Is Google Scholar Lab Delivering Its Promise?
Is Google Scholar Lab Delivering Its Promise?
Is Google Scholar Lab Delivering Its Promise?
Is Google Scholar Lab Delivering Its Promise?
Is Google Scholar Lab Delivering Its Promise?
A first test from the field of design in public-sector innovation in Latin America
A first test from the field of design in public-sector innovation in Latin America
By Tina Rosado, Researcher
By Tina Rosado, Researcher
When Google released Scholar Lab a few weeks ago, it framed the tool as “an AI-powered Scholar search that is designed to help you answer detailed research questions.” Scholar Lab is still in limited early-access testing, but the promise is ambitious: instead of relying on keywords, it claims to identify key concepts within a research question, scan the literature, and surface the papers that best speak to it—complete with short contextual summaries.
I’ve spent the past year helping build a network of Latin American public-sector innovation labs at the Public Design Collective. So when this new tool was released, I was eager to see how it would perform against our carefully curated corpus, now available on our GitHub repository as part of our multilingual systematic literature review. Within 24 hours of requesting access, I received an invitation and ran a simple test. What follows is a first impression: part methodological reflection, part commentary on where AI-assisted academic discovery can (and cannot yet) take us.
How Does Scholar Lab Work?
Google frames Scholar Lab’s workflow as follows:
Analyze the research question for key topics, relationships, and concepts.
Retrieve literature aligned with those elements.
Evaluate and rank papers based on how well they address the question.
Deliver results, ten relevant documents at a time.
For my test, I iterated through that workflow twice, and in just a few minutes, gathered 20 documents:
Step 1: I entered my research question. Scholar Lab scanned roughly 65 papers and returned 10 AI-selected results.
Step 2: I clicked “More,” prompting a broader pass over about 185 documents, after which the tool delivered 20 ranked results, each with a short explanation of why it was selected.
The real question, of course, is whether this workflow actually delivers on Google’s promise of helping researchers answer detailed, domain-specific questions.
Designing a Simple Test for Scholar Lab
To evaluate the tool, I turned to the central question guiding our systematic literature review detailed in our presentation and paper, Building a Networked Repository of Public Sector Design in Latin America, for the 7th International Conference for Public Policy:
“How is design being used in Latin American public-sector innovation units?”
I queried both Scholar Lab and traditional Scholar, collected the top 20 results from each, and compared them against our curated multilingual corpus of 84 documents selected from an initial corpus of 700 sources across English, Spanish, French, and Portuguese.
This gave me a direct way to test Scholar Lab’s relevance, precision, and blind spots against a known benchmark.
Results & Observations
1. Speed and Volume: Scholar Lab Is Undeniably a Productivity Engine
The tool dramatically accelerates early discovery. Here is a comparison of the 20 results returned by each tool (access the data here):
Results & Observations
1. Speed and Volume: Scholar Lab Is Undeniably a Productivity Engine
The tool dramatically accelerates early discovery. Here is a comparison of the 20 results returned by each tool (access the data here):
These results are impressive. Scholar Lab clearly enhances information-seeking and retrieval tasks for researchers by significantly reducing the time required for initial discovery and screening.
One area the Google team could improve is duplicate handling; several results surfaced different versions of the same paper. Ideally, duplicates would be grouped together or surfaced as a single entry with multiple versions, a simple enhancement to further streamline the experience.
2. Language Limitations: A Major Gap for Multicultural Research
Our repository is multilingual, and our review showed that much of Latin America’s public-sector innovation scholarship is published in Spanish and Portuguese. Scholar Lab, however, currently searches only the English-language site—a real limitation for work shaped by cultural or linguistic context. For our own review, we had to run searches across four different Scholar sites, one for each language represented in our corpus.
A version of Scholar Lab capable of understanding a question and retrieving sources across languages—without requiring researchers to navigate separate, language-specific sites—would fundamentally reshape global and regional research workflows. This feels like both a challenge and a major opportunity for Google’s team, and the prospect of truly multilingual academic discovery is an exciting one.
3. Topic Scope: Scholar Lab Paints a More Homogeneous Portrait of the Field
One of the most revealing outcomes of this experiment is the contrast in conceptual breadth.
Our systematic review required generating a large search universe, filtering painstakingly, and debating its boundaries, raising questions such as:
What counts as an innovation lab? (A deceptively complex question in a multilingual, multicultural region where labels and naming conventions vary widely.)
Do we include design practices for social change that occur outside government institutions?
Where do we draw the line between concepts as service design, digital government innovation, open government, or intra-entrepreneurship?
This boundary work was intellectually generative, an experience that required us to articulate definitions, confront ambiguities, and recognize emerging lines of inquiry. It also reflects a broader scholarly reality: work of this kind is foundational for shaping a field and advancing research rather than merely mapping it.
Google Scholar Lab, by contrast, bypasses this deliberation. It retrieves relevant material efficiently, but the portrait it generates is often more homogeneous than reality. This is both an asset and a constraint. Its strength lies in surfacing relevant scholarship quickly and efficiently; its limitation is that it can inadvertently create blind spots, showing you what is nearby rather than what belongs or where the boundaries lie.
A metaphor helps: Scholar Lab hands you a beautifully curated set menu, complete with clear descriptions of each dish’s ingredients. What it doesn’t do is let you into the kitchen where substitutions, omissions, and improvisations occur. Those hidden decisions are often what enable researchers to define boundaries and shape new questions that ultimately move a field forward.
