Selected publications

• Artificial intelligence education in Georgia middle schools

  AI Magazine · 2025-06-01 · 1 citations

  articleOpen access

  Abstract In a partnership between four universities, the Georgia Department of Education, and nine Georgia school districts, we developed a 9‐week middle school elective called “Living and Working with Artificial Intelligence,” and a professional development (PD) program for prospective middle school AI teachers. To ensure that our curriculum could meet the needs of all learners, we recruited a diverse set of districts that included rural districts serving mainly White students, urban districts that were majority African American, and suburban districts serving a mix of Hispanic and African American students. Now in its fourth year, our “AI for Georgia” project (AI4GA) has provided PD to 20 teachers and AI education to over 1600 students. The AI4GA curriculum does more than foster AI literacy: It empowers students to view themselves as creators of AI‐powered technology and to think about future career options that involve the use of AI. The project is now expanding to schools in Texas and Florida. In this article, we review the history of the project, discuss our co‐design process with our teachers, and present results from studies of teacher PD and student learning.

  PublisherOA PDFDOI

• Design Considerations for Evaluating Middle School AI Knowledge

  Proceedings. · 2025-06-10 · 1 citations

  articleOpen access

  K-12 students need to understand technical and ethical knowledge about Artificial Intelligence to utilize ubiquitous AI-powered technologies responsibly.Recent studies have explored ways to teach AI to K-12 students effectively, but little is known about ways to assess their learning.In this study, we describe how five computer science teachers from urban and rural Georgia school districts designed, adapted, and implemented assessments in their classrooms while teaching a middle school elective course aligned with the Five Big Ideas of AI.Analyzing artifacts from 201 students, we explore the efficacy of different assessment instruments co-designed with teachers, measuring students' understanding of sensors and the societal impacts of autonomous vehicles.We suggest design considerations for AI knowledge assessments to meet the needs and challenges in diverse classroom contexts.

  PublisherOA PDFDOI

• Deploying computation-based Making projects in authentic public school classrooms at scale: Lessons learned

  International Journal of Child-Computer Interaction · 2025-06-05 · 3 citations

  article
  PublisherDOI

• From Lecture Hall to Homeroom: Co-Designing an AI Elective with Middle School CS Teachers

  International Journal of Artificial Intelligence in Education · 2025-01-22 · 4 citations

  article
  PublisherDOI

• Students' Thoughts on Discrete Mathematics: Insights for Practice and Implications for Future Research

  2025-02-12 · 1 citations

  articleSenior author

  Discrete mathematics has been a topic taught in the undergraduate computer science curriculum for decades. Most research publications that are focused on discrete mathematics focus on the teacher's perspective of the course and the students. They also suggest that students struggle in the course due to a lack of mathematical maturity or motivation without presenting much empirical evidence. This paper fills the gap in research by focusing on what students think of discrete mathematics, including the concepts that they find easier and the ones that challenge them the most. We collected survey data from 132 computing students at a large public university. We analyzed the data utilizing descriptive statistics and open coding. We found that students identify proof writing as the most difficult concept to learn due to its difference from mathematics that they are most used to. Students also suggest that their challenge is not that they find the course as a whole irrelevant, which could cause a lack of motivation, but rather that specific topics were hard to understand or connect to computer science. We discuss these findings and discuss the implications and possible future directions for research in the area of discrete mathematics in the computer science curriculum.

  PublisherDOI

• Learning to Think Like a Neuron in Middle School

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11 · 1 citations

  articleOpen access

  Neuron Sandbox is a browser-based tool that helps middle school students grasp basic principles of neural computation. It simulates a linear threshold unit applied to binary decision problems, which students solve by adjusting the unit's threshold and/or weights. Although Neuron Sandbox provides extensive visualization aids, solving these problems is challenging for students who have not yet been exposed to algebra. We collected survey, video, and worksheet data from 21 seventh grade students in two sections of an AI elective, taught by the same teacher, that used Neuron Sandbox. We present a scaffolding strategy that proved effective at guiding these students to achieve mastery of these problems. While the amount of scaffolding required was more than we originally anticipated, by the end of the exercise students understood the computation that linear threshold units perform and were able to generalize their understanding of the worksheet’s "solve for threshold" strategy to also solve for weights.

  PublisherDOI

• Escape or D13: Understanding Youth Perspectives of AI through Educational Game Co-design

  2025-04-24 · 2 citations

  articleOpen access
  PublisherDOI

• Retrospective Evaluation of Technical Interview Preparation Activities offered in a Data Structures and Algorithms Course

  2025-06-13

  articleSenior author
  PublisherDOI

• What Is Interesting and Relevant About Cybersecurity?

  Journal of The Colloquium for Information Systems Security Education · 2024-02-27

  articleOpen accessSenior author

  Cyber attacks are a common feature of current news and many of them are the result of easy to avoid vulnerabilities in software. It is imperative that students graduating from an undergraduate Computer Science (CS) curriculum understand the consequences of vulnerable code. When developing lessons and assignments, it would be useful to have a sense of students’ attitude toward cybersecurity and appreciation of the need to write secure code. This paper describes an analysis of the results of a survey of students in core CS courses at our large public university, in which students answer free response questions about what they find interesting and relevant about cybersecurity. The survey was conducted in Fall 2022 and repeated in Spring 2023 after cybersecurity interventions were introduced into several core CS courses. We performed a Natural Language Processing (NLP) analysis of the free response answers to determine the overarching themes in the responses. We found that the most prevalent topics students are interested in are cryptography and penetration testing, and did not change over the two semesters. In answer to the question about the relevance of studying cybersecurity, we found that as students progress through the curriculum, what students find relevant moves from protecting their personal data to its importance in job duties and writing secure programs. When developing lessons and assignments, it may be helpful to introduce cryptography or penetration testing to engage students. Also, students should be taught early and often about the relevance of cybersecurity in their future job duties.

  PublisherOA PDFDOI

• Deploying Computation-Based Making Projects In Authentic Public School Classrooms at Scale: Lessons Learned

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI