AI in the Classroom: Empowering Educators with Practical Tools for the Future

As we enter the era of Industry 4.0, artificial intelligence (AI) is revolutionizing industries and reshaping the skills that students need for future success. Yet, for many educators, integrating AI into the classroom can feel overwhelming. How do we teach AI in ways that are accessible, meaningful, and ethical? More importantly, how do we ensure that AI tools are transparent and understandable to both students and educators?

The answer lies in human-centered AI, a framework that emphasizes the role of human input and collaboration in AI systems. Human-centered AI focuses on creating algorithms within a larger human-based system, continuously improving through interaction and feedback. This approach closely aligns with explainable AI, which prioritizes transparency, making AI processes understandable and accountable.

In this article, we’ll explore why human-centered AI and explainable AI are critical for education and introduce powerful tools that can help educators integrate AI into their teaching. No coding knowledge is required—just a desire to empower your students with the skills they’ll need in a technology-driven world.

Why Explainable AI Matters in Education

AI isn’t just a technology we should teach; it’s one we should also understand. Explainable AI—AI systems that are transparent and interpretable—plays a critical role in schools, districts, and policymaking.

Incorporating human-centered AI ensures that the technology doesn’t operate in a vacuum but interacts with and learns from human inputs, continuously evolving in ways that are beneficial and ethical. This approach is especially important in the classroom, where students need to not only learn how AI works but also question its decisions and ethical implications.

Why? Because it ensures that educators and students aren’t merely passive users of AI but active participants in understanding how these systems work. Explainable AI allows teachers to:

• Demystify AI processes for students, showing them how decisions are made.
• Build trust in AI tools, making them more accountable and reliable for classroom use.
• Support ethical AI practices, ensuring students understand data privacy implications, algorithmic bias, and automated decision-making.

One of the key challenges in AI education is the "black box" problem—AI systems that perform tasks but don’t explain how or why decisions are made. This lack of transparency creates hurdles for educators and students alike. When AI decisions are unclear, it becomes more challenging for students to trust and understand the technology, limiting their ability to engage with AI tools critically.

Human-centered AI addresses this by ensuring that human input is part of the loop, making AI systems easier to understand and continuously improving them through feedback.

Explainable AI solves this by ensuring that AI models and decisions are interpretable. In the classroom, this means AI can be taught and explained. Students can learn not just to use AI, but to question and analyze how and why AI systems come to their conclusions. This promotes ethical AI use, data privacy awareness, and critical thinking. Explainable AI tools empower teachers to foster trust and accountability in AI technologies, encouraging students to see AI as more than just a "magic box" and inspiring a deeper understanding of how technology impacts the world.

For educational leaders, explainable AI means greater confidence in our adopted tools. Whether you're a teacher looking to incorporate AI in lessons or an administrator evaluating AI-powered platforms, transparency is key. It's about knowing why an AI tool makes specific recommendations and how it impacts teaching and learning.

Tools to Simplify AI for Your Classroom

Let’s explore some easy-to-use, practical tools that can bring AI concepts to life in your classroom, providing hands-on experiences that make AI both understandable and engaging.

1. Google Teachable Machine

Google Teachable Machine is a user-friendly, no-code platform that lets students train AI models using images, sounds, or poses. For example, they can create an image classifier that differentiates between various objects, or they can teach the system to respond to specific sounds. This hands-on tool introduces machine learning in an accessible way, allowing students to understand how AI learns patterns from data.  The platform's transparency allows students to see exactly how their training data influences the AI's decisions, making it an excellent tool for demonstrating explainable AI. ​(Teachable Machine)

Classroom Ideas:

• Use it for science experiments where students train AI models to identify types of leaves or animals.
• In physical education, pose detection can be used to track body movements.

2. Perplexity AI

Perplexity AI is a research assistant powered by AI that helps students and teachers quickly gather information on complex topics. It uses advanced algorithms to synthesize data from various sources, helping students explore ethical dilemmas in AI, technology’s impact on society, or even historical events. It provides students with a clearer understanding of how AI processes information, encouraging critical analysis of AI’s role in everyday research. (Perplexity AI)

Classroom Ideas:

• Use Perplexity AI to research AI ethics, helping students understand issues like algorithmic bias.
• Assign students to compare AI-generated research with traditional research methods to understand the differences.

3. Matatalab Nous AI

Matatalab Nous AI provides tactile, interactive learning with robotics, helping students grasp AI and coding through hands-on projects. Students can build and program robots, learning how AI uses data to perform tasks like sorting or navigating spaces. This makes AI tangible and transparent, key components of human-centered AI.

Classroom Ideas:

• Engage younger students with simple programming tasks that demonstrate how AI automates processes.  For example, using the Matatalab visual programming interface, students will program the robot to detect colored objects (e.g., colored blocks) using a color sensor.  The lesson will introduce students to basic AI concepts like data input, pattern recognition, and automation, helping them understand how AI uses information to make decisions.
• For more advanced students, they can program robots to perform tasks like sorting objects by color. The robot will take an advanced step and move the object to the correct designated spot based on color, helping students understand how AI uses data to make decisions.

4. Piper

Piper is an educational platform that combines hands-on learning with integrated lessons in AI and programming. Through Piper Make, a block-based coding platform, students can build their own computers and explore fundamental computer science concepts, all while learning about artificial intelligence interactively. The Piper Make environment allows students to easily create physical circuits and program devices, providing step-by-step lessons to guide their understanding of both hardware and software.

Piper’s lessons and projects cover a wide range of topics, from basic electronics to advanced programming. These lessons are designed to align with STEM-focused standards, promoting problem-solving and critical-thinking skills. Piper also offers ready-to-use projects, including the creation of voice assistants, programmable robots, and intelligent sensor systems, introducing students to AI concepts like automation and decision-making.  These kits teach aspects of both explainable AI and human-centered AI.

Classroom Ideas:

• Students can build and program a functional computer with the Piper Computer Kit, learning the fundamentals of electronics and coding.
• With Piper Make, students can engage in practical AI projects, such as creating a robot that responds to voice commands or programming sensors to detect environmental changes.

5. Finch Robotics

Finch Robots from BirdBrain Technologies allow students to program robots that interact with their environment using data inputs. Finch’s Snap! interface makes it easy for students to see how their programming choices affect the robot’s behavior, offering a clear example of explainable AI.

Classroom Ideas:

• Assign students to create an AI model that guides the Finch through a maze using data from sensors to make decisions about where to turn.

6. Hummingbird Robotics

Hummingbird Robotics kits enable students to design AI systems that interact with their surroundings. By using sensors and machine learning algorithms, students can explore complex AI processes, such as real-time decision-making based on environmental data.  Exploring AI through the lens of the environment fits perfectly into human-centered AI, and explaining the steps behind how it works is under the realm of explainable AI.

  Classroom Ideas:

• Use Hummingbird kits to create an AI-powered robotic pet that reacts to movement or touch, demonstrating how AI can be applied in real-world robotics.

7. UGOT from UBTECH

UGOT is a hands-on AI and robotics platform that helps students learn AI and coding through project-based experiences. Students can create and train AI models that integrate with robotics, exploring real-world applications like facial recognition and speech interaction. UGOT encourages students to think about the ethics of AI, making it a powerful tool for human-centered AI learning.

Classroom Ideas: 

Students can build and program robots to recognize facial expressions or respond to voice commands, giving them insight into how AI operates in industries like health and customer service.

• Program robots to recognize facial expressions, introducing students to AI applications in industries like healthcare.
• UGOT projects can also focus on ethical AI, helping students understand the responsibilities and challenges of working with AI technologies in areas like privacy and bias.

8. Strawbees

Strawbees blends creativity, engineering, and AI learning. Students can build interactive projects using sensors and AI models to control their designs. This hands-on approach to learning emphasizes project-based learning and human-centered AI, making AI more relatable and less intimidating.

Classroom Ideas:

• Have students create an AI-driven traffic light system that responds to different environmental conditions, such as light or sound, showcasing real-world AI applications.

Challenges and Opportunities in Explainable AI

One of the most significant challenges in AI education is the "black box" problem, where AI systems produce results without offering clear explanations. This can lead to mistrust or disengagement. However, the tools discussed above provide opportunities for explainable AI by making AI processes transparent and relatable.

For example, Google Teachable Machine shows students how their training data directly impacts AI decisions, while Piper and Finch offer real-time feedback on how data inputs drive AI actions. By incorporating explainable AI tools into the classroom, educators can create a learning environment where students engage critically with AI, understanding how it works and why it makes specific decisions. This promotes ethical AI use and prepares students to interact responsibly with AI technologies in their future careers.  This approach ties directly to human-centered AI, which focuses on creating systems that are continuously improved through human interaction and collaboration.

Bringing AI into the Classroom: Practical Steps

When teaching AI, it’s important to make the learning process interactive and accessible. Here are some practical steps to help you integrate AI into your curriculum:

1. Start with simple, real-world applications.
   Tools like Google Teachable Machine or Strawbees allow students to quickly see the impact of AI on everyday tasks, making AI less intimidating and more relatable.
2. Use project-based learning (PBL).
   Give students real-world problems to solve using AI. For example, with Finch, they can design an AI-powered obstacle-avoiding robot, or with Matatalab, program robots to complete simple tasks.
3. Emphasize explainability and human-centered AI.
   Help students understand why and how AI makes decisions. Tools like Piper and UGOT show students the interactions between hardware and software, providing insight into how AI models work and evolve through human input.

Final Thoughts

AI is not just the future; it’s the present.  As educators, we are tasked with preparing students for a world where AI will be a central part of their careers and daily lives. By incorporating tools like Google Teachable Machine, Strawbees, Piper, Hummingbird, and the others mentioned above, we can demystify AI and give students the skills they need to thrive in the technology-driven world of tomorrow.

Through human-centered AI and explainable AI, we can inspire the next generation of innovators to engage critically with technology, ensuring a future where AI systems are transparent, ethical, and deeply integrated into human collaboration. Ready to bring AI to your classroom? Let’s get started!