AI coding assistants such as Cursor and GitHub Copilot are increasingly embedded in professional software development workflows. As these tools become more common, computer science education faces the challenge of preparing students both to write code independently and to collaborate effectively with AI-assisted tools. This dual expectation is difficult to address through traditional assignment design.

This poster explores the potential of ChatGPT to replace the traditional approach of pair programming in introductory computer science courses. Traditionally, two students collaborate as a driver and a navigator, periodically switching roles. Now, a student can pair up with ChatGPT, which offers an innovative approach to pair programming. This exploratory activity, which emphasizes collaboration and communication, provides step-by-step instructions for effectively interacting with ChatGPT during pair programming.

Applying various algorithms learned from the courses of Algorithms and Artificial Intelligence to solve real-world problems is intriguing, especially considering that most problems found in textbooks tend to be either trivial, theoretical, or oversimplified. In this poster, we present a challenging project where we apply a variety of common algorithms and their variations to solve the snake game. Our project's primary goal is to develop a perfect snake algorithm, defined as one that collects food until the grid is entirely covered by the snake, indicating the completion of the game, ideally achieved in as few steps as possible. This project is particularly intriguing as it offers a novel approach to completing the game, a task that humans often find challenging.

Tower of Hanoi is a classical tutorial example traditionally for introducing recursive algorithms in CS1. This poster describes a lecture of teaching graph algorithms using the graphical representation of the game and its variants in upper-level courses. More specifically, this poster provides a totally different perspective on solving the tower of Hanoi using DFS, BFS, A*, greedy, and other search algorithms on graphs. Students will not only solve the game and its variants in an inspiring way, but also learn a modeling method to convert problems into graphs.

The efforts for incorporating cultural competence in computer science curriculum will not only benefit the retention of marginalized students, but also, more importantly, educate students with a better understanding of diversity, equity, and inclusion issues. This poster describes a programming lab for learning string matching algorithms and gaining knowledge of cultural competence at the same time. Specifically, this lab requires students to search a group of names from a word search puzzle and then order these names based on various naming conversions from different cultures. Overall, this poster demonstrates an example of infusing cultural competence into a programming lab.

This poster describes an effort to design labs for teaching algorithms through gamification and visualization. In order to make the labs doable and enjoyable, games are crafted under four simple rules. Visualization code is provided for each lab, so that students only need to focus on developing solutions to implement algorithms. Students work in pairs and demonstrate solutions of labs visually using pictures or animations at the end. This experience helps students to engage in the learning process.

An effective dual-channel noise reduction algorithm is proposed based on sparse representations. The algorithm is composed of the following steps. Firstly, overlapping patches sampled from two channels together instead of each channel one by one are trained to be a dictionary via K-SVD. Secondly, OMP(Orthogonal-Matching-Pursuit) reconstruction algorithm is applied to obtain the sparse coefficients of patches using the dictionary. Thirdly, the denoising speech can be obtained by the updated coefficients. Lastly, the above three steps are iterated to get clearer speech until some conditions are reached. Experimental results show that this algorithm performs better than that with single channel.