Education Innovation
Our Education Innovation research area includes publications on online learning at scale, educational technology (which is any technology that supports teaching and learning), curriculum and programming tools for computer science education, diversity and broadening participation in computer science the hiring and onboarding process at Google.
Recent Publications
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Bidirectional (right-to-left, RTL) layout defects arise when interfaces authored with physical directional CSS (margin-left, border-left, text-align:left) are rendered under dir="rtl" without being mirrored. Accessibility engines (axe-core), actionability engines (Playwright), and even Stylelint's current core logical-property rule systematically fail to localise these defects. We present runtime structural invariants, a zero-golden mode over computed style and geometry, and a bidi-conservative differential mode — that catch them in a headless browser. On a held-out benchmark of 14 real RTL bugs reconstructed from dated fix commits in Bootstrap and Material Components Web, the detector recovers 13/14 (2 via zero-golden, 11 via the differential mode), with no false alarm on any patched version; on genuinely real historical compiled CSS it catches 1 of 2 attempted, using the actual committed bootstrap.rtl.css. On 16 negative LTR-island cases (8 hard) it reports 0 false positives, and both modes report 0 false positives on 43 unmodified real components across four design systems and three RTL architectures (rule-of-three 95% upper bound ≤ 7%). It surfaced a real directional defect : a physical text-align:left in .menu-list in the current Bulma 1.0.4. By contrast, Stylelint's core rule emits an identical 1744 warnings on Bootstrap's LTR and correct-RTL builds, pseudo-localisation detects none of the mirroring bugs, and a naive pixel mirror-and-diff reaches only F1 0.87 with a test-set-tuned threshold, whereas the differential needs none. We find the two modes complementary and scope this paper to detection; repair is future work.
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A Validated Scale Measuring Student Self-Efficacy for Programming with Generative AI
Erin Spaulding
Yekaterina Kharitonova
Paul Denny
Juho Leinonen
Lauren Elizabeth Margulieux
James Prather
Yonggao Yang
Brent N. Reeves
Jamie Benario
Ernest Holmes
2026
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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.
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Unlocking Potential with Generative AI Instruction: Investigating Mid-level Software Development Student Perceptions, Behavior, and Adoption
Daniel Mejia
Jenn Marroquin
Monica Chan
Jamie Benario
Ernest Holmes
2025
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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.
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Bridging Academia and Industry: Leveraging Generative AI in a Software Engineering Course for Practical Industry Experiences
Daniel Mejia
Jenn Marroquin
Jamie Benario
Ernest Holmes
2025
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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.
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Motivated by the necessity of guiding and monitoring students' progress in real-time when assembling circuits during in-class activities we propose BlinkBoard, an augmented breadboard to enhance offline as well as online physical computing classes. BlinkBoard uses LEDs placed on each row of the breadboard to guide, via four blinking patterns, how to place and connect components and wires. It also uses a set of Input/Output pins to sense voltage levels at user-specified rows or to generate voltage output. Our hardware uses an open JSON protocol of commands and responses that can be integrated with a graphical application hosted on a computer that ensures bidirectional communication between each of the students' BreadBoard and the instructor's dashboard and slides. The hardware is affordable and simple, partially due to a customized circuit configured via a hardware description language that handles the LEDs' patterns with minimal load on the Arduino micro-controller. Finally, we briefly show how this hardware made its way to a workshop with high-school students and an undergraduate class in a design department.
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Leveraging Virtual Reality to Enhance Diversity and Inclusion training at Google
Karla Brown
Patrick Gage Kelley
Leonie Sanderson
2024 CHI Conference on Human Factors in Computing Systems, ACM
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Virtual reality (VR) has emerged as a promising educational training method, offering a more engaging and immersive experience than traditional approaches. In this case study, we explore its effectiveness for diversity, equity, and inclusion (DEI) training, with a focus on how VR can help participants better understand and appreciate different perspectives. We describe the design and development of a VR training application that aims to raise awareness about unconscious biases and promote more inclusive behaviors in the workplace.
We report initial findings based on the feedback of Google employees who took our training and found that VR appears to be an effective way to enhance DEI training. In particular, participants reported that VR training helped them better recognize biases and how to effectively respond to them. However, our findings also highlight some challenges with VR-based DEI training, which we discuss in terms of future research directions.
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