About

Diana Franklin is an Associate Professor in Computer Science at the University of Chicago. She earned her Ph.D. at UC Davis in 2002, focusing on computer architecture and new technologies. Her early research included work on intelligent memories, memristors, and quantum computers. In 2008, she transitioned to computer science education research and now leads the CANON (Computing for ANyONe) Lab. Her lab specializes in computer science interventions for 3rd-8th grade students and quantum computing education for novices of all ages, with a particular emphasis on creating more equitable learning experiences. She is also the co-lead of the Q-12 Partnership, an initiative involving the Office of Science and Technology Programs, the National Science Foundation, industry, and professional organizations to advance K-12 quantum information science education. Her research interests center on how students learn computer science concepts, especially at the elementary school level. She explores curriculum design and development environments that can reach a broad spectrum of learners, including underrepresented ethnic and gender minorities and students who struggle academically. Her work in the CANON Lab spans learners from pre-K through college and focuses on equity as a core design constraint alongside learning outcomes. Franklin's projects include creating intermediate Scratch programming curricula, developing quantum computing learning resources and games for middle school learners, and investigating personalized algorithm design problems to increase engagement and belonging. Her research aims to scaffold complex computer science concepts while fostering inclusive and effective learning environments.

Research topics

• Computer Science
• Psychology
• Pedagogy
• Mathematics education
• Sociology
• Political Science
• Mathematics
• Programming language
• Medical education

Selected publications

• Helping Teachers Harmonize: Supporting Teachers in Creating Customized, Localized, and Culturally Relevant Instructional Materials

  Journal of Technology and Teacher Education · 2026-01-01

  article

  Customizing instructional materials to draw on the prior knowledge and lived experiences of students is an important but time-intensive process. Towards accomplishing the goal of creating personalized, localized instructional materials in an efficient and manageable way, this paper introduces the Harmonizing process. Harmonizing is a structured, scaffolded process that helps teachers revise existing instructional materials to better resonate with the identities, interests, and ideas of students while preserving the focal learning goals of the lesson. The paper introduces the motivation and big ideas of the harmonizing approach along with the strategies, technological tools, and scaffolds developed for carrying out the process. To demonstrate the harmonizing process in action, we share examples of teacher-harmonized middle school computer science materials, providing a concrete demonstration of the harmonizing process in action. In doing so, it provides another tool in the toolbelt for teachers and curriculum designers to help align instructional materials with the students who will learn with them.

  PublisherDOI

• Boosting Coding Confidence in Elementary Students: The Impact of ELA-Integrated Computational Thinking Curriculum

  2026-02-13

  articleOpen access

  Integrating literacy and computational thinking (CT) can broaden computer science education participation, especially for multilingual learners. This study examined how the Computing and AI for All (CAIforALL) Act 1 Curriculum, an ELA-integrated Scratch-based CT curriculum, impacts coding attitudes among elementary students in predominantly Latine and multilingual districts. The curriculum integrates literacy strategies into CT instruction as proposed by the National Academies of Sciences, Engineering, and Medicine (NASEM) to support multilingual learners. We conducted a cluster randomized controlled trial with 1,325 students in grades 3–5 across 23 schools in two suburban districts. The treatment group used an ELA-integrated CT curriculum for a school year while controls continued with business-as instruction. Pre- and post-surveys measured five coding attitude constructs: confidence, interest, utility, perceived coding values of social circles, and perceptions of young coders. We estimated treatment effects using a two-level hierarchical linear model, controlling for student and classroom characteristics. Findings show no statistically significant differences emerged in overall coding attitudes between groups. However, students exposed to a year of ELA-integrated CT curriculum showed significant increases in coding confidence. The curriculum did not significantly affect other attitude dimensions. Findings suggest that an ELA-integrated CT curriculum can enhance coding confidence among elementary students, demonstrating the value of early computing exposure and integrated approaches.

  PublisherDOI

• Introducing Quantum Computing to K-12 Teachers through a Professional Development Workshop

  2026-02-13

  articleOpen accessSenior author
  PublisherDOI

• Quander: Student Conceptions of Quantum Concepts from a Gameworld

  2026-02-13

  articleOpen accessSenior author
  PublisherDOI

• Analogical Reasoning in Undergraduate Algorithms

  2026-02-13

  articleOpen accessSenior author

  The ability to identify the important takeaways from a previously-seen solution and apply them in different contexts is an important problem-solving skill. However, this skill, known as analogical reasoning, is traditionally left implicit in algorithms courses. Students are expected to develop the skill naturally as they progress through the course. In this study, we aim to conduct a more thorough investigation of analogical reasoning in algorithms. We integrate explicit metacognitive scaffolds for reflection and schema development into an undergraduate algorithms course. Then, on course exams, we insert an additional task alongside select algorithm design questions, in which students are asked to describe how a previously-seen problem influenced their design. We analyzed both the previously-seen problem selected by the student and the stated similarity. Within the 142 comparisons analyzed, we find that 37% provide insight about the underlying solution structure, and these comparisons were significantly associated with higher scores on the problem. Furthermore, about one-third of the comparisons were with a problem that course staff also selected, and these comparisons were not only much more likely to be structural but were also correlated with higher performance on the question. Our results indicate that the analogical reasoning skills are closely tied to success in the algorithms course, and encourage instructors to integrate explicit demonstrations into their curriculum.

  PublisherDOI

• An Interactive Generative AI Tool to Help Teachers Contextually Customize Scratch Projects

  2026-02-13

  articleOpen accessSenior author

  Teachers are well positioned to customize curricula to local contexts. In CS, one approach involves choosing themes relevant to students and incorporating them into the technical materials (e.g., Scratch projects, CS concept explanations). However, teachers' time constraints make curriculum customization challenging. This poster introduces Conjuror, an interactive GenAI tool to help teachers create contextually customized Scratch projects aligned with a structured curriculum while retaining teacher agency. We present Conjuror design and its pilot with two teacher cohorts, showing promising evidence for process efficiency and output quality.

  PublisherDOI

• Collapsing Qubits: A Quantum Themed Card Game

  2026-02-13

  articleOpen access1st authorCorresponding
  PublisherDOI

• Outcomes from a workshop on a national center for quantum education

  EPJ Quantum Technology · 2025-03-31 · 6 citations

  articleOpen access

  In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions focused on how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the US quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.

  PublisherDOI

• Can GPT Help? Supporting Teachers to Brainstorm Customized Instructional Scratch Projects

  2025-02-12 · 3 citations

  articleOpen accessSenior author

  While many recent studies have explored how large language models can transform computer science instruction from the instructor perspective, they are primarily at the college level. Thus, little is known about using large language models towards curriculum development and teacher supports outside of the college setting. Given the emphasis placed on culturally responsive teaching at the K-8 level and well-documented evidence of insensitive and inaccurate language model outputs from a cultural perspective, it is imperative to perform systematic and principled research before considering their use in this setting.

  PublisherDOI

• Generative AI in Computer Science Education

  Cambridge University Press eBooks · 2025-04-05 · 7 citations

  book1st authorCorresponding

  Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught. Like previous innovations such as high-level programming languages and block-based programming languages, generative AI lowers the technical expertise necessary to create working programs, bringing the power of computation to more people. The programming process is already changing as a result of its presence, even for expert programmers. It also poses significant challenges to educators around re-thinking assessment as some well-established approaches may no longer be viable. Many traditional programming assignments can be completed using generative AI tools with minimal effort, thus potentially undermining learning. In this Element, the authors explore both the opportunities and the challenges for computer science education resulting from the widespread availability of generative AI.

  PublisherDOI

Recent grants

• ScratchEncore: Equity via a Flexible, Culturally-Relevant Advanced Scratch Curriculum for Upper Elementary Diverse Students and Teachers

  NSF · $1.3M · 2017–2022

• CAREER: Horseshoes and Hand Grenades: Exploiting Error Tolerance in Applications

  NSF · $259k · 2008–2012

• Collaborative Research: Adapting Reading Comprehension Strategies for use in Elementary School Computer Science Instruction to Educate Tomorrow?s Computational Innovators

  NSF · $325k · 2017–2022

• Building Quantum Information Science Intuition through Digital Games

  NSF · $1.3M · 2021–2026

• BPC-DP:Animal Tlatoque: A Synergy between Mesoamerican Cultural History and Endangered Species to attract and retain Latina/os and Females in Computer Science

  NSF · $542k · 2010–2014

Frequent coauthors

• Jean Salac

  University of Washington

  51 shared

• David Weintrop

  University of Maryland, College Park

  41 shared

• Donna Eatinger

  University of Illinois Chicago

  40 shared

• Frederic T. Chong

  University of Chicago

  37 shared

• Danielle B. Harlow

  University of California, Irvine

  32 shared

• Jen Palmer

  University of Chicago

  27 shared

• Jennifer Tsan

  24 shared

• Alexandria K. Hansen

  19 shared

Labs

• EPiQCPI

Education

• Ph.D.

  UC Davis

  2002

• B.S.

  California Polytechnic State University, San Luis Obispo

Awards & honors

• 2022 ACM Distinguished Member
• 2020 Best paper award, ICER
• 2019 Keynote Speaker, RESPECT conference
• 2018 Honourable Mention Award, CHI
• 2017 Best paper award, ICER