We present Coding Pathfinder, a platform to help non-programmers learn to code for a specific purpose. This paper explores how we can scaffold generative AI to provide structure and ensure mastery in informal learning settings, introducing a new approach to coding education. In the current iteration of Pathfinder, a user describes the coding task that they are working on. After collecting some details and scoping the project, Pathfinder identifies the skills that the user will master upon successful completion of the project. It then assesses which of the skills our users already has, and designs a personalised learning journey. The guided journey consists of instructions, explanations, tasks and videos. We also incorporate a chat feature so users can ask questions and engage as if they are working with a tutor.

To inspire student engagement in middle school math, we explore the possibility of using generative AI to enhance the creativity of math learning. We present the Math IDE, a math education environment in which students learn about math concepts by building artifacts. We aimed to create a platform in which students can engage with mathematical concepts, create an artifact that embodies the math that they are learning about, and practice their high-level specification skills. In the current iteration of the Math IDE, students can create custom web pages by describing and demonstrating understanding of the math that is involved in the web page. In this short overview, we describe our process and discuss several open questions regarding the design and application of this novel method of math education.

In this paper, we evaluate the most effective error message types through a large-scale randomized controlled trial conducted in an open-access, online introductory computer science course with 8,762 students from 146 countries. We assess existing error message enhancement strategies, as well as two novel approaches of our own: (1) generating error messages using OpenAI's GPT in real time and (2) constructing error messages that incorporate the course discussion forum. By examining students' direct responses to error messages, and their behavior throughout the course, we quantitatively evaluate the immediate and longer term efficacy of different error message types. We find that students using GPT generated error messages repeat an error 23.1% less often in the subsequent attempt, and resolve an error in 34.8% fewer additional attempts, compared to students using standard error messages. We also perform an analysis across various demographics to understand any disparities in the impact of different error message types. Our results find no significant difference in the effectiveness of GPT generated error messages for students from varying socioeconomic and demographic backgrounds. Our findings underscore GPT generated error messages as the most helpful error message type, especially as a universally effective intervention across demographics.