The rise of generative artificial intelligence (GenAI) has sparked a rapid change in computing curricula and teaching approaches. GenAI coding tools can accurately complete assignments, answer test questions, and perform other tasks traditionally associated with learning programming, especially at the introductory level. Because GenAI is still so new, researchers investigating student usage of GenAI have used informal rubrics and questionnaires. To advance, the field needs validated instruments that measure student perception and use of GenAI. This paper presents the development and initial validation of an instrument to measure self-efficacy while using GenAI to learn programming. Self-efficacy is an important construct in education research because it robustly correlates with student success, across disciplines and ages, including undergraduate computing education. Computing education researchers have presented several validated self-efficacy instruments, most recently by Steinhorst et al. in 2020. Critically, this instrument was created before the rise of GenAI’s popularity in 2022. To complement this instrument, we created a GenAI scale similar in style to the Steinhorst self-efficacy instrument, consisting originally of 11 items and revised to 5 items. We report two important findings in this paper. First, we found strong support for the validity of the existing Steinhorst instrument in a new context, specifically an introductory programming course that fully integrates GenAI. Second, the new GenAI scale shows strong internal reliability, discriminant validity with items in the Steinhorst subscales, and criterion validity with students’ GenAI usage patterns. Based on statistical analysis and cognitive probing interviews, we argue for the validity of the five-item scale to measure students’ GenAI self-efficacy in the context of programming.

The rapid adoption of generative AI across the tech industry demands a corresponding evolution in educational practices. By proactively incorporating generative AI, educational institutions can ensure their programs remain relevant and continue to provide students with the skills necessary for career success. This work presents an intro Software Engineering course, Software Development Studio (SDS), designed and implemented by Google in collaboration with faculty, to ensure students acquire industry-relevant skills. The course focuses on integrating generative AI tools into software engineering practices, mirroring the evolving methodologies used by professionals in the field. The curriculum emphasizes practical, real-world projects, providing early undergraduate computer science students hands-on experience using generative AI tools. Data collected during the Spring 2024 semester from students and faculty reveals a positive experience and enhancement of software engineering learning through the integration of generative AI.

Generative AI tools are rapidly evolving and impacting many domains, including programming. Computer Science (CS) instructors must address student access to these tools. While some advocate to ban the tools entirely, others suggest embracing them so that students develop the skills for utilizing the tools safely and responsibly. Studies indicate positive impacts, as well as cautions, on student outcomes when these tools are integrated into courses. We studied the impact of incorporating instruction on industry-standard generative AI tools into a mid-level software development course with students from 16 Minority Serving Institutions. 89% of student participants used generative AI tools prior to the course without any formal instruction. After formal instruction, students most frequently used generative AI tools for explaining concepts and learning new things. Students generally reported positive viewpoints on their ability to learn to program and learn problem-solving skills while using generative AI tools. Finally, we found that students: reported to understand their code when they work with generative AI tools, are critical about the outputs that generative AI tools provide, and check outputs of generative AI tools to ensure accuracy.

Generative AI (GenAI) has seen great advancements in the past two years and the conversation around adoption is increasing. Widely available GenAI tools are disrupting classroom practices as they can write and explain code with minimal student prompting. While most acknowledge that there is no way to stop students from using such tools, a consensus has yet to form on how students should use them if they choose to do so. At the same time, researchers have begun to introduce new pedagogical tools that integrate GenAI into computing curricula. These new tools offer students personalized help or attempt to teach prompting skills without undercutting code comprehension. This working group aims to detail the current landscape of education-focused GenAI tools and teaching approaches, present gaps where new tools or approaches could appear, identify good practice-examples, and provide a guide for instructors to utilize GenAI as they continue to adapt to this new era.

The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed, recent research has shown that LLMs are capable of solving the majority of the assignments and exams we previously used in CS1. In addition, professional software engineers are often using these tools, raising the question of whether we should be training our students in their use as well. This experience report describes a CS1 course at a large research-intensive university that fully embraces the use of LLMs from the beginning of the course. To incorporate the LLMs, the course was intentionally altered to reduce emphasis on syntax and writing code from scratch. Instead, the course now emphasizes skills needed to successfully produce software with an LLM. This includes explaining code, testing code, and decomposing large problems into small functions that are solvable by an LLM. In addition to frequent, formative assessments of these skills, students were given three large, open-ended projects in three separate domains (data science, image processing, and game design) that allowed them to showcase their creativity in topics of their choosing. In an end-of-term survey, students reported that they appreciated learning with the assistance of the LLM and that they interacted with the LLM in a variety of ways when writing code. We provide lessons learned for instructors who may wish to incorporate LLMs into their course.