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Tutorial

The Future of Research: What AI Agents Mean for Academia

Ilya ShabanovEnglishStart here

TL;DR

1-min read

In this tutorial, Ilya Shabanov, creator of EffortlessAcademic.com and a former ecologist who focuses on knowledge management and research workflow design, explains what separates AI agents from general chat assistants such as ChatGPT, and why that matters for research. Using SciSpace Agent, he works through real examples of multi-step tasks that chatbots are not built to handle. The tutorial suits academics and students who want to understand how agents could change the way research is done.

Key takeaways

  • A chatbot mainly answers prompts in conversation, while an agent plans and carries out a sequence of steps toward a goal, such as searching sources, running analyses and assembling outputs.
  • Agents can help generate new research ideas by building on data that already exists, rather than starting from a blank prompt.
  • One demonstrated task is replicating a published paper's methods on your own dataset, a longer, structured job that suits an agent better than a chatbot.
  • Agents can search for publicly available datasets on a topic and compile them into a structured table, but you should open each original page to confirm licensing, documentation and fit.
  • Current agents automate parts of research such as searching, extraction and routine analysis, yet framing questions, designing studies and interpreting results still require human judgement.
  • Researchers who learn to direct and check these tools now are likely to be best placed as agents reshape academic work.
Apply it

To apply this, give SciSpace Agent a concrete multi-step task, such as compiling public datasets on your topic, and review each step of its output against the original sources.

How to do it

  1. Pick a concrete multi-step task

    Choose a task that needs planning and several steps, such as compiling public datasets on your topic. This is where AI agents for academic research differ from chatbots.

  2. Give the goal to SciSpace Agent

    Describe the goal to SciSpace Agent and let it plan and carry out a sequence of steps, such as searching sources, running analyses and assembling outputs.

  3. Compile public datasets into a table

    Ask the agent to find publicly available datasets on your topic and compile them into a structured table. Open each original page to confirm licensing, documentation and fit.

  4. Replicate a paper's methods on your data

    Have the agent apply a published paper's methods to your own dataset, a longer structured job that suits an agent better than a chatbot.

  5. Generate ideas from existing data

    Ask the agent to build new research ideas on data that already exists instead of starting from a blank prompt.

  6. Review every step yourself

    Check each step of the output against the original sources. Framing questions, designing studies and interpreting results still require your judgement.

Watch the tutorial42 min

What AI Agents Mean for Academic Research vs ChatGPTPlay video: What AI Agents Mean for Academic Research vs ChatGPT

Chapters

  1. Welcome and agenda
  2. What are AI research tools and how do they work? LLM vs deep search vs AI agent
  3. How does the SciSpace Agent work? Think of prompts as programs
  4. How to find public research datasets with SciSpace Agent
  5. Reading agent output: to-do list, step logs and generated files
  6. How to write better AI prompts: the who, what, how template
  7. How to find academic conferences for your paper with an AI agent
  8. Other AI agent uses: grant search, grey literature, collaborators and postdoc jobs
  9. How to rewrite your abstract for different conference word limits with AI
  10. Literature review: AI agent vs deep search across Google Scholar, arXiv and PubMed
  11. Can AI make a mind map from research papers?
  12. How to generate novel research ideas from your existing data with AI
  13. Recap: when regular AI, deep search or an agent fits
  14. Why do AI agents fail? Repeated self-editing that burns credits
  15. Why AI errors compound in multi-step agents and how to prevent it
  16. When should you use AI agents for research, and when not?

Try this on SciSpace

Frequently asked questions

What is the difference between an AI agent and ChatGPT?

A general chatbot mainly responds to prompts in conversation. An AI agent can plan and carry out a sequence of steps toward a goal, such as searching sources, running analyses and assembling outputs, often using specialised tools along the way. For research, this means agents can handle longer, structured tasks rather than single answers.

Can AI help me find datasets for my research?

Yes, AI agents can search for publicly available datasets on a topic and summarise them, for example in a table listing source, coverage and access conditions. Always open each dataset's original page to confirm licensing, documentation, quality and whether it suits your research question before using it.

Will AI agents replace researchers?

Current agents automate parts of the research process, such as searching, extraction and routine analysis, but they do not replace the judgement needed to frame questions, design studies, interpret results and take responsibility for findings. Researchers who learn to direct and check these tools are likely to benefit most.

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