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In this post I want to talk about using generative AI to extend one of my academic software projects—the Python Tutor tool for learning programming—with an AI chat tutor. We often hear about GenAI being used in large-scale commercial settings, but we don’t hear nearly as much about smaller-scale not-for-profit projects. Thus, this post serves as a case study of adding generative AI into a personal project where I didn’t have much time, resources, or expertise at my disposal. Working on this project got me really excited about being here at this moment right as powerful GenAI tools are starting to become more accessible to nonexperts like myself. 

Undergraduate teaching assistants (tutors) are commonly employed in computing courses to help students with programming assignments. Prior research in computing education has reported the benefits of tutoring both for students and for the tutors' own learning. In contrast, recent research that examined actual tutoring sessions has reported that these sessions may be less productive than one might hope, with tutors often just giving students the answers to their problems without trying to teach the underlying concepts. To better understand why tutors may be employing these suboptimal practices, we interviewed ten tutors across early computing courses in higher education to identify their perceived role in these sessions, what stressors and factors influence their ability to perform their job effectively, and what kinds of best practices they learned in their tutor training course. Tutors reported their roles around student learning, gauging student understanding, identifying or providing solutions to students, and providing socioemotional support. They reported their stressors around environmental factors (e.g., number of students waiting to be helped, preparation time, peer-tutor frustrations), internal influences, student behavior, student skill levels, and feeling the need to ''read a student's mind.'' Regarding their tutor training course, Tutors reported learning about interaction guidelines and procedures and question-based problem solving. We conclude by discussing how these results may contribute to the less-effective behaviors seen in prior research and potential ways to improve tutoring in computing courses. 

Over the past decade, data science courses have been growing more popular across university campuses. These courses often involve a mix of programming and statistics and are taught by instructors from diverse backgrounds. In our experiences launching a data science program at a large public U.S. university over the past four years, we noticed one central tension within many such courses: instructors must finely balance how much computing versus statistics to teach in the limited available time. In this experience report, we provide a detailed firsthand reflection on how we have personally balanced these two major topic areas within several offerings of a large introductory data science course that we taught and wrote an accompanying textbook for; our course has served several thousand students over the past four years. We present three case studies from our experiences to illustrate how computer science and statistics instructors approach data science differently on topics ranging from algorithmic depth to modeling to data acquisition. We then draw connections to deeper tradeoffs in data science to help guide instructors who design interdisciplinary courses. We conclude by suggesting ways that instructors can incorporate both computer science and statistics perspectives to improve data science teaching.