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Essential UX Practices for Ed-tech

The slingshot effect of COVID-19 on distance learning contributed to the ed-tech boom in 2020–2022. Keeping an optimal user experience became crucial for ed-tech rivals to obtain a maximum user base and supply high-quality learning solutions to learners. This was due to the huge volume of tech goods on websites and apps aiming to impart education. Due to the remarkable changes in career opportunities and the need for upskilling for them, the trend of remote education is still popular and in great demand. This trend is especially evident in upskilling and test preparation. And there is increased demand to maintain a good user experience with ed-tech products. Here are some essential UX practices for Ed-tech:

  1. Accessibility:
    It is crucial to make sure that all users, including those with disabilities, can access ed-tech platforms. This includes tools like text-to-speech choices, closed captioning, and screen readers. Additionally, a user-friendly interface should be included in the platform’s architecture. This makes it simple for users to explore and locate the data they require. One example below about accessibility:
Legends (for those without complete color blindness)
Legends Appearance (for those without color blindness) 
Legends (for those with complete color blindness)
Legends (for those with complete color blindness)

The above picture is an interface design for the nationally adopted format of the online examination. It has been designed to make the system accessible for people with color blindness (1 of every 12 people in the world is color blind). With the help of the legends, the students navigate questions with color and shape recognition. Observe the shape and color used for ‘answered’ and ‘not answered’ which are in green and red, respectively. If the shape had not been different, it would have been difficult for a student with color blindness to recognize which question is answered, which one is not, and which question is marked for review. Different shapes break the consistency in design elements as per some UI design rules, but this is necessary due to accessibility.

Legends with different shapes and colors
Legends with uniform shapes and different colors (visibility without and with colorblindness, respectively)
Legends with different shapes and colors
Legends with different shapes and different colors (visibility without and with colorblindness, respectively)
  1. Personalization:
    Our learning at school was organic and nurturing due to the personal connection each student had with the teachers. When we talk about education, there should be a personal connection between the students and the system to boost students’ learning. Platforms for ed-tech should be created to meet the behavior and demands of every student. This entails tailoring feedback, designing a learning path specifically for each student, and personalizing the educational experience. This strategy boosts student enthusiasm and engagement, which will result in better learning outcomes.
  1. Gamification:
    Gamification has been one of the most important and essential UX practices in ed-tech and education. By adding game-like components like incentives, points, and badges, gamification techniques can be utilized to improve the learning experience. Learning can become more enjoyable and interesting as a result, especially for younger children.
  1. Collaboration and feedback:
    Edtech platforms should make it easier for students and teachers to collaborate, as it is a crucial component of the learning process. Features like collaborative projects, discussion boards, and video conferencing fall under this category.
    Giving feedback on time is essential for the learning process. Platforms for education technology should provide feedback on students’ progress along with suggestions for what might be done better. Students who are driven and interested in their studies may benefit from this. While building an ed-tech platform, it’s crucial to build features for smooth collaboration among learners, educators, and administration. 
  1. Mobile optimization:
    Ed-tech platforms should be mobile-optimized given the rising use of mobile devices. This entails creating an interface that is appropriate for mobile use, providing mobile-specific functionality, and making sure the platform is usable on a range of devices.
    Note: Soon an upcoming blog will have a detailed view of “mobile optimization” in ed-tech. Keep reading Mantra Labs’ blog post.
  1. Data analytics:
    Data analytics systems for ed-tech platforms should be able to monitor student progress, pinpoint areas for development, and give teachers feedback. This can assist teachers in modifying their instruction to better meet the needs of each student.
  1. Continuous improvement:
    Last but not least, ed-tech platforms must be created with ongoing improvement in mind. This includes ongoing upgrades and enhancements based on user input and the most recent developments in ed-tech. Platforms should be built with scalability in mind so that they may change and evolve as requirements do. The Design Thinking process will help in creating such a system that will help students, teachers, and the company from all angles in this situation by making, remaking, and continuously refining the system.

Key takeaways:

  1. Prioritize accessibility and personalization to create a user-friendly learning experience for all students.
  2. Personalization in design creates strong and nurturing connections between the system and students.
  3. Incorporate gamification to increase student engagement and motivation.
  4. Provide collaboration and feedback features to improve engagement in the learning process.
  5. Optimize ed-tech platforms for mobile use to cater to the growing use of mobile devices.
  6. Utilize data analytics to track student progress and identify areas for improvement.
  7. Continuously improve ed-tech platforms based on user feedback and the latest industry trends.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UX Research and Product Design.

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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