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The success of GPT with coding tasks has made it important to consider the impact of GPT and similar models on teaching programming. Students’ use of GPT to solve programming problems can hinder their learning. However, they might also get significant benefits such as quality feedback on programming style, explanations of howa given piece of codeworks, helpwith debugging code, and the ability to see valuable alternatives to their code solutions. We propose a newdesign for interactingwith GPT calledMediated GPT with the goals of (a) providing students with access to GPT but allowing instructors to programmatically modify responses to prevent hindrances to student learning and combat common GPT response concerns, (b) helping students generate and learn to create effective prompts to GPT, and (c) tracking how students use GPT to get help on programming exercises. We demonstrate a first-pass implementation of this design called NotebookGPT. 

The recent public releases of AI tools such as ChatGPT have forced computer science educators to reconsider how they teach. These tools have demonstrated considerable ability to generate code and answer conceptual questions, rendering them incredibly useful for completing CS coursework. While overreliance on AI tools could hinder students’ learning, we believe they have the potential to be a helpful resource for both students and instructors alike. We propose a novel system for instructor-mediated GPT interaction in a class discussion board. By automatically generating draft responses to student forum posts, GPT can help Teaching Assistants (TAs) respond to student questions in a more timely manner, giving students an avenue to receive fast, quality feedback on their solutions without turning to ChatGPT directly. Additionally, since they are involved in the process, instructors can ensure that the information students receive is accurate, and can provide students with incremental hints that encourage them to engage critically with the material, rather than just copying an AI-generated snippet of code. We utilize Piazza—a popular educational forum where TAs help students via text exchanges—as a venue for GPT-assisted TA responses to student questions. These student questions are sent to GPT-4 alongside assignment instructions and a customizable prompt, both of which are stored in editable instructor-only Piazza posts. We demonstrate an initial implementation of this system, and provide examples of student questions that highlight its benefits. 

As interest in programming as a major grows, instructors must accommodate more students in their programming courses. One particularly challenging aspect of this growth is providing quality assistance to students during in-class and out-of-class programming exercises. Prior work proposes using instructor dashboards to help instructors combat these challenges. Further, the introduction of ChatGPT represents an exciting avenue to assist instructors with programming exercises but needs a delivery method for this assistance. We propose a revision of a current instructor dashboard Assistant Dashboard Plus that extends an existing dashboard with two new features: (a) identifying students in difficulty so that instructors can effectively assist them, and (b) providing instructors with pedagogically relevant groupings of students’ exercise solutions with similar implementations so that instructors can provide overlapping code style feedback to students within the same group. For difficulty detection, it uses a state-of-the-art algorithm for which a visualization has not been created. For code clustering, it uses GPT. We present a first-pass implementation of this dashboard 

Today, AI tools are generally considered as education disrupters. In this paper, we put them in context with more traditional tools, showing how they complement the pedagogical potential of the former. We motivate a set of specific novel ways in which state-of-the-art tools, individually and together, can influence the teaching of concurrency. The pedagogy tasks we consider are illustrating concepts, creating motivating and debuggable assignments, assessing the runtime behavior and source code of solutions manually and automatically, generating model solutions for code and essay questions, discussing conceptual questions in class, and being aware of in-progress work. We use examples from past courses and training sessions in which we have been involved to illustrate the potential and actual influence of tools on these tasks. Some of the tools we consider are popular tools such as interactive programming environments and chat tools - we show novel uses of them. Some of the others such as testing and visualization tools are in-use novel tools - we discuss how they been used. The final group consists of AI tools such as ChatGPT 3.5 and 4.0 - we discuss their potential and how they can be integrated with traditional tools to realize this potential. We also show that version 4.0 has a better understanding of advanced concepts in synchronization and coordination than version 3.5, and both have a remarkable ability to understand concepts in concurrency, which can be expected to grow with advances in AI. 

As part of a 3-day workshop on training faculty members in concurrency, we developed a module for hands-on training in Java Fork-Join abstractions that had several related novel pedagogical and technical components: (1) Source and runtime checks that (a) tested whether test-aware code created by the trainees met the expected requirements and (b) logged their results in the local file system and the IBM cloud. (2) Editable worked example code along with a guide on how to understand the underlying concepts behind the code and experiment with the code. (3) The ability to follow the guide (a) synchronously, with graduate student help, in a session devoted to this module, and (b) asynchronously, on one's own, before or after the synchronous session. (4) Assignments trainees could do after experimenting with the worked example. (5) Zoom recording of the entire synchronous session. Fourteen faculty members across the country attended the session and had varying amounts of knowledge of Java and automatic assessment. Data gathered from check logs and a Zoom recording, together with novel visualizations of them, provide information to evaluate our pedagogical model and differentiate the participants. 

During the Covid pandemic, we gave a Java assignment that exercised threads, synchronization, and coordination and wrote tests to check each concurrency aspect of the assignment. We used four different technologies to record events related to work on this assignment: the Piazza discussion forum, the Zoom conferencing system, an Eclipse plugin, and a testing framework. The recorded data have given the instructors of the course broad awareness of several aspects of student work: How much time did a student spend on an assignment? How many attempts students made on thread, synchronization, and coordination tests before they reached their final scores? How many times did they go to Piazza or use Zoom-supported office-hour visits to fix concurrency problems, and what was the nature of these problems? How effective was Zoom transcription to classify the office hour problems? How long and effective were the office hour visits, and to what extent was screen sharing used during these visits? To what extent did students use the tests to determine if they had met assignment requirements? These data, in turn, have provided us with preliminary answers to a variety of questions we had about unseen work and the concurrency aspects of the assignment. While the answers may be specific to our assignment, the questions answered by these mechanisms can be expected to apply to other settings. 

We have developed a novel structure for a course on distributed computing suitable for juniors, seniors and graduate students that covers (a) use, design and implementation of state of the art IPC mechanisms, and (b) implementation and experimentation with state of the art consistency algorithms. 

Programming in the large allows composition of processes executing code written using programming in the small. Traditionally, systems supporting programming in the large have included interpreters of OS command languages, but today, with the emergence of collaborative “big data” science, these systems also include cyberinfrastructures, which allow computations to be carried out on remote machines in the “cloud”. The rationale for these systems, even the traditional command interpreters, is human-centric computing, as they are designed to support quick, interactive development and execution of process workflows. Some cyberinfrastructures extend this human-centricity by also providing manipulation of visualizations of these workflows. To further increase the human-centricity of these systems, we have started a new project on cyber training - instruction in the use of command languages and visual components of cyberinfrastructures. Our objective is to provide scalable remote awareness of trainees' progress and difficulties, as well as collaborative and automatic resolution of their difficulties. Our current plan is to provide awareness based on a subway workflow metaphor, allow a trainer to collaborate with multiple trainees using a single instance of a command interpreter, and combine research in process and interaction workflows to support automatic help. These research directions can be considered an application of the general principle of integrating programming in the small and large.