Lauren Elizabeth Margulieux

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.

As schools struggle to add computer science (CS) curricula and prepare students to be computationally literate, many have become interested in an integrated approach that uses CS concepts and tools to support learning in non-CS classrooms. This paper describes the creation and testing of free, flexible, online professional development resources for teachers who are interested in this integrated approach. The project’s first goal was to design and develop a series of online micro-courses and associated micro-credentials to prepare teachers across disciplines to integrate computing into their classroom activities. The second goal was to implement and examine the effectiveness of these resources to understand better how non-CS teachers learn and apply CS concepts. Results indicated that while participants did identify some barriers to the integration of CS in their classrooms, the professional development significantly increased participants’ general confidence that CS concepts and practices can be integrated into their content areas. Participants also demonstrated a burgeoning understanding of CS concepts and their relation to aspects of their content areas after relatively limited instruction.

This chapter explores the feasibility of integrating AI assistants (e.g., ChatGPT, Gemini) into teachers’ early literacy instruction within the high-leverage teaching practices (HLTP) framework. Rather than developing standalone AI literacy curricula, we propose embedding AI use naturally into existing classroom routines—such as morning meetings, book readings, and snack times—to enhance the quality of teacher–child interactions (e.g., extended conversation, phonological, and vocabulary learning). Unlike a task-oriented approach (e.g., using AI for lesson planning), this approach encourages teachers to model AI usage in real time (e.g., posing questions during ongoing activities) while discussing ethical considerations. Situating AI interactions within authentic classroom contexts not only supports individualized and engaging instruction but also fosters children’s independent and reflective thinking by making ethical AI use personally meaningful. This approach also supports teachers’ development of AI literacy and efficacy without adding new teaching responsibilities to teachers. Teachers’ reflective teaching practices can also be promoted. Drawing on an ongoing observational study of early childhood educators’ AI integration strategies, this chapter offers practical recommendations for implementation.

Self-efficacy is a reliable predictor of academic motivation and achievement across various disciplines and age groups, including computing education. Enhancing self-efficacy through instructional experiences and tools improves motivation and achievement, making it valuable in computing education. To make use of this relationship, educators and researchers must be able to accurately measure how experiences and tools affect self-efficacy. This study replicated and extended the validation of a new self-efficacy measurement for introductory programming students developed by Steinhorst et al. (2020). It replicated features of the original study, such as using other programming-specific self-efficacy measures. The study also introduced measures of general self-efficacy, collected data in new types of courses, collected data for a new programming language, and explored changes in self-efficacy throughout courses to assess validity. The results showed robust internal consistency and construct and convergent validity of the Steinhorst instrument for both introductory programming and data structures courses, aligning with general self-efficacy theory. The findings indicate the Steinhorst instrument's adaptability across different programming languages and contexts. A key insight is the need for researchers and educators to tailor the instrument by excluding items not yet covered in the curriculum to maintain its reliability. This research enhances the understanding of the Steinhorst instrument's robustness for assessing programming self-efficacy across diverse educational settings.

Integrated computing uses computing tools and concepts to support learning in other disciplines while giving all students opportunities to experience computer science. Integrated computing is often motivated as a way to introduce computing to students in a low-stakes environment, reducing barriers to learning computer science, often especially for underrepresented groups. This study explored integrated computing activities implemented in US schools to examine which programming and CT concepts they teach and whether those concepts differed across contexts. We gathered data on 262 integrated computing activities from in-service K-12 teachers and 20 contextual factors related to the classroom, the teacher, and the school. Our analysis revealed that programming and CT concepts were inversely related to five contextual factors, such that factors predicting more CT concepts also predicted fewer programming concepts. These factors reflected school resources, such that wealthier schools used activities with more CT concepts and fewer programming concepts. In addition, factors related to underrepresented groups often related to fewer concepts being taught in activities. School racial composition was the only factor of the 20 that had a relationship with both types of concepts in the same direction-students from underrepresented groups used activities with fewer CT and programming concepts. Our findings suggest that, although integrated computing can potentially introduce computing to a broader audience, we must evaluate what these activities are teaching and to whom. Another primary contribution of this work is an open-access dataset about integrated computing activities, the concepts that they teach, and the contextual factors around their use.