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“It struck me that academic integrity is a serious issue, but one whose treatment I felt was overly punitive. I don’t want us to have to act as police for our students. Students very much want to do the work, but they often are just ignorant, for whatever reason, of what academic standards at the university level are. And so I wanted to instill this kind of restorative justice framework to make moments where students do falter and they do make mistakes, I wanted to turn those into teachable moments where they could learn, and turn what is a bad situation into perhaps a positive one.” —Taiyo Inoue

Today, we speak with Sarah Senk and Taiyo Inoue, co-hosts of My Robot Teacher, which is a podcast affiliated with the California Learning Lab. Sarah and Taiyo discuss how they both bring their respective lenses of comparative literature and mathematics to examine the question and implementation of AI in education, sharing concrete classroom and academic policy uses for LLMs. They touch on academic integrity through a restorative-justice lens, the idea of AI as an opaque cultural archive, and examining higher education as a “slow disaster.” Finally, they end with valuable advice for faculty listening in, giving tips on how to approach AI.

To hear more about Sarah and Taiyo’s thoughts about all things AI and education, listen to their podcast, My Robot Teacher!

“When we talk about cultural memory, we’re thinking about things that no one individual or social group could hold in their minds. It’s the stuff that is recorded in archives, libraries, cultural practices, arts, etc., and so all of that stuff trained large language models. And so I think you can think about large language models as a kind of archive, but a pretty opaque one.”—Sarah Senk

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“I think the biggest thing I would say is just involve students in real work as early as possible. I think sometimes we have in our mind, oh, we cannot do research with students until they’re advanced in their mathematical studies, but I’ve actually found this isn’t true. I think if there’s a compelling project and students are excited about it, they are really great at learning the tools that they need to do it, and that’s something we as faculty can also help with. Students are able to make really meaningful contributions early in their careers. In terms of teaching or mentoring, I think it’s just about teaching thinking, not tools.” —Kamila Larripa

In this episode, we speak with Kamila Larripa, Associate Professor of Mathematics and Data Science Program Lead at Cal Poly Humboldt, along with her former students John Gerving and Jonathan Juarez. Kamila shares about the development of Humboldt’s new Data Science major and its "data for good” mission, as well as her California Education Learning Lab project, which builds a cross-campus community of practice, fosters data literacy, and bring climate justice modules into introductory science courses for students. Students John and Jonathan reflect on their undergraduate research experiences, highlighting how real-world data projects helped identify their interests and build collaboration skills.

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“The driving framework of how I think about equity in my classroom is from a paper by Rochelle Gutiérrez, who is a fairly predominant math educator, about equity being of these two axes: the dominant and the critical. It has four main components—access and achievement—which form the dominant axes, and identity and power, which form the critical axes. I think of these four ideas as guiding the way that I think of equity across every classroom I design.”

In this episode, we speak with Allison Theobold, Assistant Professor of Statistics at Cal Poly SLO. Allison shares her journey from economics to statistics and data science education, and explore her research on equitable pedagogy. She discusses frameworks for equity and how these inform her teaching practices, as well as how her own experiences as a learner in the age of AI help to inform her own teaching.

“For me, a lot of this work comes from me studying and reflecting on how my pedagogy impacts who might be successful in my class, and what types of students may or may not be successful. How can I broaden that more, in terms of assessment, classroom spaces, and access to resources, whether it’s through their peers, me, or outside of class. So thinking about and reflecting on ways in which the way I’m teaching might not be as favorable for some students as opposed to others.”

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“The biggest challenge for us initially was just, where does data science live? Is it in your math department? Is it in your computer science department? Who's going to teach it? Are you going to have a math faculty? Computer science faculty? And then once you decide where it's going to be, then you have to ensure that you have faculty who are willing to teach, because the class is challenging: it does require some programming, as well as statistical analysis, so it's a lot for a faculty. Usually faculty don't have both of those skills, so that's a challenge.”

In this episode, we sit down with Kyla Oh, Acting Dean of Math, Science, and Career Education at Berkeley City College. Kyla shares her unique path from engineering to patent law and now community college leadership. Together, we discuss the evolving role of community colleges in expanding access to data science education, as well as the challenges that come with building out new programs. Kyla discusses the importance of collaboration across departments and institutions as a means of expanding data science across schools, and highlights the power of support programs and internships to keep students motivated.

“I treat my students like clients. If my students are not showing up to class, then I feel like, oh, I'm doing something wrong. And the same with our industry partners—I want to be able to bring in industry partners, so I have to treat them like clients. Like, how can we best serve you and ensure that that partnership is mutually beneficial?”

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“I feel like most practicing teachers grew up in the same educational system that I did where you are penalized for getting the wrong answer, and you kind of get into this flow of needing to have the correct answer. And that has really informed the way that they teach—they're afraid to be wrong. And so the number one thing I work with teachers on is really building up their confidence to be flexible in the classroom.”

In this episode, we sit down with Hannah Kurzweil, STEM educator and Community Manager for Data Science for Everyone. Hannah shares her unconventional journey to STEM teaching and national community-building in data literacy, and reflects on what it means to support teachers in embracing flexibility and designing interdisciplinary curriculum. Together, we discuss the barriers to bringing data literacy into K-12 classrooms and strategies for building stronger educator communities.

“A lot of teachers feel like they're working in silos…a lot of teachers, often because of the median salaries for educators, don't feel like professionals, and that's really hard when you are so passionate about your work and you don't feel like you're able to be a valued member in society for the work that you're doing. And that's why a community of educators is so important, to bring those levels and that sense of community back, and professionalism back, to the classrooms and the classroom teachers.”

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“At Howard, we're looking at having people understand that data is the new oil, right? Everyone has access to it, everyone should be aware of it, everyone should be able to understand where they fall when it comes to their own data, but not knowing that cost. So we want everyone to kind of have a space to say, I'm not a computer scientist, I am not someone who loves statistics, but I want to get involved in this ecosystem of data science, where can I start? And social impact and social justice is where everyone can find a space to begin to understand why data is so important.”

Today, we sit down with Dr. Amy Yeboah Quarkume—also known as Dr. A—Associate Professor at Howard University and Director of Graduate Studies for the Applied Data Science and Analytics program. Dr. A shares her journey from Africana Studies into data science, and how she’s building Howard into a hub for data science, social justice, and environmental justice. Together, they discuss her groundbreaking projects like What’s Up with All the Bias and the HELLO BLACK WORLD curriculum, the importance of addressing “data pollution” in marginalized communities, and how students of all ages can find their way into coding and data science.

“Let's create more space to make mistakes. And even though mistakes cost—because the environmental impacts of all this…there are impacts to what we do—being able to make a mistake and learn should be something that we should continue to encourage. Coding takes practice, it takes patience.”

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“We're all used to tracking changes in Word, so why wouldn't we want to have something like that for our code? And we're all used to Google Docs where we can collaborate in real time, so why wouldn't we want to be doing that with our code too? So both for keeping track of changes and for facilitating collaboration, anyone who I work with, I mentor them in using GitHub”

Welcome to Season 10! To kickoff our new season, we sit down with Jade Benjamin-Chung, an Assistant Professor at Stanford University in the Department of Epidemiology and Population Health, to talk about her journey into public health and becoming a leader in reproducible data science practices. Throughout the episode, we discuss the creation of her lab manual outlining best practices in data science, mentoring in low-resource settings, and promoting ethical data practices.

“If a student isn't able to be part of data collection, then I really encourage them to build a relationship with a local collaborator who knows the data really deeply. For example, I'll have a student who is really bright with coding, but has less experience working with real world data sets. I'll have them pair up with someone from, say, Bangladesh, where I do a lot of research, and they'll kind of mentor them in coding…and the person working in Bangladesh will mentor them in the data”

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“I never thought I would find that sense of community here, especially as a transfer, because I've heard so much about the stereotypes…I think a club really helped combat that” —Avani Gireesha

In our final episode of Season 9, we hear from three graduating UC Berkeley seniors, all of whom transferred from California community colleges into the Data Science major: Avani Gireesha, Hannah Brown, and Jake Pastoria. They reflect on their transitions from community college to Berkeley, discussing the clubs, research, and experiences they’ve gained in their two years here. Listen in as they offer advice for incoming transfer students on how to prepare academically, find community, and get the most out of their Berkeley experience!

“I'm still not really used to the exam rigor here and how difficult it is, but that's totally okay. I feel challenged here, and it really pushes me to get out of my comfort zone and be a better student” —Hannah Brown

“I think these classes change the way that I view education as a whole…I'll never forget opening up my first Data 8 Jupyter notebook and submitting it. Education here is really cool, and I think you should take all these classes, especially when the professors are absolute legends in Berkeley and just computer science and data science in general” —Jake Pastoria

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“There's a famous quote by a statistician, John Tukey, who's often associated with sort of introducing and promoting the concept of exploratory data analysis. And his quote is that the best thing about being a statistician is that you get to play in everyone's backyard, by which he means, as a data scientist, you get to dabble in all of these different areas…the longer you work in statistics, data science and adjacent fields, you really start to see that all these stories around data that come up in different disciplines, they're actually linked through the language of statistics and mathematics. So when I start a new domain, I will usually try to start by reasoning by analogy” —Prof. Alex Franks

In this week’s episode, we talk with Professors Mike Ludkovski and Alex Franks from UC Santa Barbara about their diverse research backgrounds—ranging from stochastic modeling to sports analytics—and how they shaped their approach to data science education. Mike and Alex discuss the value of co-teaching, designing interdisciplinary curriculum, and helping students connect theory to real-world practice. They also touch on some major initiatives aimed at expanding access to data science education, including the Southern California Consortium and the Pacific Alliance for Low-Income Inclusion.

“We found out… the awareness of data science is vastly different across campuses within just a few miles of each other… we are trying to help different places stand up data science courses, programs, and share best practices. We organize events like datathons for high school and community college students” —Prof. Mike Ludkovski

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“Lo nuevo que va a entrar al curso esta vez es la pregunta de qué hacemos con las herramientas de inteligencia artificial en este contexto. ¿Cómo? ¿Cómo usar? Yo no voy a pretender que eso no existe. Yo creo que es absurdo hoy en día imaginarnos que los estudiantes no lo van a usar. Prohibirles usar esas herramientas yo creo que es, es, es fútil. Entonces la pregunta mía es bueno, cómo le creo a los estudiantes un ambiente en el cual sepa que su privacidad está siendo respetada, que tienen acceso a herramientas que pueden usar potencialmente en su propio computador.”

In our second Spanish-speaking episode of the podcast, Eric Van Dusen and special guest host Edwin Vargas Navarro sit down with Fernando Pérez, who is the Faculty Director of the Berkeley Institute for Data Science at UC Berkeley (BIDS), a Professor of Statistics, and co-founder of Project Jupyter and IPython. Fernando reflects on his path from physics to computational science, as well as the role of open-source tools and interactive computing in the development of Juptyer Notebooks. We touch on the evolution of Jupyter and how it furthers interdisciplinary and reproducible collaboration, and discuss Fernando’s teaching philosophy through courses like STAT 159, a course that emphasized reproducibility and collaborative computing. He speaks on the challenges of AI integration in education, and offers broader advice to fellow data science educators on how to approach this quickly-evolving landscape.

En nuestro segundo episodio en español del podcast, Eric Van Dusen y el invitado especial Edwin Vargas Navarro conversan con Fernando Pérez, quien es el Director de Facultad del Berkeley Institute for Data Science (BIDS) en UC Berkeley, profesor de Estadística y cofundador de Project Jupyter e IPython. Fernando reflexiona sobre su camino desde la física hasta la ciencia computacional, así como el papel de las herramientas de código abierto y la computación interactiva en el desarrollo de los Jupyter Notebooks. Tocamos la evolución de Jupyter y cómo promueve la colaboración interdisciplinaria y reproducible, y discutimos la filosofía de enseñanza de Fernando a través de cursos como STAT 159, un curso que enfatizaba la reproducibilidad y la computación colaborativa. Él habla sobre los desafíos de la integración de IA en la educación, y ofrece consejos más amplios a los educadores de ciencia de datos sobre cómo abordar este panorama en rápida evolución.

“Porque si bien la matemática puede ser la misma, el valor de la ciencia de datos es que no es puramente probabilidad estadística o álgebra lineal. Es que esos datos vienen de algún lugar concreto, vienen de una comunidad, vienen de un grupo de personas, se reflejan, reflejan aspectos de ese contexto local y las decisiones que se van a tomar sobre esos datos van a afectar a una comunidad local.”