry, here's an overview of the soft skills, basic technical skills, and more specialized skills you'll need.

How much math knowledge do you need for machine learning and deep learning? Some people say not much. Both are correct, depending on what you want to achieve. There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions. At some point in your exploration and mastering of artificial intelligence, you'll need to come to terms with the lengthy and complicated equations that adorn AI whitepapers and machine learning textbooks.