Fred G. Martin

Summer '24 Office Hours email for appointment

Hi, everyone! I am a professor of computer science at the Univerity of Texas at San Antonio. I’m also chair of the department. I started at UTSA on August 1, 2023.

Chair’s Welcome. It is my privilege to serve the Computer Science department as chair. I strive that all have the opportunity to pursue their professional success and to foster an environment where all are respected and valued members of our community.

Future and prospective students (and parents and other family members): please see my FAQs about our undergraduate and graduate programs.

My Research. I create novel technologies to empower young learners as designers with computing, artificial intelligence, and data science. My students and I have designed robotics construction kits, a collaborative data visualization platform, and a virtual reality programming environment.

I work with K–12 teachers and school districts to bring these materials to their students. I collaborate with faculty in computer science, education, and psychology to create new technologies and curricula, and to study how people learn when using them.

My team is called the Engaging Computing Group—see our people, projects, and publications.

My Teaching. In Spring 2024, I taught a small group of students in a research-oriented independent study, CS 4913 Developing AI Tools for K-12. For Fall 2024, I will be teaching combined undergrad/grad version of the same; see catalog numbers CS 4953-901 and CS 5463-902. Students in our MS in AI program may take the graduate section as an elective.

Before UTSA, I was at the University of Massachusetts Lowell. I started in 2002 as an asst. prof. of computer science. After tenure, I did a lot of administrative service, including being an associate dean. Here’s my web page there.

selected publications

* = student under my supervision

Introducing Children to AI and ML with Five Software Exhibits

Saniya Vahedian Movahed*, James Dimino*, Andrew Farrell*, and 10 more authors

In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024

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Artificial intelligence (AI) and machine learning (ML) have a deepening impact in our world. For empowered citizenship and career readiness, elementary and middle school students need to understand these technologies. To provide engaging introductory experiences, we created five original interactive software exhibits introducing children to hands-on activities in AI and ML. The exhibits were tested by 125 elementary and middle school students (aged 7 to 14). Four themes emerged: Students recognized that AI and ML systems can process data from cameras (perception); they saw that these systems responded to their training input (trust); they appreciated the practical import of AI/ML systems (affective and cognitive attitudes); and students were introduced to models and modes (specialization). This paper presents our goals in creating the exhibits and results from our initial testing with children. Our work contributes to the literature on formal and informal activities for introducing AI and ML to young learners.
Envisioning AI for K-12: What Should Every Child Know about AI?

David Touretzky, Christina Gardner-McCune, Fred Martin, and 1 more author

Proceedings of the AAAI Conference on Artificial Intelligence, Jul 2019

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The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA’s national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.
Computational Thinking for Youth in Practice

Irene Lee, Fred Martin, Jill Denner, and 5 more authors

ACM Inroads, Feb 2011

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Computational thinking (CT) has been described as the use of abstraction, automation, and analysis in problem-solving [3]. We examine how these ways of thinking take shape for middle and high school youth in a set of NSF-supported programs. We discuss opportunities and challenges in both in-school and after-school contexts. Based on these observations, we present a "use-modify-create" framework, representing three phases of students’ cognitive and practical activity in computational thinking. We recommend continued investment in the development of CT-rich learning environments, in educators who can facilitate their use, and in research on the broader value of computational thinking.