Executable exams assessments where students write code in development environments using computers with digital validation, offer a format more aligned with actual programming practice than traditional paper-based methods. Our previous work established a comprehensive taxonomy characterizing executable exam aspects including timing, feedback mechanisms, submission policies, resources, proctoring, and grading. However, the emergence of powerful generative AI tools like ChatGPT and GitHub Copilot has fundamentally transformed programming education and assessment. These tools can generate complete solutions, explain code, provide debugging assistance, and offer alternative approaches based on natural language descriptions capabilities that directly challenge traditional executable exam designs. Studies demonstrate that large language models correctly solve most introductory programming problems, making conventional assessment methods particularly vulnerable. This work revisits our original taxonomy through the lens of generative AI, examining how each characteristic must adapt to this new reality. We introduce two critical new characteristics: generative AI tool usage (spanning unrestricted, limited, filtered, and restricted approaches) and problem design in the generative AI era (encompassing AI-resistant, AI-accepting, and AI-cooperative question types). Two case studies illustrate practical implementations: a hybrid course maintaining no-AI policies with minimal changes, and an on-campus course adopting AI-resistant problem design. Survey data from CS educators reveal that while most prohibit generative AI during exams, they embrace it for pedagogical purposes. This updated taxonomy provides educators with frameworks to maintain assessment validity while acknowledging the transformative impact of AI on programming education.

Recent advancements in large language model-based generative artificial intelligence (GenAI) tools have transformed computer science education, presenting both opportunities and challenges. A study investigating students' attitudes toward these tools was conducted during an Introduction to Computer Science course. The target of the study was to gauge students' evolving attitudes toward using GenAI tools in the course, before, during and after ChatGPT was gradually assimilated into homework assignments. The study refers to three phases: preliminary phase, assimilation phase, and calibration stage, which currently takes place. Findings show that, in the preliminary phase, students appreciated the efficiency of GenAI tools offered but were concerned about developing a dependency on these tools and about ''cheating''. Findings from the assimilation phase indicate that consistent, guided exposure to GenAI tools positively shifted students' views, alleviating initial concerns and promoting a positive attitude toward using GenAI tools in the course. The targets of the calibration phase are: a) to examine how to leverage independent learning by formulating clear guidelines that can build trust in the technology and help overcome concerns regarding reliability and credibility; b) to check how GenAI can help students in a Introduction to Computer Science course acquire skills such as critical thinking and code comprehension. The study offers insights for educators on the integration of GenAI tools into computer science courses to enhance learning while maintaining academic integrity.

Generative AI has the potential to become disruptive technology for computer science education. Therefore, computer science educators must be familiar with the threats they should deal with and with the opportunities that generative-AI opens for the computer science education community. In the workshop, we explore the integration of several generative-AI tools and applications in computer science education. Activities include lesson design, code development, test design and assessment. We address the students' and the educators' perspectives. In addition, we explore computer science practices and soft skills to be applied with these tools as well as immediate and future applications and implications for computer science education and for the society. AT the end of the workshop, the participants will be able to use these generative AI tools in their daily educational computer science activities and beyond.

Traditionally exams in introductory programming courses have tended to be multiple choice, or "paper-based" coding exams in which students hand write code. This does not reflect how students typically write and are assessed on programming assignments in which they write code on a computer and are able to validate and assess their code using an auto-grading system.

This lightning talk presents insights from preliminary research on attitudes toward executable exams in an Introduction to Computer Science (CS1) course. In an executable exam, during the exam, students work on a computer in a designated programming environment that enables them to compile their code and run unit tests. Although this exam format is authentic and resembles the way students work during their studies and in the industry, it is not a popular format. We implemented executable exams in the 2021 Winter semester final exam and explored the attitudes toward executable exams of three main groups - students, course staff, and industry representatives. In addition, we collected and analyzed data from high school CS teachers, CS and EE students from other academic institutions and CS lecturers around the world. The attitudes are categorized into three aspects: pedagogical, technical and psychological. All groups expressed concerns regarding technical problems related to computer malfunction and plagiarism. In addition, the course staff and the students expressed concern about the change in the exam format. The groups' attitudes are supported by the pedagogical advantages and disadvantages of executable exams they pointed out.

The paper presents a new application of automatic speech processing in the Ambient Assisted Living area, developed in the course of a three year research project. Recording and automatic processing of spoken conversations plays a major role in this solution enabling effective search in a personal audio archive and fast browsing of conversations. Processing of elderly conversational speech recorded by a distant PDA microphone poses a great challenge. The speech processing flow includes transcription, speaker tracking and combined indexing and search of spoken terms and participating speakers identity extracted from the audio. We present the entire application and individual speech processing components as well as evaluation results of the individual components and of the end-to-end spoken information retrieval solution.