Over a decade ago, I wrote two articles: ‘A Beginner’s Guide to Computer Science Research’ [1] and ‘How to Start a Research Work in Computer Science: A Framework for Beginners’ [2]. These articles provided foundational guidance to students and early-career researchers on how to navigate the world of academic inquiry in computer science. However, the landscape of research has evolved significantly since 2014, driven by advancements in artificial intelligence (AI), automation, and collaborative tools.
In 2025, pursuing research in computer science is more accessible than ever, thanks to sophisticated AI-powered research assistants, open-access repositories, and real-time collaborative environments. This updated guide aims to provide students and researchers with a structured framework while integrating the latest tools and methodologies.
The Modern Eight-Step Approach to Computer Science & AI Research
- Selecting a Research Area Aligned with Interests and Trends
The first step in research remains unchanged: select a domain that excites you. However, in 2025, staying ahead requires identifying trends in cutting-edge fields such as quantum computing, generative AI, computational sustainability, or blockchain security. Use tools like Google Trends [3], OpenAlex [4], Semantic Scholar [5] and Arxiv-Sanity [6] to explore trending topics and gaps in research.
- Utilising AI-Powered Literature Review Tools
Traditional databases like Google Scholar [7], IEEE Xplore [8], and ACM Digital Library [9] are still relevant, but researchers now have access to AI-driven tools like:
- Elicit.org [10] – Summarises research papers and generates literature insights automatically.
- Research Rabbit [11] – Builds visual research paper networks to track related studies.
- Connected Papers [12] – Shows how different research papers are interlinked.
- Scite.ai [13] – Tracks citations and debates on published research.
These tools significantly reduce the time required to conduct a comprehensive literature review while ensuring that your research remains cutting-edge.
- Efficiently Managing Research Papers and References
Instead of manually storing references, use Zotero, Mendeley, or EndNote to manage citations efficiently. These tools integrate with AI assistants like SciSpace Copilot, which can extract key information and summarise findings from a vast number of papers.
- Reading Research Papers Effectively
A key challenge in research is reading academic papers efficiently. The three-pass approach proposed by S. Keshav [14] is still useful:
- Pass 1: Skim the abstract, introduction, and conclusion.
- Pass 2: Read figures, diagrams, and methodology in detail.
- Pass 3: Dive deep into proofs, experiments, and discussions.
However, researchers in 2025 can leverage AI-based summarisation tools like ChatGPT, Claude AI, and SciSummary [15] to condense long papers into digestible insights.
- Brainstorming and Problem Identification
Innovation begins with problem identification. The key questions to ask remain as follows:
- What is the problem addressed in this research?
- What are its limitations?
- Can AI or computational models improve this research?
Engaging in discussions on Reddit (r/MachineLearning), Arxiv Sanity Preserver, or Twitter/X science communities can provide fresh perspectives and challenges from global researchers.
- Conducting Experiments with Open-Source Tools and Cloud Computing
Unlike in 2014, where researchers often struggled with hardware limitations, today’s computing environment is cloud driven. Some of the best tools available include:
- Google Colab Pro and Kaggle Notebooks – Cloud-based Jupyter environments for deep learning and computational research.
- GitHub Copilot – AI-powered coding assistance for writing research code efficiently.
- Paperspace Gradient [16] – GPU access for AI and machine learning experiments.
- OpenAI API [17] & Hugging Face [18] – Libraries for integrating advanced AI models into research.
For simulation-based research, platforms like SimPy [19] (for event-driven simulation) and AnyLogic [20] (for multi-agent simulation) are indispensable.
- Publishing and Open Science in 2025
The traditional model of publishing in expensive or time consuming, closed-access journals is changing. Open-access platforms such as arXiv, bioRxiv, and ResearchGate have gained traction in recent times. In addition, researchers should explore preprint servers, which allow work to be shared early, facilitating feedback before formal publication.
AI-driven writing tools like Grammarly, DeepL Write, and Writefull assist in improving research clarity and structure. Additionally, AI-based peer review assistants such as Scite Assistant can provide automated feedback before submission.
- Collaborating with the Global Research Community
In the modern research environment, networking is crucial. Platforms like Overleaf (for collaborative LaTeX writing), Notion (for research organisation), and Obsidian (for personal knowledge management) streamline workflows.
Additionally, virtual conferences and online hackathons provide opportunities to showcase research globally. Some popular communities to engage with include:
- AI Alignment Forum – For discussions on AI safety and ethics.
- MLCommons – For benchmarking and standardising machine learning models.
- IEEE DataPort – For accessing open datasets and collaborating on AI-driven projects.
Connecting the Eight Steps
The eight steps in this research framework are interconnected, forming a continuous and iterative research cycle as shown in Figure 1. Selecting a research area guides literature review, which in turn informs effective reading and brainstorming. Problem identification leads to experimentation using modern computational tools, followed by publishing and networking for collaboration. With each research cycle, new questions arise, prompting further refinement of the initial research question, ensuring that the process remains dynamic and evolving in the fast-paced world of computer science and AI.
Figure 1: Block Diagram of “Eight-Step Approach to Research” in 2025
Conclusion
The core principles of research—critical thinking, curiosity, and rigorous experimentation—remain unchanged. However, the methods and tools available in 2025 make research more efficient and accessible. By leveraging AI-powered research assistants, open-source computing resources, and global collaboration platforms, students and researchers can navigate the evolving landscape of computer science research more effectively.
As we move forward, embracing open science and AI-driven methodologies will be crucial in pushing the boundaries of knowledge. Whether you are an undergraduate student exploring your first research project or an experienced researcher expanding your domain, the future of research is now more exciting than ever.
References
- https://ray.yorksj.ac.uk/id/eprint/8605/
- https://www.academia.edu/34540632/How_to_Start_a_Research_Work_in_Computer_Science_A_Framework_For_Beginners
- https://trends.google.com/trends/
- https://openalex.org
- https://www.semanticscholar.org
- https://arxiv-sanity-lite.com
- https://scholar.google.com
- https://ieeexplore.ieee.org/Xplore/home.jsp
- https://dl.acm.org
- https://elicit.com
- https://www.researchrabbit.ai
- https://www.connectedpapers.com
- https://scite.ai
- Keshav, S. How to Read a Paper. ACM SIGCOMM. Accessed January 6, 2014. http://ccr.sigcomm.org/online/files/p83-keshavA.pdf
- https://scisummary.com
- https://www.paperspace.com/artificial-intelligence
- https://platform.openai.com/docs/overview
- https://huggingface.co
- https://simpy.readthedocs.io/en/latest/
Disclaimer
Views expressed above are the author’s own.
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