Read our blog and carry on - EdTech / LMS

Voice biometry is changing the way businesses operate by using distinctive features of a person's voice, like pitch and rhythm, to confirm their identity. This technology, a central part of Voice AI, turns these voice characteristics into digital "voiceprints" that are used for secure authentication. Unlike traditional methods such as fingerprint or facial recognition, voice biometry can be used remotely with just standard microphones, making it both practical and non-intrusive. This technology enhances security using advanced algorithms that block fraudulent attempts, making it a popular choice in various sectors requiring reliable and user-friendly authentication solutions, such as finance, healthcare, and customer support. The voice biometric market, valued at $1.261 billion in 2021, is expected to grow significantly, with a projected annual growth rate of 21.7%. By 2026, the market is anticipated to exceed $3.9 billion. Voice recognition is a valuable method capable of improving the security and customer service and offering rich personalization experience. Today we’ll explore, how it works and take a look on use cases in different areas of business Voice is produced when humans push the air from the lungs through the vocal cords, causing them to vibrate. Vibrations resonate in the nasal and oral cavity, releasing the sounds to the world. Each human's voice has unique characteristics, such as pitch, tone, and rhythm, shaped by the anatomy of their vocal organs. This makes the voice as unique as fingerprints, faces, or eyes. Voice recognition identifies individuals by analyzing the unique characteristics of their voice. This involves two key stages: Acoustic Analysis This stage involves analyzing the voice sample as an acoustic wave. Technicians use a waveform or a spectrogram to visualize the voice. Waveform displays the amplitude of voice, featuring the loudness, while spectrogram reflects the frequency, representing them in color or grayscale shading. Mathematical Modeling After analyzing the voice, its unique characteristics are transformed into numerical values through mathematical modeling. This step uses statistical and artificial intelligence methods to create a precise numerical representation of the voice, known as a voiceprint. Active & Passive Extraction Active Voiceprint Extraction requires the person to actively participate by repeating specific phrases. It’s used in systems that need very accurate voiceprints. Passive Voiceprint Extraction captures voice data naturally during regular conversation, like during a customer service call. It doesn’t require any specific effort from the user, making it more convenient and less intrusive. The choice between active and passive extraction depends on the needs of the system, such as the level of security required and how intrusive the process can be for users. Voiceprints are securely saved in a database, and each is stored in a unique format set by the biometrics provider. This special format ensures that no one can recreate the original speech from the voiceprint, protecting the speaker's privacy. Voiceprint Comparison When a new voice sample is provided, it is quickly compared to the stored voiceprints to check for a match, which is crucial for verifying identities. This comparison can happen in a few ways: Main Challenges Solution The language learning platform supports various types of exercises, including writing ones, guessing games, and pronunciation training. This module focuses on providing precise, unsupervised pronunciation training, helping the students to refine their pronunciation skills autonomously. How It Works When a student speaks, the system displays a visual waveform of their speech. This points out errors by highlighting incorrect words, syllables, or phonemes and offers the correct pronunciation. It also presents alternative pronunciations, providing learners with a broad understanding of different speaking styles. The pronunciation evaluation module uses artificial neural networks and deep learning to analyze speech patterns, while machine learning and statistical methods identify common errors. Decision trees analyze speech patterns against set linguistic rules to determine pronunciation accuracy, identify errors, and suggest corrections. Implementation The development team upgraded from traditional MATLAB-based ASR models to a more sophisticated, TensorFlow-powered end-to-end ASR system. This new system uses the International Phonetic Alphabet (IPA) to convert sounds directly into phonetic symbols, efficiently supporting multiple languages within a single system. Key features include: Conclusion Analyzing unique voice characteristics offers endless possibilities in various business areas. More secure than traditional passwords, voice recognition can safeguard customers’ money and sensitive information, like health records. Quick processing of client support requests, easy and non-intrusive authentication will both please the customers and make business more efficient. Voice recognition can even become a key selling feature in your product – like training pronunciation of language learners. SciForce has rich experience in speech processing and voice recognition. Contact us to explore new opportunities for your business.

Imagine a classroom where each lesson seems tailor-made for you, the course curriculum adapts to your pace, the materials are selected according to your interests, and even your questions! Sounds futuristic? With the integration of AI power into traditional learning management systems (LMS), it’s already becoming a reality. With AI, an LMS becomes a full-fledged learning tool, offering exceptional learning experiences that even underachievers would be amazed by. Learn more about smart and incredibly personalized AI-based learning management systems. While traditional LMS can be compared to a classroom, where students communicate with a teacher, an AI-driven one is an individual tutor for each student. This digital tutor is always available, offers tailored learning resources for each student's unique needs, and corrects mistakes in assignments swiftly. Let’s see the contrast between traditional and AI-driven LMS in more detail: All the capacities of smart digital education are possible thanks to decision trees and neural networks integrated into AI-driven LMS: Teaching efficiency AI-driven LMS provides teachers with useful tools that simplify everyday tasks. This lets them focus more on improving teaching methods and developing customized learning paths for each student. Data-Driven Learning How do teachers analyze student performance in traditional education? Check their assignments and activities during lessons. It takes a lot of time, limits individual approaches to each student, and lacks real-time insights. Let’s see, how a data-driven approach offered by AI-powered LMS can tackle this challenge. Intelligent Course Management The old-school approach had educators wait for occasional student feedback and guess if it was too easy, too challenging, or just boring. With an AI-empowering LMS, timely feedback is now possible for teachers. This allows them to refine their course materials according to the needs of current students, not next semester. Deep learning models and recurrent neural networks track and analyze students’ interaction with the platform, helping to understand real engagement and comprehension rates. Advanced Natural Language Processing (NLP) algorithms can analyze student’s feedback and mark the content as engaging or boring, too difficult or too simple, etc. Let’s see how it can work in practice! Imagine that students often replay a specific video fragment. Perhaps, it’s because the explanation is not clear enough. What does AI do? Streamlined Administrative Routine As per McKinsey research, teachers work about 50 hours per week, spending only 49% of their time in direct interaction with students. Technology can help teachers reallocate 20-30% of their time for supporting students, instead of doing routine tasks: 1. AI-Driven Learning Solutions Developing all kinds of AI-driven solutions for educational institutions, EdTech companies, and internal training systems for businesses: 2. Data-Driven Education 3. Workflow Automation 4. Hi-Tech Learning Experience

Last year, when Open AI introduced to the world ChatGPT-3, it absolutely revolutionized the field of artificial intelligence and changed the lifestyle of millions of people. This generative AI tool can create a broad answer to almost any question and is considered to be one of the best chatbots that have ever been created. Right now, generative AI is the newest form of artificial intelligence, and it's making a huge impact in every industry, especially in EdTech. However, many companies are presenting their solutions and creating the illusion that generative AI can solve everything. At the same time, many experts are raising concerns regarding the social and employment implications of generative AI, stating that new tools can destabilize our society. When we are talking about the Education field, there are many different opinions on AI: some teachers have rejected AI in education, having concerns about its impact on the quality of the study process, and other instructors compare the use of chatGPT to the use of calculators in school. So, let's explore the technology behind the term generative AI. Generative AI is a type of AI system that can create text, images, or other forms of media in response to various prompts. The most common approach is basically providing the text as an input prompt. For example, Large Language Models (LLMs) such as ChatGPT and GPT-4 respond to textual prompts by producing corresponding text. Such systems definitely have contributed to the popularity of generative AI, and increased interest in the application of such tools in education and other industries. However, the spectrum of GenAI goes far beyond text-based inputs – generative AI can handle a variety of input types such as image, voice input, etc. In the pictures below, you can see how GenAI works and what are the types of Generative AI based on data: Let us provide you with examples of different types of input and output data: An effective remedy: A few approaches can be used to solve the issue of factually incorrect information that GenAI creates. And the most common and effective ones are post-processing fact-checking and implementation of the feedback system. Basically, the post-processing fact-check is the process when the model generates an answer, and a separate fact-checking system verifies the information. This system can, for example, cross-reference the generated text with a database of verified facts. The feedback system enables users to flag incorrect or misleading information. And, afterward, this feedback can be used to improve the future responses of the model. Without internet access, a standard LLM engine lacks knowledge of unfamiliar topics, resulting in false or no responses. An effective remedy: The model can be integrated with a search engine, or users can enhance the generated answers by giving the model additional sources for responses (for example, books or articles). Also, the model can be tuned for any particular case. For example, Open AI has recently published a tutorial on how to customise a model for an application for your particular purposes. AI in EdTech often requires collecting and analyzing vast amounts of student data. Ensuring the privacy and security of this data is crucial to prevent breaches and misuse. Training data used in LLMs do not include any confidential information like biometrical data, medical personal data, banking data, etc. However, because of the fact that LLM training requires a large amount of data, the process of content filtering needs to be done automatically, and privacy filtering algorithms can be imperfect. So, it is possible that some sensitive or confidential data can leak. An effective remedy: Data anonymisation or de-identification can effectively solve that problem. These processes aim to remove personally identifiable information from datasets, allowing for data analysis without compromising privacy. There are many effective types of data anonymization methods, like deletion of direct and indirect identifiers, pseudonymization/tokenization, data masking, introducing statistical noise, data aggregation, and synthetic data usage. Even in the case when training data was sorted out, and inappropriate content was filtered before training, LLM can still generate harmful content. Some conversational AI models like ChatGPT use training techniques involving humans to prevent certain types of content from being generated, but it still happens occasionally. Thus, there is a need in the development of appropriate content filters to avoid harmful and inappropriate content generation. An effective remedy: The problem with AI bias can be solved at early stages by testing data and algorithms and using best practices to collect the data. To address the issue of bias in AI systems, we usually start by thoroughly understanding the algorithm and data to assess high-risk areas of unfairness, examining the training dataset for representativeness, and conducting subpopulation analysis. Also, it is important to monitor the model over time and establish a comprehensive debiasing strategy that encompasses technical, operational, and organizational actions. Also, one more effective way to fix this issue is using human feedback during fine-tuning. Some examples of using such an approach are InstructGPT and Reinforcement Learning with Human Feedback (RLHF). Even though the implementation of GenAI in Edtech might bring some challenges, the ability to change how students interact with learning resources overcomes these drawbacks. It is important to understand that any technology has its imperfections, however, as we demonstrated earlier, such difficulties can be solved. Thus, we all should bear in mind that AI in EdTech is not only about intelligent responses, it's about creating a secure and unbiased educational environment for every student. Although, at this moment, genAI has some flaws, advantages are overtaking the disadvantages in many cases. For example, one of the biggest strengths of LLM models is their capacity to produce grammatically and syntactically accurate language when given existing text. LLM models acquire this skill through training with properly written English texts. During output generation, each word is chosen based on its likelihood, considering the previous context and the model's learned knowledge, which heavily relies on the grammar and syntax from the training data. Such a capacity allows people to use generative AI in EdTech in the following cases: Users can ask LLMs to summarise long texts while keeping the meaning and main details. This is a very helpful way of extracting important information from complex texts. Also, LLM can simplify information that is too hard to understand. Generative AI can help in creating course materials. For example, quizzes, exercises, explanations, tests, and summaries, benefit teachers who need diverse content. LLM models are capable of comparing two texts and detecting their differences and similarities. Researchers can ask AI to analyze two research papers and highlight variations in conclusions, and approaches or set any other relevant criteria. LLMs can be used to improve clarity and correct mistakes in any trained language. Moreover, besides the rephrasing, we can ask AI to compare both versions of the text and provide explanations for the changes made. This allows students to gain insights and learn from their own mistakes. Generative AI has a great capability for the translation and explanation of terms. In many cases, AI can be better than regular dictionaries and translators as it takes into account the context of the words. Also, it can cover a lot of idioms and phrases in English. Based on the description, we can use LLMs to create different exercises and tests for students. Here everything depends on the desired type of exercise and how well-detailed the description is. You can even ask the tool you are using to provide an explanation for the correct answers to the exercise or test. However, we must remember that the LLM's output may not be perfect, and corrections might be needed. GenAI tools can become extremely useful for designing and structuring course materials like syllabi, lesson plans, and individual assignments. They can also personalize the content of the course to match individual students' knowledge gaps, skills, and learning styles by practicing problematic areas and creating interactive exercises. LLM models can be used as general-purpose language classifiers. This means that they can classify input text into a variety of predefined categories. For example, we can ask it to tell us what language a text is written in, the emotion/sentiment of the text, its writing tone, etc. This list does not fully cover all possible applications of generative AI in education. Some of these use cases may not be suitable for all types of data and tasks. To achieve the best results, we suggest you get insights from successful implementations, find effective strategies, and tailor them to your specific case. However, to ensure the efficiency of generative AI applications, lecturers should ensure that the following principles for the effective and ethical use of AI are well-explained to students. Even though AI is gaining popularity everywhere, it's crucial to understand not only the advantages of its usage but also the limitations and potential risks. By using genAI's language capabilities, the EdTech sector can truly benefit from improved content generation, summarization, translation, and explanations. However, we must be extremely cautious because genAI might produce incorrect or nonsensical information. In order to optimize the benefits of this technology, integrating generative AI with expert systems can ensure the delivery of accurate and reliable knowledge, which will definitely positively change the educational experience for both: students and lecturers. Our conversation doesn't end here. We are truly eager to learn from you too! How are you integrating GenAI into your educational responsibilities? Share your insights and experiences in the comments below!

We have already written several posts on the ways in which modern technologies are changing education. We have discussed how IoT can make the classroom a safer and more personalized space and explored new challenges and demands for future specialists. We also talked a bit about gamification, microlearning and AR/VR application as e-learning tools. However, one of the most critical changes we are witnessing in education is that the school is changing its image — and its mission — from a place to study hard and show respect to a place where you can be entertained as well as taught. In this new environment, teachers are challenged to adopt new attitudes and technologies immediately — the demand that averts rather than attracts. To help teachers, we have selected the best (in our opinion) AR applications that can be used in the classroom from preschool settings to high schools. Narrator AR The Narrator AR app is designed for the youngest children aged 3–5 who are mastering handwriting skills. It encourages children to write words and letters with a pen and paper, then takes the child’s handwritten word and launches it off the page using augmented reality (AR). To make it safe for little users, the application is free from in-app purchases, and ads and does not require a constant Wi-Fi connection. iphone.apkpure.com Catchy Words AR Catchy Words AR is a simple educational word game for ARKit-powered devices. The player catches floating letters without touching the screen and arranges them in the frame so they make up a word in English, solving a word puzzle. Intelligently combining movement and learning, this app is good for young children, who are only beginning to read and spell. https://apps.apple.com/ EKID* *— The world at your fingertips EKID — The world at your fingertips is an AR app for children up to 8 years old that helps them explore the world around them with the help of augmented flash cards. The application explores animals, including mammals, insects, and other creatures, and various modes of transport. The application is free from in-app ads, however, there are in-app purchases for further flashcard content. Though there might be more specific information on the flashcards giving more details on each subject, it is a good introductory application for young learners that provides endless fun for children to explore the app at their own pace whilst embracing the interactive technology. https://www.educationalappstore.com/ Figment AR Figment AR allows users to turn the world around them into an augmented funhouse. Users are able to create imaginative scenes and add interactive components like emojis, animals, and other playful objects, along with environmental effects, such as fireworks and snow. AR Flashcards These AR flashcards cover a variety of topics for elementary school students, including alphabet, shapes and colors, simple addition, and space objects. Children can turn their devices towards a flashcard to see beautiful 3D objects on the screen and can interact with them and hear lots of interesting facts if they just tap the info button. JigSpace JigSpace is an educational app that offers a library of knowledge. Each so-called “Jig” is a 3D presentation of how everyday things work, explained in simple steps and viewed in visual terms in augmented reality. It’s a flexible collaborative app where users can create their own Jigs. Still quite basic, some of the Jigs include how a car engine works, how a coffee machine works, and what’s inside the planet Earth which makes them interesting for older children as well. Anatomy 4D Anatomy 4D is a free AR app that lets students interact with the human body. At first, students print out a 4D-enabled image of the human body or heart from the app’s Target Library. Then the app uses the device camera to bring to life a three-dimensional image, revealing the spatial relationships of our organs, skeleton, muscles, and body systems. Visually stunning and completely interactive, Anatomy 4D makes a step forward in education creating an imaginative and compelling environment for students. Curioscope Curioscope is another physiology and anatomy app. The company offers a wearable T-shirt, called Virtuali-Tee, as a product that can be viewed through a mobile device app to open internal organs, allowing students to dive into an anatomy learning experience. https://www.curiscope.com/ Elements 4D Elements 4D is a classroom app for learning chemistry that allows students to view and interact with 36 different elements, providing detailed information such as atomic weight, fun facts, and trivia about each. The manufacturer provides paper or wooden blocks inscribed with chemical symbols, and the app transforms them from inanimate objects to dynamic representations of each depicted chemical element. Students can work in pairs or small groups with an iPad to bring the element blocks together and test their predictions. CoSpaces Edu Dedicated to older students, CoSpaces Edu allows them to build their own 3D creations out of a library of content, then animate their creations with code, and explore them in augmented reality or virtual reality. Students can also project their virtual creations onto any surface in the real world in AR. The app promises to improve digital literacy skills, enhance creativity, and promote creative collaboration. https://www.youtube.com/channel/UC6VsnmaKQ9MNRpJbFsIhoGw/ As you can see, AR is a powerful tool to ensure a more creative and engaging learning environment for students of all ages and for any discipline.

In recent decades we have seen technology changing our world in any sense: what was considered to be Sci-Fi ten years ago is around us now. But when you ask people which sphere remains outdated, you can hear a lot of complaints about the classroom. Despite new whiteboards and projectors, the schoolrooms of today are strikingly similar to those of 50 years ago. Students sit in a room together, listen to the same explanation and complete the same lessons with little variations no matter their learning styles or mastery. Some students are left behind, while others are bored and unmotivated. AI is believed to be exactly the remedy to change this. The explosion in cheap computing power, development in algorithms, and the increase of available data are making possible new, sophisticated tools to teach children in a more personalized way. Yet, the school resists. In this post, we’ll try to explore the benefits and the risks of introducing AI in the classroom 1\. Personalized and Customized Learning: It is probably the most famous argument for Artificial Intelligence, since AI can let children choose everything: the learning pace, the curriculum, the form of education, and the educator. By using predictive computing, AI can learn students’ habits and propose the most efficient study schedule for them. On the other hand, AI can give teachers a better understanding of how their students learn and allows them to collect insights about every student they teach, develop an individualized approach, and handle even the toughest kids easier. 2\. Adaptive Group Formation: No class is truly homogeneous: there are always more and less academically inclined kids. By analyzing learner information, AI can generate groups whose motivation or skills are particularly suited to a certain task, or groups that balance one learner’s weaknesses with another learner’s strengths. 3*. *24/7 response: Every person has a biorhythm and the brain function of larks and night owls differ. But at a traditional classroom, all students are equal before morning hours. Moreover, they cannot contact teachers whenever we need an answer to a question or suddenly “feel like studying”. AI doesn’t need to take breaks, it doesn’t get annoyed because someone called at 1 am, so everyone can contact a virtual tutor whenever he needs academic assistance. Virtual humans like avatars, digital assistants or chatbots are cost efficient and can work all day round, seven days a week in repetitive and time-consuming tasks no human enjoys doing. 4\. Virtual Reality Learning: Speaking about the virtual world, VR-assisted learning allows for educational support in authentic environments and extends the boundaries of the classroom. Students can visit places we’ve never been to, do something we’ve never done and get closer to things they learn about — space and nature, complex projects and concepts. 5\. Learning for all: The challenge of education is tough for every child, but it gets even more severe for kids with special needs or children of emigrants. They have to cope with the learning process, the environment they learn in, their community and the lifestyle. AI programs that augment the educational experience for the disabled are already in development by companies like Facebook and are aimed at granting learners with special needs a greater sense of autonomy. At the same time, AI-driven machine translation projects try to bridge the language gap for many second language students that is a possibility to foreign students to understand their teacher in real time. 6\. New methods of teaching: If other points concern class management and organization, this concerns real innovations in pedagogy. Game learning with the help of bots, Intelligent Tutoring Systems that challenge and support learners using different algorithms, intelligent moderation and real-time problem solving are just a few examples of new concept of the learning process that can disrupt the traditional classroom. 7.Assistance to teachers. AI can help teachers be more effective and efficient in their work in several ways. For example, they can outsource grading to specialized apps sparing a large portion of their time. Besides, AI can improve the course and the relevant textbooks by analysing patterns in which a large number of students submit wrong answers to the same questions and attracting teachers’ attention to such areas of improvement. 8\. Increasing tech experience for students. These days, technology is needed in every professional activity and the importance of acquiring STEM (Science, Technology, Engineering, Math) skills grows. So, it’s better that children start learning to use it as early as possible. The use of artificial intelligence shows the power of tech and coding and might encourage schoolchildren to proceed with learning STEM. _Source:_ _https://medium.com/ai-revolution/when-will-the-first-machine-become-superintelligent-ae5a6f128503#.g8ydf092h_ Obviously, all developments come at a certain cost and usually entail falls in other areas. Bringing Artificial Intelligence to classroom may not be always beneficial for schools and learners and their families. 1\. Cost: The challenge that schools face immediately is the high cost of the new technology. When combining the cost of installation, maintenance and repair, as well as the cost of power consumed by new robot teachers, it becomes clear that AI is expensive. 2\. Lack of personal bond: It may be true that smart machines have more information and this information is more trustful and recent than that of human teachers. However, educators are not just keepers of knowledge, they give children personal guidance, influence their worldview and lead them by example. Substituting personal interaction with machine-based education can lead to educational oversights that hurt learners more than help. 3\. Children’s ability to learn from a virtual assistant is still unclear. The biggest open question is whether students will be motivated enough to study when teachers are not there to supervise.The teacher is a central figure during the formative years, and sometimes it’s the wish to impress a teacher that makes a kid eager to master the subject. Sometimes, on the contrary, it is the teacher who embodies the power and makes a reluctant child learn. How will children react to the absence of such a person in their life? 4\. The attention span and the ability to multitask reduces. New technology shortens human attention span, as research suggests. We use so much help from machines that our own abilities shrink. By introducing more machines at school, there is a great risk that children will become so used to relying on AI that their abilities to multitask or to focus will get even smaller. Artificial Intelligence is quite often depicted as an evil power able to ruin the Earth and wipe away mankind. Though it is an artistic exaggeration, AI can lead to societal shifts, including those at the classroom, that are quite harmful and even dangerous. 1\. Unemployment: At present, teaching is one of the biggest professional branches. Therefore, if we replace teachers with machines, it will create less of a demand for human educators and, consequently, it will lead to high unemployment and protests. Just remember the times of the industrial revolution when people were replaced by machines with same functions there were strikes and wars. 2\. Addiction: We know that our reliance on machines can lead to technology addiction. A school is traditionally a place where children dump their mobile phones and get to open a traditional book. With tech implemented in every classroom, kids can become too used to having their devices always ready for them. As a result, we can end up with a bunch of socially-unadapted technology-addicted adults in several decades. 3\. AI-based power: The sad truth is that the power belongs to those who have means to control our minds. Even without thinking of possible biases fed to learners by the officially distributed software, there is always a risk that someone hacks the code and gains the power of spreading violent, inappropriate information and propaganda. 4\. Widening of the rich-poor gap: At the early stages of AI conquering the classroom, bots and other AI learning tools will require a student to have a tablet or a laptop. Of course, nowadays not every child has these gadgets, so having AI will become a privilege of the rich. However, it is believed that with the development of new technologies, robots and AI will become more common, while having a human tutor will be a luxury. 5\. Mind control: This time, quite literal. Chinese high schools are currently busy introducing technology that either scans students’ faces every 30 seconds or read their brainwaves, looking to see if students are paying attention or losing interest. Besides from being stressful and innervating to the children and probably disrupting their joy of learning, AI-powered tools also run the risk of creating classroom-based, mini-surveillance states. Despite having great benefits for schools and success in other spheres and industries, AI hasn’t made huge inroads into schools yet. That is partially due to how conservative the field is compared to other areas and due to the limited resources. However, the biggest obstacle might be the concern over the influence AI might have on children. In this sense, introducing AI at school can be compared with the decades-long debates over the GMO: recent studies might have proved it safe, but the prejudice remains on agenda. Yet, it is a cheaper option, and it is often the price (and laziness) that underlies our choices and pushes us forward. So, once AI outbeats a human teacher in cost-efficiency, we all will welcome robots at our schools — for better or for worse.

People have always wanted to know their future — from astrology and Tarot cards to modern futurology have been providing them with more ideas about the future and the changes it will bring to us, our families, and mankind. We can, of course, sit cozily on a sofa and reread old Sci-Fi books and keep fantasizing about the future which will never be true — or we can study the current trends and try to get ready for what is coming and prepare ourselves and, above all, our children to their adult lives in the changed world. The most obvious fact about the future is that technology will dominate both our everyday life and market. McKinsey Global Institute estimates, for example, that by 2030, artificial intelligence will have displaced up to one-fifth of the global workforce. At the same time, new technology will create new jobs, but what will they be like and what skills will be necessary for our children and for us? Labor Market Trends As stated in the WEF Future of Jobs Report 2018, technological breakthroughs change the frontier between the human jobs and automated machine tasks, global labor markets are undergoing massive transformations, and people are largely unprepared to them. 1\. Technology will advance rapidly in several directions: AI, Big Data, Cloud, and high-speed mobile internet. 2\. The frontier between human and machine tasks will shift significantly: in the past year 71% of total task hours were performed by humans, and only 29% by machines. Even in five years, the ratio is expected to be 58% vs. 42%. 3\. The spreading of new technologies will shift the core skills required to perform a job. The Future of Jobs Report estimates that by 2022, no less than 54% of employees will require re- and upskilling. 4\. The geography of production, distribution, and value chains will change in response to the changes in the task distributions. Even now, the necessity of skilled local talent is considered to be more critical than the labor costs (74% vs. 64% of respondents). Not surprisingly, the jobs landscape will change as well with roles based on and enhanced by the use of technology, such as AI and ML Specialists, Big Data Specialists, Process Automation Experts, Robotics Engineers, Blockchain Specialists or already established roles such as Data Analysts and Scientists, and Software and Applications Developers. Besides, to counter the growth of robotics, the market will need more distinct ‘human’ skills, expanding the need for Sales and Marketing Professionals, Training and Development, People and Culture, and Organizational Development Specialists and Innovation Managers. It is evident, that companies will have to manage the skill gaps resulting from the adoption of new technologies by sifting to almost complete automation, hiring new staff, or retraining existing personnel (or hoping that they will eventually pick up the necessary skills themselves). It is believed that the likelihood of hiring new staff with relevant skills is nearly twice the likelihood of strategic redundancies of staff lagging behind in new skills adoption, so maintaining knowledge at an acceptable level or even thinking ahead of new demands becomes a vital skill. How we should respond? What is evident now is that the days of a lifetime job, and of a single curriculum and training that fits that job, are gone for good. Both children and adults will have to study for their whole life always ready to embrace new trends and develop new skills. In this lifestyle, it is important to be equipped with foundational skills that will stand you in good stead regardless of a specific job. Flexibility Probably, in these settings, flexibility and the readiness for change become critical skills to be developed from an early age. We can’t know for sure what skills will be required in the future, but we can be ready for them. When technologies change fast, we can spare our children’s time in learning current trends, but give them a broader and more stable perspective on the evolution of the world around them. The four C’s In his book, _21 Lessons for the 21st Century_, Yuval Noah Harari argues that general-purpose skills should dominate schooling and children should be taught critical thinking, communication, collaboration, and creativity rather than technical skills and rigid disciplines. Only in this way they will be able to learn new things and preserve their mental health in unfamiliar situations as well as fit the emerging demand for “human” skills in industry. In our article, How to Build Data Culture and Make Data Your Friend, we state that critical thinking is the basis for building data-driving teams — now and in the future. STEM Despite the focus on general education, it is impossible to ignore the utmost need for science, technology, engineering, and math (STEM) skills that are needed to fuel the 4th Industrial Revolution. The curricula in these disciplines are developed in collaboration with industry experts, giving young people a clear and straightforward path into STEM careers and giving professionals ways to upskill with respect to the demands of their industry. Balance One of the most difficult and important tasks of education is to foster the required specialists without creating skills gaps in other areas. Growing the STEM workforce, for instance, must not be done at the expense of creativity, social skills, and collaborative problem-solving, the abilities that resist automation and therefore will be valued for decades to come. On the other hand, limiting education to soft skills will end up in the lack of scientists and engineers needed to build the new economy. Joy For all mammals, not speaking about humans, learning through play is vital, and digital technology can support us in this. There are multiple efforts to gamify the process of learning to gain foundational skills in an enjoyable way. Eben Upton, Raspberry Pi CEO names such skills as numeracy, literacy, and critical thinking that are integral for future education and can be tackled by teaching children basic computer science in the games format. Far beyond simple coding for kids, this approach is intended to prepare everyone, independent of their age, for a more digital, more automated world. Gamification is enjoyable but it also offers a glimpse into how to combine the capabilities of people and machines, automate repetitive tasks, and free our time to more creative aspects. All in all, we are on the verge of the 4th Industrial Revolution, and it has huge potential to boost economic growth and raise living standards. But first of all, we should prepare ourselves to view it as an opportunity and not a threat, so we need to get serious about education and skills both for the current workforce and future generations.

“The world is changed,” said Galadriel in _the Lord of the Rings_. What the elf queen could not foresee is the way the world would change. In her times magic was the only means to communicate and get information across the distance, now almost everyone has a smartphone equipped with sound wave recorders, cameras, acceleration gauges, gyroscopes, magnetometers, Wi-Fi internet and whatever manufacturers find attractive enough. We think that these technologies make our lives more comfortable and effective, and in this belief a new — Internet of Things — paradigm is born. What is the IoT paradigm? In IoT, the whole world is turned into a global computer, which gets data from everywhere. The processor works on big data in some form, while the output is considered to be crucial for the mankind to survive: to use energy more efficiently, to evolve as a civilization, to protect health, welfare, and offsprings. Such shift in understanding the IoT not as a technology but as a philosophical category explains why it has penetrated all spheres so quickly: from households and corporations to schools and hospitals. In this blog post we’ll discuss the benefits of the IoT for education — the sphere that still remains farther to the background in terms of the IoT application, but can benefit from it at all stages. Besides, schools are meant to prepare students for entry into the adult world. As the IoT changes the landscape of their futures, it is crucial to change the space where students spend their formative years. Most probably, when it comes to IoT-driven education, we imagine a certain technically advanced classroom equipped with smart whiteboard. It is valid, of course, but the IoT for schools begins far before stepping into classroom: Internet of Things (IoT) application begins in connected schools buses that can tell parents when they would drop off children and provide the latter with internet access to revisit their homework. IoT automatically adjusts lighting and audio visual settings to a teacher’s specifications and monitors hallways and building perimeters to keep students safe. Within school buildings and in classrooms smart HVAC systems may help save money and energy by functioning only when needed. More Internet-connected devices, such as tablets, paired with analytic technology, can help educators monitor student attendance and activity during testing and classwork, and ultimately provide more agile, personalized instruction. IoT enables this innovation through a growing collection of internet-connected technologies and devices — such as connected school buses, smart lighting and security cameras — all providing real-time data and valuable insights to students, parents, faculty and administration. The most common IoT devices used in the classroom But returning to education process, how can smarter devices help educate smarter and tech savvy students? Here are some instrumental changes that those involved in education will soon begin to see: Real-time insight and correction Insight into a student’s progress, including test results, end-of-term reports, and possible peer reviews is a cornerstone of education. In future, students’ cognitive brain activity could be measured by neurosensors, and haptic vibrations could be sent to a student’s wearable or tablet to discreetly guide them back on task in case of them being distracted. Even now, smart devices make the process of knowledge monitoring more consistent and continuous: when IoT-enabled tablets replace traditional pens, teachers will be able to monitor the students’ notes that students in real time and to track their understanding. If they see that a student absorbs the information slowly, they can immediately take action by sending extra notes or exercises to bring the class up to the same level. Better visualization The IoT paradigm means interactive content will soon become the norm, and everything from distributing exercises to marking papers will become automated and streamlined. Smart whiteboards (here they are at last!) and screen can provide for optimized learning and at the same time engage more students into the education process. Visualization of difficult abstract concepts and immediate access to new information on the click can ensure better and more eager learning. Safer environment Smart tech is capable of making schools safer places. Such devices as, for example, IoT-enabled wristbands can register students’ entering and exiting the school premises — so no more sneaking out at lunchtime or between lessons. This measure also helps to prevent intruders and other unauthorized persons entering school premises. It looks extremely promising, but some establishments and instructionists remain concerned about implementation of IoT in the real schooling environment. First of all, to integrate the devices in the classroom, the school will need to have capabilities such as reliable Wi-Fi, robust network bandwidth, a certain amount of devices for students, network analytics as well as teacher training. In other words, both the IT equipment and teaching strategies should support and have capabilities to use IoT in the classroom. Besides, some devices and applications may not be compatible and therefore would impede the organization’s ability to create an adequate IT environment. At the same time, when devices begin measuring and collecting data from students, they are automatically putting the security and privacy of students at risk by storing sensitive data in an Internet-based network of connected devices prone to cyber-attacks. If connected devices penetrate all spheres this can compromise a student ID linked to an individual’s health record and even to the family’s financial information. To sum it up, two main concerns about IoT in education are expensiveness* and *security. However, students already live in a connected reality: in the smart cities, in buses and cars linked to the internet, with smartphones, smartwatches, tablets and laptops. Can they learn in a classroom with pen and paper note-taking from word of mouth presentations? To educate new humans schools have to adopt new technologies — and they will become smarter as they have no choice.

“Here we are now, entertain us,” if we could choose one phrase that would describe the modern learning process, this quote would suit best. Games have been present in education for a long time: role-playing games aimed at preparing students for real-life scenarios have been used in a wide range of fields — from the second language teaching to corporate trainings, and various types of simulators are starting to gradually replace driving lessons. Turning to this strategy in e-learning would have been unavoidable. Yet, gamification is not equal to games _per se_. It is referred to as the “use of game design elements within non-game contexts” (Deterding et al., 2011, p. 1). The central idea is to take the “building blocks” of games, and to implement these in real-world situations, often with the objective of motivating specific behavior within the specific situation. It is widely accepted as promising and is taken quite seriously by the e-learning industry: by 2018, gamification in e-learning will grow to a 5.5 billion USD global market. 1\. Better learning experience — having more fun in the process of learning will result in higher levels of engagement which, in turn, will lead to better recall and retention. 2\. Better learning environment — e-learning, especially when paired with gamification, provides a more user-oriented informal environment where a student can practice without the fear of being criticized. 3\. Instant feedback — learners know immediately what they know or what they should know. 4\. Universality — it can be applied for most learning needs, including induction and onboarding, product sales, customer support, soft skills, awareness creation, and compliance. As we can see, these benefits are chiefly related to increasing learners’ engagement which helps achieve higher levels of retention and ultimately remembering. In 1956, Benjamin Bloom and his fellow educational psychologists developed a classification of levels of educational behavior: cognitive (knowing), affective (feeling), and psychomotoring (doing). At the cognitive level, Bloom suggested six levels: basic knowledge, comprehension, application, analysis, synthesis, and evaluation (Bloom et al. 1964). In the 1990ies, the taxonomy was updated to reflect the changes in the society and the relevance of the skills in the future years. The updated taxonomy defined the categories as remembering, understanding, applying, analyzing, evaluating, and creating (Anderson et al. 2001). Simulation and educational games are quite effective for the three lower levels of the taxonomy, contributing to motivation, emotion, and attitude. To give the answer how we can increase such motivation and engagement by deliberately addressing human needs, educational psychologists turn to motivational theories that explain the success of gamification by addressing the human needs of self-fulfillment, competition, and independence.One of the theories that has been successfully applied in the context of gamification, is the self-determination theory (Ryan & Deci, 2002) that postulates three basic psychological and intrinsic needs: _The need for competence_ assumes that every human strives to feel competent when deliberately influencing the environment they interact with. _The need for autonomy_ refers to psychological freedom and to volition to fulfill a certain task. In this context, autonomy refers both to _(a) decision freedom_, which implies being able to choose between several courses of action, and _(b) task meaningfulness_, which implies that the course of action at hand conforms with one’s own goals and attitudes. _The need for social relatedness_ refers to one’s feelings of belonging, attachment, and care in relation to a certain group. Research (Sailer et al. 2017) suggests that the popular gaming elements such as badges, leaderboards, and performance graphs positively affect competence need satisfaction and perceived task meaningfulness/autonomy. On the other hand, introduction of avatars, meaningful stories, and teammates positively influence social relatedness creating a sense of belonging to a certain group. With gaming elements being effective primarily for lower levels of the cognitive domain, the question remains whether gamification can be used to tackle more complicated and abstract entities, like advanced-level grammar or computer programs and algorithms. Studies suggest that even for advanced online courses, the key factors are a learner’s initial interaction with an online platform (Tyler-Smith, 2006) as well as a learner’s control (Chou & Liu, 2005) and motivation (Keller & Suzuki, 2004). Here again, engagement and motivation play the decisive role. At the same time, there is no evidence of increase in the performance, tackling the upper cognitive levels — analyzing, evaluating, and creating — which are crucial for programming. Besides, learning styles and, therefore, the attitude to gamification in learning, varies depending on the type of the personality, as well as on the culture and environment. Gamification is a trendy phenomenon that probably never will attract all. Nevertheless, there is definitely a need for e-learning to address the issues of boredom and loneliness in online platforms. And in this case a badge or a leaderboard, even without being an ultimate goal for anyone, can be used as a fun gimmick creating a positive attitude in online courses, including the most complex and challenging ones. References Anderson, L. W. and Krathwohl, D. R., et al (Eds..) (2001) A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Allyn & Bacon. Boston, MA: Pearson Education Group. Bloom, B. S., Mesia, B.B., and Krathwohl, D. R. (1964) Taxonomy of Educational Objectives (two vols: The Affective Domain & The Cognitive Domain). New York. David McKay. Chou, S. W., & Liu, C. H. (2005) Learning effectiveness in a Web-based virtual learning environment: a learner control perspective. Journal of Computer Assisted Learning, 21(1), 65–76. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011) From Game Design Elements to Gamefulness: Defining “Gamification”. Paper presented at the 15th International Academic MindTrek Conference, Tampere. Keller, J., & Suzuki, K. (2004) Learner motivation and e-learning design: A multinationally validated process. Journal of Educational Media, 29(3), 229–239. R.M. Ryan, E.L. Deci (2002) Overview of self-determination theory: An organismic dialectial perspective in R.M. Ryan, E.L. Deci (Eds.), Handbook of self-determination research, University of Rochester Press, Rochester, pp. 3–33 Sailer, M., Hense, J.U., Mayr, S.K., Mandl, H. (2017) How gamification motivates: anexperimental study of the effects of specific game design elements on psychological need satisfaction. Comput. Computers in Human Behavior, 69, 371–380. Tyler-Smith, K. (2006) Early attrition among first time eLearners: A review of factors that contribute to drop-out, withdrawal and non-completion rates of adult learners undertaking eLearning programmes. Journal of Online learning and Teaching, 2(2), 73–85.

In the previous blogpost we started discussing the trendiest e-learning tools with an overview of AR/VR applications. Among others, we mentioned that such applications may be integrated with microlearning techniques to increase the impact on the student. In this post, we will talk more about microlearning: what it is and why it is gaining popularity among educators. Microlearning — as learning in short, digestible, bite-sized units — is a relatively new learning strategy known for quickly closing skill and knowledge gaps. It represents short, focused learning nuggets (3–5 mins long or shorter) designed to meet a specific learning outcome. Focused, short and easily accessible from devices such as mobile phones, tablets, and laptop computers in formats as varied as videos, blogs, games, quizzes, simulations, podcasts, or slideshows, microlearning is believed to yield results, where traditional approaches fail. The idea lying behind the microlearning approach is that modern learners are unable to memorize and digest the information given to them at traditional long learning sessions due to a number of reasons: 1. Learner-centric: microlearning nuggets offer learners a higher control in defining a personalized and flexible learning path to match individual learning styles; 2. Just-in-time: learners can access nuggets at the moment of their learning need; 3. Immediate results: microlearning helps quickly close a small knowledge or skill gap; 4. Diverse formats: it is easily accessible via devices such as mobile phones, tablets, and laptop computers in formats as varied as videos, blogs, games, quizzes, simulations, podcasts, or slideshows; 5. Budget friendly: production costs for microlearning is significantly lower than the costs for a major course production; 6. Fast-paced culture-oriented: microlearning is a solution that employees will appreciate because it is not as disruptive as a day of training or even an hour or two of eLearning, besides, it appeals to millenials living at a faster pace than previous generations. Despite a wide range of possible applications, the microlearning strategy still has disadvantages that need to be considered: 1\. Fragmentation of knowledge: in the long run, microlearning content could end up as a bunch of separate fragments that are not tied together. 2\. Lack of cognitive synthesis: so far, there is no proof that learners will be able to synthesize content from microlearning to construct appropriate mental models. 3\. Potential for confusion: with the wide variety of devices and formats available, some learners could have problems switching between them. Certain primitive forms of microlearning such as flashcards and quiz books have been known for over a century. Combine them with modern e-learning tools, add multimedia elements, employ gamification strategies, or take advantage of techniques such as spaced repetition — and they become more powerful, effective and engaging for learners. In the modern culture such brief and multimodal learning activities become more common in a wide variety of spheres, such as: _What kind of content shall be included? _Actionable content that can be broken into small chunks is well-suited for microlearning strategies. However, abstract or more complex content which may be more difficult for learners to grasp may need a blended solution which can develop foundational knowledge. _What technology shall be used? _The effectiveness of microlearning depends on its accessibility: if a learner can’t quickly find what they’re looking for, it’s less likely that they’ll put the effort in to use training pieces. Therefore, the learning management system should be easily searchable and provide excellent tagging so that learners can find what they need. _Who are the learners? _Though microlearning can be highly effective across all generations, younger learners or learners who are more technologically savvy may feel more comfortable with microlearning assets than those who use digital devices less frequently. Besides, web-based microlearning depends on learners having web-enabled devices readily available while they work. Without sounding too pompous, we can say that microlearning in its modern sense is more than an effective way to deliver training content. It’s an era-defining innovation that empowers educators to make students more knowledgeable every day, at their own pace and in the preferred format.