references.bib

@article{bastani_generative_2025,
  title = {Generative {{AI}} without Guardrails Can Harm Learning: {{Evidence}} from High School Mathematics},
  shorttitle = {Generative {{AI}} without Guardrails Can Harm Learning},
  author = {Bastani, Hamsa and Bastani, Osbert and Sungu, Alp and Ge, Haosen and Kabakc{\i}, {\"O}zge and Mariman, Rei},
  year = 2025,
  month = jul,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {122},
  number = {26},
  pages = {e2422633122},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.2422633122},
  url = {https://pnas.org/doi/10.1073/pnas.2422633122},
  urldate = {2026-10-26},
  abstract = {Generative AI is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human productivity. A key question is how generative AI affects learning---namely, how humans acquire new skills as they perform tasks. Learning is critical to long-term productivity, especially since generative AI is fallible and users must check its outputs. We study this question via a field experiment where we provide nearly a thousand high school math students with access to generative AI tutors. To understand the differential impact of tool design on learning, we deploy two generative AI tutors: one that mimics a standard ChatGPT interface (``GPT Base'') and one with prompts designed to safeguard learning (``GPT Tutor''). Consistent with prior work, our results show that having GPT-4 access while solving problems significantly improves performance (48\% improvement in grades for GPT Base and 127\% for GPT Tutor). However, we additionally find that when access is subsequently taken away, students actually perform worse than those who never had access (17\% reduction in grades for GPT Base)---i.e., unfettered access to GPT-4 can harm educational outcomes. These negative learning effects are largely mitigated by the safeguards in GPT Tutor. Without guardrails, students attempt to use GPT-4 as a ``crutch'' during practice problem sessions, and subsequently perform worse on their own. Thus, decision-makers must be cautious about design choices underlying generative AI deployments to preserve skill learning and long-term productivity.},
  langid = {english}
}

@misc{becker_measuring_2025,
  title = {Measuring the {{Impact}} of {{Early-2025 AI}} on {{Experienced Open-Source Developer Productivity}}},
  author = {Becker, Joel and Rush, Nate and Barnes, Elizabeth and Rein, David},
  year = 2025,
  publisher = {arXiv},
  doi = {10.48550/ARXIV.2507.09089},
  url = {https://arxiv.org/abs/2507.09089},
  urldate = {2026-10-26},
  abstract = {Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24\%. After completing the study, developers estimate that allowing AI reduced completion time by 20\%. Surprisingly, we find that allowing AI actually increases completion time by 19\%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39\% shorter) and ML (38\% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.},
  copyright = {Creative Commons Attribution 4.0 International},
  keywords = {Artificial Intelligence (cs.AI),FOS: Computer and information sciences,Human-Computer Interaction (cs.HC),I.2,Software Engineering (cs.SE)}
}

@article{cui_effects_2026,
  title = {The {{Effects}} of {{Generative AI}} on {{High-Skilled Work}}: {{Evidence}} from {{Three Field Experiments}} with {{Software Developers}}},
  shorttitle = {The {{Effects}} of {{Generative AI}} on {{High-Skilled Work}}},
  author = {Cui, Kevin Zheyuan and Demirer, Mert and Jaffe, Sonia and Musolff, Leon and Peng, Sida and Salz, Tobias},
  year = 2026,
  month = feb,
  journal = {Management Science},
  pages = {mnsc.2025.00535},
  issn = {0025-1909, 1526-5501},
  doi = {10.1287/mnsc.2025.00535},
  url = {https://pubsonline.informs.org/doi/10.1287/mnsc.2025.00535},
  urldate = {2026-10-26},
  abstract = {This study evaluates the effect of generative artificial intelligence (AI) on software developer productivity via randomized controlled trials at Microsoft, Accenture, and an anonymous Fortune 100 company. These field experiments, run by the companies as part of their ordinary course of business, provided a random subset of developers with access to an AI-based coding assistant suggesting intelligent code completions. Although each experiment is noisy and results vary across experiments, when data are combined across three experiments and 4,867 developers, our analysis reveals a 26.08\% increase (standard error: 10.3\%) in completed tasks among developers using the AI tool. Notably, less experienced developers had higher adoption rates and greater productivity gains.             This paper was accepted by Jean-Pierre Dube, marketing.             Funding: M. Demirer and T. Salz thank the MIT GenAI Initiative for funding.             Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2025.00535 .},
  langid = {english}
}

@book{downeyThinkPythonHow2024,
  title = {Think {{Python}}: How to Think like a Computer Scientist},
  shorttitle = {Think {{Python}}},
  author = {Downey, Allen B.},
  year = 2024,
  edition = {Third edition},
  publisher = {O'Reilly},
  address = {Beijing Boston Farnham Sebastopol Tokyo},
  url = {https://greenteapress.com/wp/think-python-3rd-edition/},
  abstract = {This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills},
  isbn = {978-1-0981-5543-8},
  langid = {english},
  keywords = {computer programming,Computer programming,Guides et manuels,Guides manuels etc,handbooks,Handbooks and manuals,Handbooks manuals etc,Programmation (Informatique),Python (Computer program language),Python (Langage de programmation),Python (programmeertaal)}
}

@book{elterSchroedingerProgrammiertPython2021,
  title = {{Schr\"odinger programmiert Python: das etwas andere Fachbuch}},
  shorttitle = {{Schr\"odinger programmiert Python}},
  author = {Elter, Stephan},
  year = 2021,
  series = {{Rheinwerk Computing}},
  edition = {1. Auflage},
  publisher = {Rheinwerk Verlag},
  address = {Bonn},
  isbn = {978-3-8362-6745-8},
  langid = {german},
  file = {/Users/tobi/Zotero/storage/MQGADYC6/Elter - 2021 - Schrödinger programmiert Python das etwas andere .pdf}
}

@book{kwonJuliaProgrammingOperations2019,
  title = {Julia Programming for Operations Research},
  author = {Kwon, Changhyun},
  year = 2019,
  edition = {Second edition},
  publisher = {Changhyun Kwon},
  address = {United States},
  abstract = {This book is neither a textbook in numerical methods, a comprehensive introductory book to Julia programming, a textbook on numerical optimization, a complete manual of optimization solvers, nor an introductory book to computational science and engineering-it is a little bit of all},
  isbn = {978-1-7982-0547-1},
  langid = {english}
}

@book{lauwensThinkJuliaHow2019,
  title = {Think {{Julia}}: How to Think like a Computer Scientist},
  shorttitle = {Think {{Julia}}},
  author = {Lauwens, Ben and Downey, Allen B.},
  year = 2019,
  edition = {First edition},
  publisher = {O'Reilly\textregistered},
  address = {Beijing Boston Farnham Sebastopol Tokyo},
  isbn = {978-1-4920-4503-8},
  langid = {english},
  file = {/Users/tobi/Zotero/storage/VAXEJ9EA/Lauwens and Downey - 2019 - Think Julia how to think like a computer scientis.pdf}
}

@book{matthesPythonCrashCourse2023,
  title = {Python Crash Course: A Hands-on, Project-Based Introduction to Programming},
  shorttitle = {Python Crash Course},
  author = {Matthes, Eric},
  year = 2023,
  edition = {3rd edition},
  publisher = {No Starch Press},
  address = {San Francisco},
  abstract = {Includes instructions for basic concepts such as variables, lists, classes, and loops. Practice exercises come with each topic. The end goal is the creation of a Space Invaders-inspired arcade game to deploy online},
  isbn = {978-1-7185-0270-3},
  lccn = {005.133},
  keywords = {computer programming,Computer programming,COMPUTERS / Computer Science,COMPUTERS / Languages / Python,COMPUTERS / Programming / Open Source,Guides et manuels,handbooks,Handbooks and manuals,Programmation (Informatique),Python (Computer language),Python (Computer program language),Python (Langage de programmation)}
}

@misc{shen_how_2026,
  title = {How {{AI Impacts Skill Formation}}},
  author = {Shen, Judy Hanwen and Tamkin, Alex},
  year = 2026,
  publisher = {arXiv},
  doi = {10.48550/ARXIV.2601.20245},
  url = {https://arxiv.org/abs/2601.20245},
  urldate = {2026-10-26},
  abstract = {AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.},
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International},
  keywords = {Artificial Intelligence (cs.AI),Computers and Society (cs.CY),FOS: Computer and information sciences,Human-Computer Interaction (cs.HC)}
}

@book{thomasPragmaticProgrammer20th2019,
  title = {The Pragmatic Programmer, 20th Anniversary Edition: Journey to Mastery},
  shorttitle = {The Pragmatic Programmer, 20th Anniversary Edition},
  author = {Thomas, David and Hunt, Andrew},
  year = 2019,
  edition = {Second edition},
  publisher = {Addison-Wesley},
  address = {Boston},
  abstract = {"Straight from the trenches, The Pragmatic Programmer, 20th Anniversary Edition, cuts through the increasing specialization and technicalities of modern software development to examine the core process: transforming a requirement into working, maintainable code that delights users. Extensively updated with ten new sections and major revisions throughout, this edition covers topics ranging from career development to architectural techniques for keeping code flexible, adaptable, and reusable. The Pragmatic Programmer illustrates today's best practices and major pitfalls of many different aspects of software development. Whether you're a new coder, an experienced programmer, or a manager responsible for software projects, applying this guide's lessons will help you rapidly improve your productivity, quality, and job satisfaction"--},
  isbn = {978-0-13-595705-9}
}

@book{vanderplasWhirlwindTourPython2016,
  title = {A Whirlwind Tour of {{Python}}},
  author = {VanderPlas, Jake},
  year = 2016,
  edition = {August 2016, First edition},
  publisher = {O'Reilly Media Inc.},
  address = {Sebastopol, Calif.},
  url = {https://jakevdp.github.io/WhirlwindTourOfPython/},
  isbn = {978-1-4919-6465-1},
  langid = {english}
}

@book{wilkeFundamentalsDataVisualization2019,
  title = {Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures},
  shorttitle = {Fundamentals of Data Visualization},
  author = {Wilke, C.},
  year = 2019,
  edition = {First edition},
  publisher = {O'Reilly Media},
  address = {Sebastopol, CA},
  url = {https://clauswilke.com/dataviz/},
  abstract = {"Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization."--Provided by publisher},
  isbn = {978-1-4920-3108-6},
  lccn = {QA76.9.I52 W55 2019},
  keywords = {Data processing,Data Visualization,Datenverarbeitung,Handbooks and manuals,Information visualization,Informationsgrafik,Visual analytics,Visualisierung,Visualization}
}