Coding with AI: Opportunities and Responsibilities for Researchers: Key Points

The AI Landscape

Artificial intelligence is not as a single capability or system, but as a broad collection of techniques and approaches for solving different kinds of problems.
AI has evolved from early symbolic, rule-based systems (1950s–1970s), to data-driven machine learning, and deep learning, to modern large language models and generative AI (2017–present), culminating in tools like ChatGPT and AI-integrated software.
AI can be grouped into 3 broad categories: Rule-Based and Decision Systems, Predictive and Analytical Systems, and Generative Systems
GPT (Generative Pre-trained Transformer) is a type of large language model built on transformer neural networks, which use attention to process entire sequences and capture context, enabling coherent, context-aware text or code generation.
GPT generates content one token at a time, predicting each new token based on all previous tokens to produce fluent, contextually relevant, and novel outputs.
GPT models are trained in stages: collecting large text datasets, designing the neural network, pre-training on broad data, fine-tuning on task-specific datasets, and iteratively evaluating and improving performance.
ChatGPT is a user-friendly application built on GPT models, while GPT itself is also integrated into other tools like Microsoft Copilot, search engines, and coding environments.

Delegating full software development to AI without understanding the code introduces technical, ethical, and reliability risks.
AI tools should function as assistants, not autonomous developers, supporting learning, debugging, and code generation.
AI can be used as a reference tool to locate, summarise, and clarify technical information more efficiently than traditional search.
Researchers can use AI to explain unfamiliar code line by line, helping them understand programming constructs and libraries.
AI can assist with debugging by explaining errors and suggesting possible fixes, but researchers should independently verify solutions.
AI is useful for generating boilerplate code, performing basic refactoring, and drafting technical documentation, saving time on repetitive tasks.
AI can translate code between programming languages, but outputs must be reviewed for correctness, compatibility, and reproducibility.
Integrating AI into IDEs offers contextual suggestions and autocompletion, but carries risks of over-reliance, limited explanation, and potential privacy concerns.

AI-generated code is not fully reliable: it may contain subtle errors, outdated functions, or fabricated solutions (hallucinations) that compromise research validity and reproducibility.
Vibe coding (AI-assisted coding without planning) can produce messy, error-prone programs. Structured development and verification remain essential.
Using AI tools can create data privacy, confidentiality, and security risks, especially when submitting sensitive datasets or proprietary code to cloud-based AI services.
AI may suggest insecure or outdated coding practices. To mitigate the risk you could embed security expectations in prompts and review outputs critically.
Be aware of the evolving issues surrounding intellectual property, authorship, and citation of AI-generated code.
Over-reliance on AI can lead to de-skilling, reducing researchers’ coding proficiency, critical thinking, and long-term ability to maintain software.
Ethical AI use requires human oversight, responsible data practices, defined boundaries, transparency, and validation of AI-generated outputs.
It could be helpful for researchers to develop personal or team-level AI ethics and security policies.