What Are Deep Research AI Models?

Deep research AI models are tools designed to support complex academic research tasks. Unlike general-purpose AI (like ChatGPT), these tools are fine-tuned for research-specific applications such as:

• Synthesizing large volumes of academic literature

• Identifying relevant papers and evidence

• Suggesting methods, hypotheses, or keywords

• Organizing and streamlining the research workflow

Examples of Deep Research Tools:

• Elicit (https://elicit.org/): An AI research assistant that helps researchers with literature reviews by finding relevant papers, summarizing findings, and structuring comparisons across sources.

• Scite (https://scite.ai/): An AI-powered citation analysis tool that categorizes citations as supporting, contrasting, or mentioning, helping users evaluate source credibility.

• Semantic Scholar (https://www.semanticscholar.org/): A research discovery engine that uses AI to provide contextual summaries of academic papers.

• Consensus (https://consensus.app/): Finds answers to research questions based on scientific literature.

These tools are designed to augment, not replace, human judgment in research.

Understanding Agentic AI:

Agentic AI refers to systems that go beyond responding to prompts—they act autonomously, pursuing goals, breaking them into steps, and adjusting based on feedback.

Key Characteristics of Agentic AI:

• Autonomy: The system can operate without constant user input.

• Planning: Tasks are broken into manageable steps.

• Memory and Context: It can recall and build on previous actions.

• Tool use: May integrate web search, APIs, or databases to complete goals.

Agentic systems are being explored in education and research for tasks like:

• Building annotated bibliographies

• Planning multi-phase projects

• Writing and testing code

• Automating lab report drafting or spreadsheet analysis

Examples of Agentic AI Tools:

• AutoGPT (https://github.com/Torantulino/Auto-GPT): An open-source experimental AI agent that can complete complex tasks with minimal user input.

• LangChain (https://www.langchain.com/): A framework for building AI agents that use tools, memory, and logic to perform research or assist in academic workflows.

• BabyAGI: A simplified agent that iteratively creates and completes tasks toward a user-defined goal.

These tools are still experimental and are best used with caution and human oversight.

Risks, Limitations, and Oversight

While these tools are powerful, they present challenges and risks:

• Accuracy: AI may hallucinate (generate false or misleading information).

• Source Transparency: Some tools don’t provide clear citations.

• Ethics: Use of AI must respect academic integrity guidelines.

• Dependence: Overuse of AI in research can limit critical engagement.

Librarians and faculty can help students critically evaluate the use of AI in their research process and ensure ethical use.