We developed GPTutor, a pioneering web application designed to revolutionize personalized learning by leveraging the capabilities of Generative AI at scale. GPTutor adapts educational content and practice exercises to align with individual students' interests and career goals, enhancing their engagement and understanding of critical academic concepts. The system uses a serverless architecture to deliver personalized and scalable learning experiences. By integrating advanced Chain-of-Thoughts prompting methods, GPTutor provides a personalized educational journey that not only addresses the unique interests of each student but also prepares them for future professional success. This demo paper presents the design, functionality, and potential of GPTutor to foster a more engaging and effective educational environment.

Written feedback has long been a cornerstone in educational and professional settings, essential for enhancing learning outcomes. However, multimodal feedback-integrating textual, auditory, and visual cues-promises a more engaging and effective learning experience. By leveraging multiple sensory channels, multimodal feedback better accommodates diverse learning preferences and aids in deeper information retention. Despite its potential, creating multimodal feedback poses challenges, including the need for increased time and resources. Recent advancements in generative artificial intelligence (GenAI) offer solutions to automate the feedback process, predominantly focusing on textual feedback. Yet, the application of GenAI in generating multimodal feedback remains largely unexplored. Our study investigates the use of GenAI techniques to generate multimodal feedback, aiming to provide this feedback for large cohorts of learners, thereby enhancing learning experience and engagement. By exploring the potential of GenAI for this purpose, we propose a framework for automating the generation of multimodal feedback, which we name MuFIN.

Learning new programming skills requires tailored guidance. With the emergence of advanced Natural Language Generation models like the ChatGPT API, there is now a possibility of creating a convenient and personalized tutoring system with AI for computer science education. This paper presents GPTutor, a ChatGPT-powered programming tool, which is a Visual Studio Code extension using the ChatGPT API to provide programming code explanations. By integrating Visual Studio Code API, GPTutor can comprehensively analyze the provided code by referencing the relevant source codes. As a result, GPTutor can use designed prompts to explain the selected code with a pop-up message. GPTutor is now published at the Visual Studio Code Extension Marketplace, and its source code is openly accessible on GitHub. Preliminary evaluation indicates that GPTutor delivers the most concise and accurate explanations compared to vanilla ChatGPT and GitHub Copilot. Moreover, the feedback from students and teachers indicated that GPTutor is user-friendly and can explain given codes satisfactorily. Finally, we discuss possible future research directions for GPTutor. This includes enhancing its performance and personalization via further prompt programming, as well as evaluating the effectiveness of GPTutor with real users.