Ed tech tools generate vast amounts of data from student performance trends, specific learning challenges, and more. This data holds significant potential to inform administrative and policy decisions, enhance learning experiences, and even develop, train, or benchmark education-focused AI models.
However, raw data is not immediately usable, especially for AI applications. In order to extract meaningful insights, the data must first be cleaned, structured, and labeled. This process is known as data annotation.
What is Data Annotation?
Data annotation involves labeling data with meaningful, structured tags that make the information easily interpretable by AI systems, whether tagging emotions in student feedback, identifying images, or labeling parts of speech in text.
Despite its importance, annotation can be both time-consuming and costly. From preparing data for labeling to sourcing skilled annotators and selecting the right platform for your project, the process can feel daunting, especially if you’re just starting out.
In this article, we’ll explore tips to help your team choose the right annotation platform for your project.
Tips On Selecting The Right Annotation Platform
1. Start with a Clear Understanding of Your Needs
Choosing an annotation tool starts with a clear understanding of your goals. What are you trying to achieve?
- Are you building an AI model to give step-by-step feedback on math problems?
- Are you adding relevant data to the field to support models that score essays?
- Will the resulting data be publicly available or kept private?
- How much data will be annotated?
Once you understand your goals, you can begin to define the scope of your annotation task. Here are some important questions to ask:
- What kind of annotation is required (text, image, audio)?
- Does it require subject-matter expertise?
- How many annotators need to be involved?
- Is there potential to incorporate AI-assisted labeling?
These questions will help you determine whether a manual, hybrid, or fully automated approach is best for your situation, and whether you need a basic tool or an end-to-end annotation platform.
2. Start Simple When You Can
If you’re working with small datasets or straightforward labeling tasks such as identifying parts of speech or assigning a score to a short sentence, you may not need a complex platform right away.
Google Sheets is a great choice for this type of annotation. It is not only free but it supports real-time collaboration across annotators and teams, clear tracking of edits and progress, and the ability to lock sheets to prevent unwanted changes.
However, Google Sheets does have its limitations. As your dataset grows or your annotation tasks become more complex, you can run into challenges. Manual formatting can become time-intensive. It can become harder to maintain consistency across large teams. Tracking changes can be inefficient.
In these cases, using an end-to-end annotation platform or pre-made tools can help save time and ensure quality data.
3. Consider The Platform Features You Actually Need
End-to-end platforms and pre-made tools often come with exciting, cutting-edge features, including AI-assisted labeling, automated workflows, large language models (LLMs) acting as judges, and high-quality interfaces.
However, it’s essential to focus on the functionality that your team actually needs. Before choosing a platform, ask:
- Do we need task automation, customizable workflows, or role-based permissions?
- Is collaboration between annotators essential?
- Is adjudication part of the process?
- Will expertise be needed to use the platform?
- Is the tool intuitive and user-friendly for annotators?
- Can we easily export data in the formats we need?
- Does the platform support agreement analysis (e.g., inter-annotator agreement)?
- Can we track annotator performance and progress?
- How important is deep customization?
By clearly identifying your must-have features before evaluating platforms, you’ll be able to narrow down your options from the overwhelming number of platforms out there. Most importantly, you’ll know exactly what questions to ask when you sit down for a demo, ensuring the conversation stays focused on what really matters to your team and project.
4. Open-Source Platforms Offer Flexibility, Require Technical Expertise
If customization is a top priority and your team has the technical capacity to manage deployment, an open-source, self-hosted platform may be the best fit. These tools and platforms give you greater control over task design, annotator experience, and workflow.
Here are some options for open-source tools:
- Potato, a web-based annotation tool that allows quick mock-up and deployment of text annotation tasks through a locally launched web server.
- Doccano, an open-source text annotation tool that supports text classification, sequence labeling, and sequence-to-sequence tasks, allowing users to quickly create labeled data by creating a project, uploading data, and annotating.
- DataQA, a labeling and exploration tool for unstructured documents that uses rules-based weak supervision to reduce labeling needs and supports search, classification, entity extraction, and entity linking, all installable via a simple pip command.
Although open-source tools offer flexibility and transparency, they can be a heavy lift, especially if your team doesn’t have the technical expertise.
5. Managed Platforms Offer Scalability and Speed
If your team lacks the bandwidth to manage developing your own annotation infrastructure or doesn’t require heavy customization, a managed, hosted solution may be more practical. These platforms offer a lot of features and reduce setup time.
Here are a few notable options:
- Label Studio, a flexible platform offering both an open-source, locally hosted version and a fully managed cloud option. It includes task assignment, annotator collaboration tools, and performance dashboards. While it supports human annotation, it’s especially geared toward LLM-enhanced workflows like LLM-as-a-judge and reinforcement learning.
- SuperAnnotate, a hosted, intuitive platform built for collaborative teams. It features customizable workflows, automatic task distribution, performance tracking, and robust dashboard tools. Like Label Studio, it emphasizes integration with LLMs and AI-assisted labeling.
- LabelBox, a cloud-based solution with support for structured team annotation. It allows for role-based permissions, manual batching of tasks, and includes tools for evaluating inter-rater reliability (IRR) directly within the platform.
- Dataloop, a scalable, fully managed platform that blends automation capabilities with collaborative features. It offers role-based access control, workflow customization, and infrastructure designed to support both manual and AI-assisted labeling.
Although managed platforms are great, they come at an exceptional cost, which is a critical factor regardless of which annotation route you choose.
6. Don’t Overlook The Costs
Budget is a crucial consideration for any project. If your project requires a managed platform, the costs can easily escalate. Platforms may charge per annotator or per data point. Some features may only be offered at premium or enterprise levels. There could also be a cost for data storage.
It is important to keep in mind that a platform that seems affordable may not scale well over time. These costs now are simply to complete the annotation process; they don’t include payment to find and pay annotators, or to get the tools needed to prepare your data to begin the process.
So, make sure the platform you choose aligns with your financial resources, not just in the short term, but over the lifecycle of your project.
Final Thoughts On Selecting An Annotation Platform
Overall, choosing the right annotation platform isn’t just about features; it’s about aligning tools with the specific demands of your project.
Start by defining your project’s needs. Explore whether simple tools might suffice, or if a managed solution is worth the investment. Evaluate platforms based on what matters to your workflow, not just what sounds cutting-edge. And never overlook the hidden costs, both financial and logistical.
The right platform won’t just help you annotate, it will scale with your goals, support your team, and deliver reliable data that powers real impact.
Kennedy Smith
Jules King
