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SUMMARY:Mitigating AI Misuse in Introductory Python Courses with Graphical
  Programming Tasks - Tristan Bunn
DTSTART;TZID=Pacific/Auckland:20251122T163500
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DTSTAMP:20261007T123627Z
UID:pretalx-kiwi-pycon-xiv-2025-SCASYU@talks.kiwipycon.nz
DESCRIPTION:As large language models (LLMs) reshape how students engage wi
 th programming\, educators face increasing challenges in maintaining acade
 mic integrity (An et al.\, 2025\; McDonald et al.\, 2025). Drawing on the 
 presenter's doctoral research\, this talk explores how visual programming 
 tasks can meaningfully resist unauthorised AI assistance\, offering a robu
 st alternative to conventional text-based exercises that are highly suscep
 tible to automation.\n\nCentral to this approach is Thonny-py5mode -- a cr
 eative coding extension for the beginner-friendly Thonny IDE\, developed a
 s part of the broader PhD research project. It enables graphical output vi
 a py5\, a Processing-inspired Python library for programming interactive g
 raphics\, animations\, and applications (Schmitz\, 2021).\n\nThe talk buil
 ds on findings by McDanel and Novak (2025)\, who observed that LLMs often 
 struggle with assignments involving graphical output\, especially when cor
 rectness hinges on appearance rather than testable outcomes. We present a 
 series of Thonny-py5mode tasks used in undergraduate assessment and evalua
 te the performance of leading LLMs (GPT-4o\, Claude Sonnet 4\, Gemini 2.5)
  in replicating them. This session offers a scalable\, discipline-agnostic
  strategy -- adaptable to other Python graphics libraries -- for promoting
  conceptual understanding\, supporting creativity\, and designing resilien
 t assessments in the GenAI era.
LOCATION:Breakout Room
URL:https://talks.kiwipycon.nz/kiwi-pycon-xiv-2025/talk/SCASYU/
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