Artificial intelligence and large language models (LLMs) are reshaping scientific research by enabling autonomous research assistants that can perform complex tasks such as literature review, hypothesis generation, and experimental design. A key emerging approach is the use of multi-agent systems, where specialised AI agents collaborate to achieve research goals. Multi-agent
Research agents and tools represent a burgeoning field within artificial intelligence, where autonomous systems leverage large language models (LLMs) and modular architectures to facilitate scientific inquiry and innovation. These agents operate by integrating perception, reasoning, planning, and action capabilities, enabling them to perform tasks such as literature review, hypothesis generation,
Artificial intelligence (AI) agents are broadly defined as computational entities that perceive their environment and act autonomously to achieve specific goals (Russell & Norvig 2020). Foundational work in the field, dating back to its early formalisation, characterises intelligent agents by key properties such as autonomy, reactivity, proactiveness, and social ability,
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
In the European Union, the General Data Protection Regulation (GDPR) serves as a foundational instrument, mandating stringent controls on personal data processing within AI systems (Novelli et al. 2024). Complementary legislation, such as the EU AI Act, introduces risk-based classifications to ensure ethical deployment (European Data Protection Board 2024). In
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.
Generative AI systems rely on vast quantities of text, images and other materials in their training. These models can generate new content, yet the copyright status of both the underlying training data and the outputs produced remains uncertain. Researchers and developers therefore need to understand the differing legal frameworks in
Publishers have recognised both the potential and the risks of generative AI and have formulated policies accordingly. Broadly, these policies emphasise three principles: (1) human authorship – GAI tools cannot be credited as authors; (2) transparency and disclosure – authors must disclose when and how GAI has been used; and (3) accountability
