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Annotated Bibliography
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Research Argument Essay: Annotated Bibliography
References: Four Sources
Baron, J., & Robert, C. (2024). Skills development in the era of AI. World Bank Group. 1-10.
https://documents1.worldbank.org/curated/en/099858406262414962/pdf/IDU143bca00811b081
469e199101e65e709473f8.pdf
This source is more useful as a broader view of the issue and focuses on how AI is
changing skills and training overall in relation to software development. It explains that
AI can level out differences in skill levels but that using AI like that can reduce the value
of certain technical abilities by automating some tasks. It also argues that AI should be
used to support human skills but not replace them, and that training systems need to
adapt to how developers are using AI. This article will mainly be used in the paper in the
sections about skill loss and possible solutions to that issue. It also helps to support the
idea that traditional learning pathways are being disrupted and that organizations need to
rethink how they train junior developers in what is becoming an ever-increasing AIdriven
environment.
Hibi, W. (2025). How AI tools influence the skills of young developers. International Journal of
Multicultural Education, 27(1), 14-32.
https://doi.org/10.5281/zenodo.15728012
This source looks directly at how AI tools are affecting newer programmers, which
makes it one of the relevant sources for this paper. It shows that AI speeds up
processes like coding, improves efficiency, and helps with steps like debuting and overall
problem-solving. At the same time, this article also raises concerns about overreliance
on AI, especially in relation to how using AI tools too much can weaken people’s critical
thinking skills and independent problem-solving skills if they become too used to using AI
to perform those steps for them. This will mainly be used to support the
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argument that while AI improves productivity, it can also interfere with how junior
developers actually learn. It also helps support the idea that a balanced, hybrid
approach to maintain real skill development is needed for these young developers.
Klemmer, J.H., Horstmann, S.A., Patnaik, N., Ludden, C., Burton, C., Powers, C., Massacci, F.,
Rahman, A., Votipka, D., Lipford, H.R., Rashid, A., Naiakshina, A., & Fahl, S. (2024). Using AI
assistants in software development: A qualitative study on security practices and concerns.
Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications
Security, 1-21.
https://doi.org/10.1145/3658644.3690283
This study looks at how developers are actually using AI tools in real-world settings,
which makes it useful for basing major parts of the paper in the current practices. It
shows that AI is widely used across tasks like coding, debugging, and testing, and that
many developers rely on it regularly. The paper also brings up concerns about
inaccurate or insecure code and the need to double-check any AI outputs. This will be
used to support both sides of the argument: first, to show how common and useful AI
tools are, but second, to show why developers also still need strong foundational
knowledge to be able to actually review and validate what the AI produces.
Shekhar, G. (2024). The impact of AI and automation on software development: A deep dive.
Chicago IEEE.
https://ieeechicago.org/the-impact-of-ai-and-automation-on-software-development-a-deep-dive/
This source focuses more on the technical and industry side of AI in software
development. It explains how automation has replaced a lot of manual coding tasks and
made development faster, cheaper, and more efficient overall. It also shows how
developers are shifting away from repetitive work and toward higher-level
decisionmaking. This article will therefore mainly be used in the section about industry
trends and productivity gains in order to show why companies are adopting AI so quickly
even
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though it is a new and highly-debated technology. It also helps to support the
counterargument that AI is changing what skills are valuable but also show that even if
AI can perform some skills that developers still need to understand how these systems
work.
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