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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. 

We present Seq2Parse, a language-agnostic neurosymbolic approach to automatically repairing parse errors. Seq2Parse is based on the insight thatSymbolicError Correcting (EC) Parsers can, in principle, synthesize repairs, but, in practice, are overwhelmed by the many error-correction rules that are notrelevantto the particular program that requires repair. In contrast,Neuralapproaches are fooled by the large space of possible sequence level edits, but can precisely pinpoint the set of EC-rules thatarerelevant to a particular program. We show how to combine their complementary strengths by using neural methods to train a sequence classifier that predicts the small set of relevant EC-rules for an ill-parsed program, after which, the symbolic EC-parsing algorithm can make short work of generating useful repairs. We train and evaluate our method on a dataset of 1,100,000 Python programs, and show that Seq2Parse isaccurateandefficient: it can parse 94% of our tests within 2.1 seconds, while generating the exact user fix in 1 out 3 of the cases; anduseful: humans perceive both Seq2Parse-generated error locations and repairs to be almost as good as human-generated ones in a statistically-significant manner. 

Data scientists often collaborate with clients to analyze data to meet a client's needs. What does the end-to-end workflow of a data scientist's collaboration with clients look like throughout the lifetime of a project? To investigate this question, we interviewed ten data scientists (5 female, 4 male, 1 non-binary) in diverse roles across industry and academia. We discovered that they work with clients in a six-stage outer-loop workflow, which involves 1) laying groundwork by building trust before a project begins, 2) orienting to the constraints of the client's environment, 3) collaboratively framing the problem, 4) bridging the gap between data science and domain expertise, 5) the inner loop of technical data analysis work, 6) counseling to help clients emotionally cope with analysis results. This novel outer-loop workflow contributes to CSCW by expanding the notion of what collaboration means in data science beyond the widely-known inner-loop technical workflow stages of acquiring, cleaning, analyzing, modeling, and visualizing data. We conclude by discussing the implications of our findings for data science education, parallels to design work, and unmet needs for tool development.