Impact of Generative AI on Learning Programming
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Software engineering and technology (MPSOF), MSc
Publicerad
2024
Författare
Bang, Kenny
Dang, Michael
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Generative AI has become a powerful tool, particularly in code generation for nonprogrammers.
Despite its potential, non-experienced programmers may face challenges
and limited knowledge acquisition.
This study analyzes whether generative AI, specifically ChatGPT, can assist individuals
with limited programming knowledge in solving programming tasks in Java
and if they learn from the experience. Additionally, it examines the strategies participants
use when leveraging ChatGPT 3.5.
A controlled experiment was conducted to observe the effects of ChatGPT on
problem-solving and learning. Participants completed three programming tasks:
a baseline task, a task using ChatGPT, and a final task without ChatGPT to assess
knowledge retention. This revealed varying degrees of success in task completion
with ChatGPT. While participants retained conceptual knowledge, practical application
without ChatGPT remained challenging.
Thematic analysis of participant prompts and exit interviews revealed three main
strategies: learning-focused, task-focused leveraging ChatGPT, and conversational
interaction with ChatGPT. The learning-focused strategy involves using the AI to
gain knowledge but may be ineffective if rushed, hindering comprehension. In contrast,
the task-focused strategy prioritizes task completion but may lead to overreliance
on AI, limiting long-term learning. The conversational strategy is distinct
because it does not serve a specific purpose like the other two. Instead, it involves a
back-and-forth interaction with the AI, where participants build upon ChatGPT’s
previous responses. Overall, participants had a positive attitude towards generative
AI, recognizing both its benefits and limitations. The study highlights the importance
of prior knowledge and effective prompt engineering to formulate informed
prompts and obtain quality responses, thereby improving the overall experience
with AI.
Beskrivning
Ämne/nyckelord
generative AI , problem-solving , learning , engineering , prompt engineering , knowledge retention , Large Language Model (LLM) , ChatGPT