Article by Ali Shiri: “…There are two categories of emerging LLM-enhanced tools that support academic research:
1. AI research assistants: The number of AI research assistants that support different aspects and steps of the research process is growing at an exponential rate. These technologies have the potential to enhance and extend traditional research methods in academic work. Examples include AI assistants that support:
- Concept mapping (Kumu, GitMind, MindMeister);
- Literature and systematic reviews (Elicit, Undermind, NotebookLM, SciSpace);
- Literature search (Consensus, ResearchRabbit, Connected Papers, Scite);
- Literature analysis and summarization (Scholarcy, Paper Digest, Keenious);
- And research topic and trend detection and analysis (Scinapse, tlooto, Dimension AI).
2. ‘Deep research’ AI agents: The field of artificial intelligence is advancing quickly with the rise of “deep research” AI agents. These next-generation agents combine LLMs, retrieval-augmented generation and sophisticated reasoning frameworks to conduct in-depth, multi-step analyses.
Research is currently being conducted to evaluate the quality and effectiveness of deep research tools. New evaluation criteria are being developed to assess their performance and quality.
Criteria include elements such as cost, speed, editing ease and overall user experience — as well as citation and writing quality, and how these deep research tools adhere to prompts…(More)”.
