3 AI Tools To Find/Refine Research Topic in 2025 (FREE Option Included)

TL;DR

Axana refines a research topic by aligning an initial idea with existing literature and reporting relevance percentages that indicate likely research gaps.

Briefing Cornell Notes

Briefing

Picking the right AI tool matters because research-topic refinement depends on whether the tool can (1) judge literature gaps, (2) force a query into sharper, searchable terms, or (3) synthesize sources into a usable draft. Three tools—Axana, Scispace’s deep review feature, and ChatGPT’s “deep research” feature—are presented as complementary options for finding and narrowing a research topic in 2025, with a free option included.

Axana is positioned as a topic-formulation and gap-identification assistant. After logging in with a Google account, users enter their field of study and degree, then feed in an initial direction—such as a supervisor’s suggestion. The example centers on “applications of [a] metazole based ionic liquid as a solvent in medicine.” Once the user clicks “formulate topic,” Axana refines the research question by aligning it with existing literature and highlighting what key areas need attention. The tool outputs a relevance score tied to how much literature already addresses the topic and flags the “research gap” implied by limited coverage. In the example, Axana suggests a more specific direction—focused on the “psychological effect” of ionic-liquid solvents on patient adherence and treatment outcomes—while also providing additional topic options and the rationale for each.

Axana also helps users decide what to pursue next. If a user wants to start a project, the tool offers further exploration areas derived from the refined topic. Selecting an option allows the user to use “three credits” to begin, and Axana can then help generate a complete research proposal by offering improvements to each section based on the user’s first draft. The practical takeaway: Axana is framed as a structured workflow for turning a supervisor hint into a literature-aware, more specific research topic—then moving toward proposal writing.

Scispace is presented as a query-refinement and literature-discovery tool. Its deep review feature prompts users to clarify what aspects of a topic they care about—mechanisms, practical applications, or economic benefits—so the query becomes narrower and more searchable. Using the example of metazole-based ionic liquids across energy storage, pharmaceuticals, and green chemistry, the tool guides users to specify the application focus (e.g., mechanisms vs. performance vs. benefits). From there, it helps generate a list of closely related “seed papers” using the key terminology embedded in the query, supporting literature review building and topic definition.

ChatGPT is offered as a synthesis layer. With its updated “deep research” feature, users can choose whether they want a general overview or recent advancements, whether the focus should be academic research, industrial applications, or both, and whether they need specific examples (like compound names and mechanisms) plus citations. The workflow is described as producing synthesized literature output with references, helping users refine their topic based on how sources connect.

Together, the tools map to a full pipeline: Axana narrows and identifies gaps, Scispace finds and organizes relevant papers, and ChatGPT synthesizes those sources into a draft-ready narrative with citations.

Cornell Notes

The workflow for refining a research topic in 2025 is built around three different AI strengths: gap-aware topic formulation, query-driven literature discovery, and citation-backed synthesis. Axana takes an initial direction (like a supervisor’s suggestion), refines it using literature coverage signals, and highlights research gaps with relevance percentages. Scispace’s deep review feature forces users to clarify what they want to study (mechanisms, practical uses, economic benefits), then returns seed papers aligned to the query’s key terms. ChatGPT’s deep research feature helps turn those sources into an organized literature review draft, with options for academic vs. industrial focus and for including citations. Using all three supports moving from a rough idea to a focused proposal-ready topic.

How does Axana turn a rough supervisor suggestion into a more researchable topic?

Axana starts after login (Google account) and requires inputs like field of study and degree. Users then enter an initial direction—e.g., a supervisor’s suggestion about “applications of metazole based ionic liquid as a solvent in medicine.” After clicking “formulate topic,” Axana refines the topic by checking how studies in the literature have handled the area and by pointing to key focus areas. It also provides a relevance percentage tied to how much literature exists and uses that to signal a research gap. In the example, Axana outputs multiple refined topic suggestions and explains why each is worth exploring, including one with a higher literature coverage (e.g., cognitive processes around drug information delivery) and another framed around a larger gap (e.g., psychological effects on adherence and treatment outcomes).

What does the “relevance” and “research gap” information mean for choosing between topic options?

The relevance percentage functions as a proxy for how much existing research already addresses the topic. A lower relevance implies less coverage and therefore a larger “research gap,” which Axana treats as potential room for future work. In the example, one refined direction shows relatively low literature coverage (reported as 6.92% relevance) and is described as having a “huge gap,” while another direction shows higher coverage (reported as 15.4% relevance) and is framed as more “good to go” because there is enough literature to support study and framing.

How does Scispace’s deep review feature help narrow a broad topic into a targeted query?

Scispace deep review works by asking users to clarify what they want to investigate within the topic. For metazole-based ionic liquids, it prompts questions such as which aspects are most interesting—critical mechanisms, practical applications, or economic benefits. It also steers users toward specifying the application type (e.g., energy storage vs. pharmaceuticals vs. green chemistry). This refinement step makes the query more precise, which then improves the quality of literature retrieval and the relevance of returned seed papers.

What is the purpose of “seed papers” in Scispace’s workflow?

Seed papers act as a starting set of highly related research articles pulled using the key terminologies embedded in the refined query. The transcript emphasizes that these papers help users synthesize information for a literature review and define the research topic in line with degree requirements. Instead of searching broadly, the tool uses the query’s specific terms to surface papers that match the intended angle (mechanism, performance metrics, or application context).

What choices does ChatGPT’s deep research feature offer for producing a usable literature review?

ChatGPT’s deep research feature is described as supporting both overview and detailed synthesis. It prompts users to choose whether they want a general overview or recent advancements, whether the focus is academic research, industrial applications, or both, and whether they need specific examples such as compound names, mechanisms, and performance metrics. It also asks whether the output should include citations or sources suitable for academic use, enabling a draft-ready literature review structure.

Review Questions

If a supervisor suggests a broad topic, what specific inputs and clicks in Axana help convert it into a refined question tied to literature coverage?
How would you use Scispace deep review to decide between studying mechanisms versus economic benefits for the same ionic-liquid topic?
When using ChatGPT deep research, what selection criteria would you set to ensure the output matches your degree needs (academic vs. industrial, overview vs. recent advancements, and citation requirements)?

Key Points

1
Axana refines a research topic by aligning an initial idea with existing literature and reporting relevance percentages that indicate likely research gaps.
2
Axana’s workflow starts with user inputs (field of study and degree) and an initial direction such as a supervisor’s suggestion, then uses “formulate topic” to generate narrowed options.
3
Axana can support project planning by offering further exploration areas and using “three credits” to help generate a complete research proposal from a first draft.
4
Scispace’s deep review narrows topics by prompting users to specify the angle—mechanisms, practical applications, or economic benefits—before searching for literature.
5
Scispace returns “seed papers” selected using key terminology from the refined query, supporting literature review development.
6
ChatGPT’s deep research feature helps synthesize literature into structured output, with options for academic vs. industrial focus and for including citations.
7
Using all three tools together creates a pipeline from topic gap identification (Axana) to targeted paper discovery (Scispace) to citation-backed synthesis (ChatGPT).

Highlights

Axana uses literature-aware relevance percentages to flag where research is already crowded versus where a gap may exist, turning supervisor hints into sharper questions.

Scispace deep review narrows a topic through guided prompts (mechanisms, practical uses, economic benefits) and then surfaces seed papers tied to those exact terms.

ChatGPT deep research can be configured to produce either broad overviews or recent advancement summaries, including citations for academic use.

Topics

Research Topic Refinement
Literature Gap Analysis
Query Narrowing
Seed Papers
Citation Synthesis

Mentioned

Dr Rizwana Mustafa