The proliferation of various generative AI tools raises profound questions about authorship in scientific publications, particularly whether GenAI systems can be considered authors and how responsibility for their output should be shared. Traditional notions of authorship emphasise human agency, accountability, and intellectual contribution, as outlined in established guidelines from bodies
The integration of generative artificial intelligence (GenAI) into research environments has fundamentally transformed how scholars approach knowledge creation, data analysis, and academic writing. As these powerful technologies become increasingly sophisticated and accessible, they offer unprecedented opportunities for enhancing research productivity and enabling novel discoveries. However, their deployment simultaneously introduces complex
Persona-based approaches contextualise AI responses within specific roles, expertise domains, or cognitive frameworks, thereby improving both relevance and quality of generated outputs (Kong et al. 2024). These techniques encompass mega prompts providing extensive contextual information, expert prompts assigning specific professional roles, and advanced reasoning frameworks such as Tree of Thoughts
Example-driven prompts leverage demonstrations to guide large language models (LLMs) in producing precise and contextually relevant outputs, forming a critical component of prompt engineering. This category includes zero-shot prompting, one-shot prompting, and few-shot prompting, each offering varying degrees of guidance for research tasks. These techniques enable researchers to tailor LLM
Prompts are central to human-AI interaction, with their complexity directly influencing the performance of large language models (LLMs). Within prompt engineering, prompts can be categorised by their structural and functional complexity. This section focuses on three core prompt types: short, general questions or instructions; longer, specific questions with defined output
Prompt engineering, the deliberate crafting of inputs to guide large language models (LLMs) towards precise and effective outputs, is pivotal in harnessing AI capabilities across diverse applications, from research to creative tasks. Attention to prompts is essential because poorly constructed inputs can lead to inaccurate, irrelevant, or biased responses, wasting
The design of input prompts is a fundamental aspect of interacting with large language models (LLMs), shaping their ability to deliver precise and meaningful outputs. As these models grow in sophistication, the quality of prompts determines their success in meeting user needs across diverse applications. A clear prompt is essential
The most fundamental component of any prompt is the instruction, which explicitly defines the task the model is expected to perform. A simple query might pose a question, but a well-crafted instruction provides a clear, actionable directive. This distinction is crucial; an instruction such as, “Summarise the following academic article
Prompt engineering is the science and art of crafting, testing, and optimising input queries to guide LLMs towards specific, high-quality outputs. Liu et al. (2023) describe it as an "empirical science" that combines linguistic precision with an understanding of model architectures, transforming human intent into machine action. Unlike
Bias in artificial intelligence systems has become a critical concern as these technologies increasingly influence decision-making across domains such as healthcare, criminal justice, and employment. Bias manifests as systematic errors that lead to unfair or discriminatory outcomes, often disproportionately affecting marginalised groups. Understanding the origins of bias is essential for
Generative Artificial Intelligence (AI) has revolutionised digital media through its ability to synthesise highly realistic content, with deepfake technology standing as one of its most prominent and contentious applications. The term “deepfake,” derived from “deep learning” and “fake,” refers to synthetic media—typically videos or audio—that convincingly depict individuals
Misinformation, defined as false or misleading information disseminated regardless of intent, poses significant challenges to societal trust and democratic processes (Wardle & Derakhshan, 2017). Unlike disinformation, which involves deliberate deception, misinformation encompasses a broader spectrum, including unintentional errors, rumours, and misinterpretations. The advent GenAI models, capable of producing human-like text,
