How to Build an AI Agent for EdTech: A Step-by-Step Guide

AI agents can act on their own, and that makes them game-changers for the EdTech sector. They engage students, remind them of tasks, reward active learners, and help educators prepare materials for studies. AI agents also allow teams to streamline and automate processing in administering the platform. As agents take preliminary tasks, people can focus on strategic processes.

In this guide, we share our view on what you need to build an AI agent for an EdTech platform. We take it from start to finish, step-by-step, so that you know on a high level what is needed to build an AI agent for your EdTech solution.

The Role of AI agents in EdTech: Helping to teach and learn

What is an AI agent in EdTech?

AI agents are intelligent programs that can operate on their own and understand human intent. These smart helpers can tackle designated tasks without human control. Moreover, AI agents do not just follow strict rules - they adapt those rules by learning over time.

So, how can they be this helpful? Here are several aspects contributing to that:

• They work autonomously, which means they work independently, once set up;
• They use machine learning that helps them learn and become better over time;
• They process and understand natural language, which forms the foundation of learning;
• They integrate across the whole EdTech platform ecosystem, linking students, teachers, and administrators.

Imagine an educational assistant who not only knows what you need, but can also predict. AI intelligent agents learn patterns in your behavior and can help you better in learning, teaching, or administering the education process.

Benefits of AI agents for EdTech platforms

Since AI agents have advanced data processing and action features, they improve the education process in numerous ways. Benefits of AI for EdTech platforms include the following:

• Students can leverage AI agents for gamification, multimedia content, and interactive assessments; support their self-paced learning; get instant feedback and controversy resolution; use student support chatbots;
• Educators and teachers get reduced workload with AI in online education; can focus on developing educational materials; get insights into teaching strategies and generate materials, like quizzes and workbooks;
• Institutions get help with the administration of educational processes; get improved graduation and student success; streamlined operations and resource management;
• Online course creators can personalize their courses; automate student support; optimize course based on data; scale without additional effort.

Use cases of AI agents in EdTech

Particular use cases provide you with insights on what your EdTech AI agent would do. So, let’s discuss how AI helps in learning.

Personalized learning assistants

They provide versatile personalized help for students and instructors. They analyze students’ behavior and provide custom learning paths depending on their progress. AI agents serve as adaptive learning systems. They recommend the next topics or resources, and even identify gaps and automatically schedule reviews or exercises. Moreover, they help with on-demand tutoring, provide real-time feedback and motivation, and help keep track of learning goals.

Examples include Duolingo Max and Querium, which assist with learning languages and STEM subjects.

AI tutors and Q&A bots

Personalized learning AI provides tailored guidance in studies. Thanks to intelligent tutoring, students get tailored experience in line with their learning goals. Such AI tutoring agents and Q&A bots are particularly helpful for neurodivergent people, as agents adapt study plans according to individual abilities and disabilities. Transforming learning into digestible conversational messages, the agents make the process engaging and improve concentration.

For example, Khanmigo on Khan Academy teaches math, science, and humanities; Socratic by Google helps you learn by uploading photos of questions and getting answers to them.

Administrative support

Streamlining administrative processes is another benefit of educational artificial intelligence. It relieves teams from unnecessary workload and provides better support for students. How do agents help? See key functions of AI education automation and optimization:

• Enrollment and onboarding. Automate form submissions, verify documents, and guide new students through the setup process;
• Scheduling and notifications. Manage class schedules, reschedule sessions dynamically, and send reminders for deadlines or events;
• Support chatbots. Answer FAQs related to billing, technical issues, course availability, and platform navigation;
• Document handling. Automate verification of assignments, certificates, or registration forms;
• Feedback collection. Conduct surveys or collect post-course feedback through conversational agents.

Planning your EdTech AI agent: Key points

Before discussing how to build an AI agent, it is vital to define key points of your AI agent’s functioning, including target users, use case, core features, data sources, and privacy.

Identify your target users and use case

Users are those who help your platform grow. Hence, it is vital to start right and ensure that your platform meets users’ needs and wants. Focus on a specific niche and target group, whether these are STEM subjects, a school program, or an upskill platform. Targeting broadly, you risk chasing a wide spectrum of users, which will not build a well-defined brand image for your platform.

Define core functionalities

Once you define your target audience, it’s time to outline what your AI agent should be able to do. The key agents’ functionalities in education technology AI solutions include the following:

• Explain concepts. Break down complex topics into simple parts with natural language;
• Quiz students. Form and send personalized quizzes to assess understanding;
• Provide feedback. Review answers and offer instant, actionable feedback, for example, correcting grammar or suggesting problem-solving steps;
• Track progress. Monitor user performance over time and adapt content accordingly;
• Answer questions. Act as a Q&A bot, resolving user queries in real time;
• Suggest content. Recommend lessons, exercises, or resources based on user behavior;
• Manage tasks. Help users stay organized with reminders, checklists, or learning goals.

Plan for data sources and privacy

To function effectively, AI agents need data. However, this data should be cleaned and standardized since it improves your AI agent’s training. Moreover, data must protect users’ privacy and security in an AI-based learning system.

Datasets must be from trusted sources, structured, labeled, and consistent, and received with user consent. Audit and update your data so that it reflects current user behavior. What else is to keep your data anonymized, encrypted, with controlled access, and collect only what is necessary to keep AI agents up-to-date.

In education, data privacy is regulated by FERPA (Family Educational Rights and Privacy Act), which protects student education records in the U.S. Another data protection standard is GDPR (General Data Protection Regulation), which protects data collection, processing, and storing in the EU.

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Tech stack for EdTech AI agent development

Robust tools ensure that your AI agent will work and learn efficiently over time. Therefore, we recommend that you use the following tools to build an AI agent and use language models in EdTech.

NLP libraries and frameworks

Natural language processing allows your AI agents to understand questions, formulate concepts, and give feedback. NLP is what makes AI agents natural companions to engage students in conversations. So, you can use NLP for EdTech startups and established companies.

In this regard, several libraries and frameworks stand out to enrich your AI agent with NLP:

• OpenAI (GPT-4, ChatGPT API). Ideal for conversational agents, Q&A bots, and content generation;
• Hugging Face Transformers. A powerful open-source library offering pre-trained models, which you can further tune up;
• spaCy. A Python and Cython-based library that supports 75+ languages and state-of-the-art speed.

ML platforms for training

Training machine learning models for EdTech AI agents is crucial to ensuring personalized and correct outcomes for platform users. To train models properly, you can use three of the leading ML training platforms for the best results:

• Amazon SageMaker. A fully managed service for building, training, and deploying machine learning models on AWS. It offers rapid setup, prepares data, deploys trained models as APIs, and is ideal for SaaS startups or EdTech platforms already using AWS;
• TensorFlow. An open-source ML framework developed by Google. It defines and trains models with computational graphs, offers fast prototyping, supports large-scale training, and works for teams who want to design models in a Python-friendly environment;
• PyTorch. An open-source deep learning framework by Meta. It provides intuitive model definition and training loops, excellent support for NLP via Hugging Face Transformers, and tools for model deployment. Perfect for training custom educational agents.

Backend infrastructure

A robust backend is essential to support scalable, responsive AI agents in EdTech. Your backend infrastructure should handle operations in a fast, flexible, and cost-effective way.

As a starting point, we recommend that you use serverless platforms, which do not require managing servers. So, you get a pay-as-you-go, easily scalable infrastructure to handle loads from lean to spiking, for example, during exams.

As for processing data, our team suggests that you choose a database depending on the data your EdTech agent processes. For example, for structured data, such as users, courses, and scores, you can use PostgreSQL and MySQL databases. For advanced data processing, use databases with semantic search, such as Pinecone and Weaviate.

Next, to enable live interactions, AI agents need real-time communication with users and systems in your educational platform. For this, use popular tools, such as Socket.IO for chatbots and dashboards, or AWS SNS for workflows across agents triggered automatically.

Step-by-step process to build an AI agent for EdTech

Building an education AI assistant is a versatile, multi-step process. It requires comprehensive planning, an implementation strategy, and continuous iteration. Whether this is EdTech chatbot development or a personalized tutor, you need to combine all components with AI development services carefully. Thus, your AI agent will seamlessly integrate with your platform.

In our AI agent development guide below, we outline the process we use in our practice as a proven method to build an AI agent for EdTech that will meet your users’ needs.

Step 1: Define objectives and learning outcomes

Before starting development, clearly define what your AI agent is meant to achieve. It must align with the pedagogy and curriculum goals on your platform. This is essential because your AI agent influences students’ success, educators’ contributions, and the team’s collaboration and platform management.

Hence, we suggest that you discuss several questions with your team before building an AI agent:

• Who will use your AI agent: students, instructors, or staff?
• What learning results it fosters: learning from start, upskilling, retention, development?
• How it aligns with pedagogy? Is it scaffolding knowledge? Practicing spaced repetition? Encouraging inquiry?

We help you find answers to those and other questions during our product discovery sessions. They aim to outline for you the target audience, scope, timeline, tech team, and other aspects of development. Thus, the project discovery phase is a necessary step to start your AI agent development correctly.

Step 2: Collect and prepare educational data

Data is the foundation of how your AI agent works and responds to queries and input. The data serves as the backbone for training models, forming agent behavior, and enabling personalized, curriculum-driven support. These materials include transcripts, questions and answers, student responses, and information on subject metadata, such as subject, topic, and grade level.

The next step is preparing and normalizing educational data for AI. We check that there are no typos, formatting issues, or inconsistent labels. Also, we add tags, including topic tags, difficulty levels, and alignment with curriculum topics and standards.

Ensure that your data is anonymized by removing personal identifiers, like login credentials or student ID numbers, to meet FERPA and GDPR.

Finally, we make a data structure that your AI model can easily consume. The formats include JSON, CSV, or embeddings to transform raw data into a format that AI agents can understand.

Step 3: Choose and train your AI models

When training your models, you have two options: use pre-trained models and fine-tune them, or train your models from scratch. Pre-trained models are designed for simple tasks and are more cost-effective, and models trained from scratch require more resources but can handle complex decisions.

For example, pre-trained models are ideal for tasks like question answering, tutoring dialogues, or grading support. Specialized agents for platforms with sensitive or proprietary data are better when built from scratch.

So, how can you train your model? Split your data into three sets:

• Training set. This set is for training;
• Validation set. Use this one to check how well the model is learning during training;
• Test set. This checks how the model performs on data it hasn’t seen before.

Then, you can measure mistakes based on test answers and use optimizers to help the model adjust its thinking with each example.

Step 4: Build conversational logic and UX

Once your model is trained, we involve our UI/UX design services to define how the model will interact with users through conversations and user experience.

So, what does your AI agent say? When does it say this? How does it guide the learning or course material creation processes? You can formulate that in two ways: using flow-based dialogues or LLM-based dialogues.

Flow-based dialogues are designed for simple conversations; they follow specific rules and are helpful for onboarding, quizzes, or FAQ bots. LLM-based dialogues use context, not a fixed script, and lead personalized tutoring, answering open-ended questions, or helping with homework.

To embody those conversations, you need an interface. Keep it simple, and guide your users with a convenient UX design:

• Use conversational cues like buttons and hints;
• Let users reset, ask for help, or switch topics easily;
• Show progress indicators or rewards to boost engagement.

Step 5: Integrate with your platform

Generally, integration options for AI chatbots include LMS (learning management system), a mobile app, or a standalone chatbot. AI integration for LMS or a web app comes as a widget, side panel, or chatbot interface. When integrating into a mobile app, use APIs for handling interactions. A standalone chatbot can be created on a separate page or platform, and is suited to include Q&As, support bots, or onboarding guides.

Step 6: Test, monitor, and iterate

When your agent has been built, the true work starts. Testing your agent in real-world scenarios helps you avoid inaccuracies and ensure proper operation. We recommend you start small and iterate with quality assurance services.

First, launch small-scale pilots and test them in real classrooms with a small number of students and teachers. Observe how your AI agent responds and whether it needs to be more flexible or accurate. Use surveys, in-app ratings, or follow-up interviews to understand whether the experience resonated with students and educators.

When monitoring agent performance, metrics are helpful. To track them, use analytics tools, such as Firebase Analytics and Mixpanel; Amplitude for behavior funnels and retention tracking.

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Scaling and maintaining AI agents in EdTech

To achieve good results, AI agents should undergo relevant training. Therefore, it is essential to maintain strategies that keep them evolving and improve their performance. This is where ongoing learning and performance monitoring step in.

Continuous learning and feedback loops

Gather anonymized user interaction data, such as questions, responses, and ratings, and use it to retrain or fine-tune your model. Collect helpful and unhelpful labels from users by logging interactions with your AI agent and identifying content gaps. Depending on that, fine-tune your model monthly or quarterly.

Another technique is Reinforcement Learning from Human Feedback (RLHF). When an agent gets “rewards”, it learns to change its behavior dynamically. It is ideal for large-scale agents that need to be optimized for long-term engagement.

Performance monitoring and bias mitigation

When monitoring the agent’s performance, ensure that it delivers fair, accurate, and unbiased support to all users. Thus, it is vital to monitor key performance metrics, such as error rate, user satisfaction, drop-off points, and response time and consistency.

At the same time, the agent’s possible bias must be mitigated. It should be trained with diverse data, including all possibilities in learning, such as subjects, learning abilities, viewpoints, and user groups.

Future of AI agents in EdTech

The next generation of EdTech agents will go from basic tutoring or automation to more advanced features. They will be more interactive, emotionally aware, and engaging than ever before. The primary trends in the future of AI agents include the following:

• Multimodal AI. Agents will not be limited to interpreting text. They will process images and even video, along with handwritten notes. Thus, students will get a spoken explanation for their questions about images and diagrams;
• Emotional intelligence. Agents will become more versatile in responding to students’ emotions, such as confusion, frustration, and excitement. They will use empathetic support if things get complicated for a student;
• Gamified AI agents. Gamification boosts learning engagement with points, badges, and challenges. Thanks to gamified AI agents, students get a track of their progress, achievements in missions, and AI-powered peers in quiz games.

Wrapping up

Building an AI agent involves several strategic steps, from planning through training, to testing and iterating. They ensure that your AI agents in the EdTech platforms respond correctly and accurately to users, whatever needs they have in educational processes, and whether they are students, teachers, or staff.

Thanks to custom software development services, your AI agent will truly engage students and teachers and help staff with administering the educational process. If you want to include one in your EdTech solution, we are eager to help you based on our experience, as illustrated in our portfolio. Contact us and let’s build a meaningful AI agent for EdTech.