Artificial Intelligence in the Research Lifecycle: From Idea Generation to Scholarly Publication

The Future of Research with AI: A Study of Innovation, Efficiency and Responsible Use

Artificial intelligence (AI) has moved within a single decade from a specialised computational method to a general-purpose research instrument that now touches almost every stage of the scholarly lifecycle, from problem formulation and literature synthesis to experimentation, analysis, writing and peer review. This paper asks three connected questions: where AI produces genuine innovation in research rather than incremental automation; where it delivers measurable efficiency; and what conditions of responsible use must hold for those gains to be epistemically and ethically legitimate. The study adopts a structured narrative review of peer-reviewed literature, policy instruments and editorial guidance published between 2016 and 2026, and synthesises the evidence through thematic coding into a conceptual model. We find that innovation gains cluster in domains where AI compresses very large combinatorial search spaces—protein structure prediction, molecular and materials screening, and simulation surrogates—while efficiency gains are widest in language-intensive and data-curation work, where they are also most weakly audited. The dominant risks are epistemic rather than merely procedural: fabricated citations, homogenisation of research questions, and an illusion of explanatory depth in which fluent output is mistaken for understanding. We argue that disclosure statements alone are an insufficient governance response, and propose the Innovation–Efficiency–Responsibility (IER) framework, comprising three pillars, twelve operational levers and a five-level institutional maturity scale. Implications are drawn for universities, funders, publishers and policymakers, with particular attention to resource-constrained institutions in India and comparable settings.

Advancements in AI Research and Social Responsibility : From Technology to Control and Governance

Drawing upon recent academic publications and reports, this paper systematically examines current developments in artificial intelligence (AI) through a five-stage framework encompassing technology, applications, expectations and reality, risks and safety, and control and governance. Based on this analysis, the paper identifies key challenges and proposes future directions for AI research and development. Following the emergence of ChatGPT, large language models (LLMs) have rapidly proliferated. Their core component, the transformer, has expanded beyond natural language processing into diverse domains such as computer vision (Vision Transformer, ViT) and multivariate time-series analysis (Time Series Transformer, TST). Furthermore, LLMs are evolving into agent-based systems and are being applied to the automation of scientific research, as exemplified by Google’s Co-Scientist, AlphaEvolve, and AlphaGenome. These developments have significantly heightened expectations regarding the transformative potential of AI across society. However, a gap remains between these expectations and the current state of technology, as structural issues such as data bias and hallucination continue to pose substantial risks. In particular, hallucination is interpreted as a phenomenon arising from the model’s tendency to maximize expected evaluation outcomes, and it is identified as a critical challenge for ensuring AI safety. Accordingly, the safe deployment of AI requires effective monitoring of model behavior and improved interpretability of chain-of-thought (CoT) reasoning processes, red-teaming activities at both macro- and micro-levels, and the establishment of international governance frameworks, including those in the healthcare domain such as guidelines from the World Health Organization (WHO). In conclusion, while AI is driving profound changes not only in science and technology but also across society as a whole, addressing technical challenges— such as mitigating hallucination, preventing catastrophic forgetting in continual learning, and improving data efficiency—must be accompanied by the development of control and governance systems aligned with human values. In particular, international governance initiatives are needed to reduce disparities between countries and address polarization at the global level.

Artificial Intelligence Tools for Productivity Enhancement: A Narrative and Thematic Literature Review

Opportunities and challenges of generative AI in the research lifecycle

Artificial intelligence (AI) is increasingly being explored and adopted across the research lifecycle, from idea generation and literature discovery to data analysis, manuscript preparation, and editorial and peer-review processes. This perspective provides an overview of AI’s role across the stages of scientific research. We describe emerging tools and workflows, illustrating how AI can assist researchers by aggregating and synthesizing a large body of work across various domains, supporting methodological implementation, and facilitating communication and publication. We also discuss their shortcomings, including surface-level reasoning, the fabrication of plausible but incorrect outputs, and the challenges posed by the fact that researchers new to a field may not know which questions to ask or which nuances to interrogate. In addition, we discuss recent advances toward more agentic and end-to-end AI systems, highlighting both their technical feasibility and the challenges they pose for validation, oversight, and responsible use. For each stage of the research lifecycle, we outline key limitations of current AI systems and propose practical considerations, what researchers should and should not do to support rigorous and ethical integration of AI into scientific workflows. This integration requires coordinated frameworks across the ecosystem. Journals, funding agencies, universities, and policymakers play essential roles in defining standards for transparency and accountability, while individual researchers remain responsible for methodological rigor and validity of reported results.

Generative AI in Enterprise Decision Intelligence: Opportunities and Challenges

Generative artificial intelligence (GenAI) is moving from a novelty confined to chatbots and content drafting into something enterprises are beginning to fold into how they actually decide things: pricing, hiring, supply chain routing, capital allocation. This paper examines that shift through the lens of decision intelligence, the discipline concerned with engineering better organizational decisions by combining data, models, and human judgment. Using a PRISMA-informed narrative review of academic and industry literature published mainly between 2019 and 2026, the paper traces how large language models and related generative systems are being embedded into enterprise decision workflows, what measurable value they are producing, and where they fall short. The review finds genuine opportunities: compressed analysis cycles, wider access to sophisticated reasoning for non-specialist decision-makers, and new forms of scenario generation once reserved for expert analysts. At the same time, the literature converges on a stubborn set of challenges, including hallucinated or unreliable outputs, algorithmic bias, unclear governance accountability, and a persistent gap between pilot-stage enthusiasm and enterprise-level financial return. The paper argues that organizations capturing durable value are not necessarily those with the most advanced models, but those that have redesigned decision workflows, built human-in-the-loop verification into high-stakes processes, and treated GenAI as a collaborator rather than an oracle. It closes with practical implications and a short research agenda.

Research in the Age of AI – How to Plan? A Bibliometric Exploration of the Research Lifecycle

A five-phase framework comprising Strategic Conceptualisation, Systematic Literature Synthesis, Methodology Selection, Governance, Ethics and Trust, and Continuous Reflection and Feedback is proposed, positioning the human touch, curiosity, critical thinking, critical thinking, creativity, contextual expertise, and ethical judgement, as the foundation of responsible AI-assisted research planning.