Artificial intelligence is changing how researchers approach everything from literature searches and manuscript preparation to peer review and scientific discovery. But for academics, using AI effectively requires more than finding a tool that can generate fluent text.
Researchers need to know whether an AI can work with current evidence, understand the context of a manuscript, verify references, identify methodological weaknesses, and provide feedback that is actually useful.
Explore Science AI approaches that challenge with Rosa, its AI co-scientist chat partner. Rather than functioning as a general-purpose chatbot that simply responds to a prompt, Rosa works within the context of a research project, using the manuscript, review, references, and other supporting materials to help researchers interrogate and improve their work.
So, what actually happens when an academic researcher uses Rosa? Here's a step-by-step look at the workflow.
How Explore Science AI's Rosa Works for Academic Researchers
Rosa is part of Explore Science's broader scientific research platform. The company describes the underlying system as a multi-phase architecture that combines multiple frontier AI models, dynamic literature retrieval, scientific agents, memory systems, and source-level verification.
That architecture is important because scientific research is rarely a one-question, one-answer process. A researcher may need to understand a manuscript, investigate a reference, challenge a conclusion, compare findings with newer literature, and then decide how the paper should change.
Rosa is designed to support that ongoing conversation.
A Step-by-Step Look at the Rosa Research Workflow
At a high level, the experience looks like this:
- Upload your manuscript and supporting research materials.
- Explore Science analyzes the work using its scientific research architecture.
- The system retrieves and verifies relevant literature.
- Your manuscript receives a structured review and Calibre score.
- Feedback is tied to specific passages and issues in the paper.
- You use Rosa to question, challenge, and explore that feedback.
- You refine the research iteratively with the same project context.
Let's look more closely at each stage.
1. Start With Your Manuscript and Research Context
The process begins with the research itself.
Explore Science's review system is built around the researcher's manuscript rather than an isolated question copied into a chatbot. The platform can analyze the paper in depth, while Rosa subsequently works from the manuscript, review, references, and additional materials available within the project.
That context changes how an AI assistant can be used.
Instead of repeatedly pasting sections of a paper and explaining what they mean, a researcher can ask questions about the work within its existing research context. For example, you might ask whether a particular conclusion is adequately supported, why a methodological issue was flagged, or what evidence lies behind a specific review comment.
The goal is to make the conversation about your research, rather than about a generic piece of text.
2. Multiple AI Models Work Behind the Scenes
Rosa is not presented as a single large language model working alone.
Explore Science says its architecture orchestrates a mixture of frontier models, including Claude, GPT, Gemini, Mistral, and Grok, alongside its own Explorer One model. The system routes individual tasks to the model it considers best suited to that task and cross-checks outputs across the broader architecture.
For a researcher, the important point is not which model happens to generate an individual response. You don't select one model and build your workflow around it.
Instead, Explore Science handles the orchestration while the researcher interacts with the resulting research system.
The platform says this architecture is intended to reduce dependence on the strengths and weaknesses of any single model and to provide a more consistent research-oriented analysis.
3. Current Literature Is Retrieved During the Analysis
One of the most important distinctions between Explore Science and a basic chatbot workflow is its use of live literature retrieval.
Explore Science says its system retrieves literature at runtime rather than relying exclusively on information contained in a model's original training data. Its architecture also incorporates novelty assessment against the published research corpus.
This matters because scientific knowledge does not stop at an AI model's training cutoff.
A manuscript may cite important work from several years ago while newer research has challenged, refined, or expanded those findings. Being able to bring current literature into the analysis gives researchers another way to test whether their argument still holds up against the available evidence.
4. References Are Checked Against Live Sources
Citation reliability is another critical part of the workflow.
Explore Science says every reference is validated against academic databases using persistent identifiers such as a DOI where available. If a reference cannot be resolved, the system says it is discarded before appearing in the review or literature search.
The platform also says users can click citations in outputs and reach the underlying paper.
This is particularly relevant because fabricated or inaccurate references are one of the major risks of using generative AI for academic work. A fluent answer is not enough if the sources supporting it cannot be found.
That said, verification should still be viewed as part of a researcher's checking process, not as permission to stop evaluating sources independently. Researchers remain responsible for determining whether a cited study actually supports the argument being made.
5. Your Manuscript Receives a Calibre Score
After the deeper review, Explore Science provides a Calibre score from 0 to 100.
Importantly, the current scoring system is more sophisticated than a simple three-factor assessment. Explore Science says the score combines two independent judgments: Quality and Design Weight.
Quality evaluates how well the paper is executed, including rigor, clarity, transparency, and the validity of its conclusions. It is built from issues and merits across eight categories:
- Research Design
- Data and Evidence
- Analytical Approach
- Scholarly Grounding
- Reporting Quality
- Interpretive Rigor
- Ethical Conduct
- Contribution
Design Weight assesses how much evidential weight the study design carries for the claims being made, with the appropriate standard depending on the research field. The two assessments are then combined into an adjusted score.
This is useful because scientific quality cannot always be reduced to whether a paper is well written.
A manuscript might be polished but have a serious methodological problem. Another might use a demanding study design but need work on reporting or interpretation. The Calibre system is designed to make those distinctions visible.
6. The Review Goes Beyond a Single Number
The score is only one part of the output.
Explore Science says every issue and merit identified during review is connected to its exact location in the manuscript. That means researchers can move from a high-level assessment to the specific passages that require attention.
This makes the feedback more actionable.
Instead of simply learning that a paper has weaknesses in its analytical approach, for example, the researcher can see which part of the manuscript prompted the concern and examine the reasoning behind it.
The platform's review also identifies strengths, meaning researchers can see what the manuscript is doing well rather than receiving a list of problems alone.
7. Ask Rosa What the Feedback Means
This is where Rosa becomes particularly useful.
Once the review is available, researchers can open a conversation with Rosa and ask questions about the analysis. Explore Science describes her as a co-scientist that has the manuscript, review, and references available as context.
You could ask:
"Why did the review flag this conclusion?"
Or:
"What evidence supports this concern?"
You could also challenge the assessment:
"I don't agree with this criticism. What evidence led you to this conclusion?"
That conversational element matters because a research review is not necessarily self-explanatory. Researchers may agree with a criticism, disagree with it, or simply need more evidence before deciding what to change.
Rosa is designed to let them investigate rather than simply accept the initial output.
8. Go Back to the Evidence
Rosa's workflow is also designed around evidence rather than simply generating another paragraph of prose.
The platform says that when Rosa answers a question, she can read, quote, and cross-check the relevant material before responding. Her interface shows the research process, including manuscript reading and reference verification.
That creates an important distinction between asking an AI to "rewrite this paragraph" and asking an AI research partner why a particular claim is vulnerable.
The latter can lead back to the manuscript, the review, and the underlying evidence.
For researchers, that is often where AI assistance becomes more valuable: not when it replaces the thinking, but when it helps make the thinking process easier to interrogate.
9. Refine the Manuscript and Keep the Research Context
Research rarely improves in a single pass.
A researcher may make a revision, reconsider a claim, add a reference, change the interpretation, or discover that an issue raised in the review affects another section of the manuscript.
Explore Science says each paper receives its own workspace that keeps versions together, allowing researchers to track how their work develops. Rosa can then continue working with that research context.
That creates a more continuous workflow than repeatedly starting new conversations with a general AI tool.
The objective isn't simply to produce a better-looking manuscript. It is to help the researcher systematically work through the issues that affect the strength of the research.
What Happens When AI and Human Judgment Meet?
Even a sophisticated scientific AI does not remove the need for the researcher.
Explore Science explicitly says its review does not tell researchers whether a paper is ready to submit. That decision belongs to the researcher and, ultimately, the editor. The platform instead aims to surface issues that could affect a manuscript's prospects, including unsupported claims, methodological gaps, scope problems, journal-fit issues, and conclusions that go beyond the evidence.
That distinction is important.
Rosa can identify a problem. It cannot assume responsibility for deciding whether a scientific claim is ultimately correct.
The researcher still needs to evaluate the evidence, understand the field, check the analysis, consider alternative interpretations, and take responsibility for the final manuscript.
In other words, Rosa works best as a co-scientist, not a substitute scientist.
What About Unpublished Research and Intellectual Property?
Privacy is naturally a major concern when researchers upload unpublished work.
Explore Science says it does not train AI models on user manuscripts. Its current data-handling documentation also says uploaded files go directly to encrypted cloud storage, are analyzed through a mixture of in-house and third-party AI providers, and are stored encrypted at rest after analysis. Access is limited to the user and authorized collaborators.
The company also notes that, in some cases, data may be retained for up to 30 days for safety checks, separately from training systems, before being discarded.
For researchers working with sensitive or unpublished material, reviewing the current privacy and data-handling policies remains an important step before using any external AI service.
Can Rosa Replace Peer Review?
No, that isn't the most useful way to think about it.
Explore Science reports that 90% of its users rate its output as equal to or better than human peer review. The company nevertheless positions the system as a way to strengthen the manuscript before it reaches human reviewers rather than as a replacement for the scholarly publishing process.
That makes practical sense.
Human peer review remains an important part of scholarly communication. An AI-assisted review can instead provide another layer of scrutiny before submission, allowing researchers to identify and address weaknesses while they still have an opportunity to revise the paper.
The result can be a stronger manuscript entering the human review process.
Where Rosa Fits Into the Bigger Explore Science Platform
Rosa is not an isolated chatbot feature.
Explore Science has evolved from Paper-Wizard, its earlier manuscript-review product, into a broader scientific research platform. The company says the platform now supports researchers from early ideas and proposals through review, revision, publication, and dissemination.
Beyond manuscript review and Rosa, the platform includes tools for areas such as novelty checking, protocol review, research integrity, reference checking, journal and grant matching, collaboration, and communicating research findings.
The company's research arm goes even further, describing autonomous AI systems capable of generating research questions, designing studies, working with data, developing analyses, and producing manuscripts.
That broader direction helps explain why Explore Science describes itself as an AI science laboratory and research platform rather than simply an AI writing assistant.
The Bottom Line: What Does Rosa Actually Deliver?
For academic researchers, Rosa's value is not simply that it can have a conversation about a paper.
Its more distinctive role is as a context-aware AI co-scientist that can work from the researcher's manuscript and review, retrieve current literature, verify references, explain feedback, and help the researcher interrogate and refine the work.
The workflow is therefore less about asking an AI to "write my paper" and more about creating another layer of research scrutiny.
You submit the work. The system analyzes it. Literature and references are checked. The manuscript receives structured feedback and a Calibre score. Then Rosa gives you a way to question that feedback, investigate the evidence, and work through revisions.
For researchers who want to use AI without handing over scientific judgment, that distinction matters.
The researcher remains responsible for the science. Rosa is there to help make the research process deeper, more rigorous, and easier to interrogate.
