A paradigm shift is taking place in the field of artificial intelligence (AI), moving from generative models that create synthetic content towards autonomous AI agents. These agents are capable of realising complex, open-ended goals with minimal human intervention (Kolt 2025). An AI agent is fundamentally a computational entity that uses
The proliferation of sophisticated large language models (LLMs) has precipitated a crisis in academic and professional integrity, prompting the development of an array of AI detection tools designed to distinguish machine-generated text from human writing. Systems such as Turnitin AI, GPTZero, and Originality.ai purport to identify the statistical hallmarks
The allure of generative AI's apparent competence has led many researchers to venture into unfamiliar territories, applying these tools to domains where they lack the necessary expertise to critically evaluate the outputs. This phenomenon represents a fundamental departure from traditional research practices, where domain knowledge serves as the
The formulation of research questions and hypotheses constitutes a fundamental aspect of scientific inquiry, providing a structured pathway for investigating phenomena and advancing knowledge. Research questions articulate specific gaps or uncertainties within a domain, whereas hypotheses propose tentative explanations or predictions amenable to empirical scrutiny. These elements ensure methodological rigour
Literature reviews are foundational to academic research, enabling researchers to contextualise their work within existing knowledge. Historically, researchers relied on manual searches and citation chaining, a systematic but labour-intensive method of tracing references backward and forward. The advent of digital databases in the 1990s, such as Web of Science and
Research data, by its very nature, often contains sensitive information that requires careful protection. Whether dealing with personal health records, proprietary research findings, confidential survey responses, or commercially sensitive datasets, researchers must navigate the tension between leveraging the analytical power of GenAI systems and maintaining appropriate levels of data protection.
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
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
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
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
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,
