Role of Artificial Intelligence in the rise of Edtech

May 5, 2022

Is it true that new technological advancements can help in the boom of education? Is AI capable of automating and revolutionizing conventional methods for cultivating more engaging educational systems? What marvels does AI contain, and how has AI created a charismatic value to the Edtech industry?

Table of Content-

  1. Overview
  1. Edtech: A scaling Industry
  1. Artificial Intelligence (AI) role in Edtech
  1. How AI nurtures students with special needs
  1. Conclusion

Overview: 

Education technology (EdTech) implies gear and software that is used to educate children on a virtual level to improve classroom learning and augment student results. By leveraging technology for learning and teaching, EdTech platforms assist students in overcoming impediments and attaining comprehensive education. 

Moreover, Artificial Intelligence (AI) has aided in the expansion of Edtech by personalizing and streamlining tasks. Over the last decade, AI has evolved to play a massive role in education, both for teachers and students. 

Not only private institutions but even governments are recognizing the importance of AI. For example, The New Education Policy 2020, has also given the Edtech sector a boost by underlining the significance of AI. One of the major ideas in navigating the education system, according to the Policy, will be the wide use of AI in the disposal of linguistic barriers, in heightening the access, in education planning and management, as well as in both teaching and learning,

The Policy concedes and promotes the necessity to adapt to changes that AI has developed and it has tasked the NETF (National Education Technology Forum ) with identifying and categorizing the potential of AI and other technological advancements in the education industry and providing a quarterly analysis to the MHRD.

Edtech: A scaling Industry

EdTech has quickly become one of the most important areas, thanks to fast digitalization. The industry's amazing expansion has captivated massive amounts of investment and capital from all over the world.

The global education technology market was valued at USD 106.46 billion in 2021, with a compound annual growth rate (CAGR) of 16.5 per cent predicted from 2022 to 2030.

From smart classrooms to individualized coaching apps, Edtech is rapidly stretching its wings and earning recognition globally. Billions of dollars are being invested in the creation of EdTech software, with the United States, China, and India receiving the greatest funding.

According to Statista, the Indian Edtech industry is US$2.8 billion in value, and by 2025 it is expected to reach US$10.4 billion. At present, there are 9,043 EdTech startups in India and the number is continuously growing. Factors like India's booming internet economy – which has a total of 850 million(61% of the total population) internet subscribers – encourage this expansion.

Artificial Intelligence (AI) role in Edtech:

The regular learning strategy is to listen, memorize, test, and repeat. It's factory-oriented and insufficient. It's not a peculiar approach to consenting cognition. Aligning today's learning with future issues necessitates visionary teaching and learning methodologies. That's exactly what AI is enabling institutions to carve.

Adaptive and Personalized learning- 

When EdTech is commixed with artificial intelligence, an adaptive learning platform can be generated. To put it another way, the platform can trace each student's progress, skills, and knowledge rifts. 

With this data, the platforms can provide each student with incredible and individual practice, ensuring that their learning is optimized. 

Platforms can gauge and provide propositions based on assorted inputs from students; they are more comparable to "intelligent tutoring systems." AI may alter each student's knowledge level, learning speed, intended goals, learning histories, and a variety of other factors. 

As a result, frailties are identified, as well as course recommendations for growth, resulting in a highly individualized learning experience.

Quick responses- 

In the opening, we talked about how a dearth of regular help deepens the academic trammels of students. Well not anymore, as AI businesses have made gains in inquiry resolution using chatbots or technology, students can simply upload a picture of their problem and can get the easiest solution. 

The AI tools validate the situation and generate a solution. This abolishes the need for teachers and the reliance on them. Additionally, this provides learners with a space for involvement that is available 24×7, cutting down on the time it takes to discover answers while retaining the learning process.

AI benefits Educators-

One of the most basic AI applications has been to automate teachers' "operational" tasks. The majority of educators inscribe more than half of their time grading assignments, completing paperwork, preparing progress reports, organizing materials and resources for courses, and managing teaching material and infinity.

Artificial Intelligence can intervene in many of these duties to manage them efficiently for the educator, letting the teacher focus on higher-order tasks like teaching and assisting students. Hence, both students and teachers can be more productive.

Reliable Content-

It is most factual to argue that automation and education are inextricably linked. As a result, intelligent and dependable content created using AI has been able to reach every school setting. Students study better and have more efficient sessions when traditional books are converted into digital automated learning modules. For example, AI-assisted learning systems can determine if a student needs to repeat a lesson or go on.

Global Exposure-

Education knows no bounds, and there are several concepts to learn from all over the world. In reality, Edtech's AI and automation have facilitated students to learn from a wide choice of courses available across borders, regardless of their timezone. Above all, global learning empowers students to widen their horizons by enabling them to learn from anywhere, at any time.

How AI nurtures students with special needs:

AI also has a role in improving the lives of students with special needs. Owing to the text states, a student with a mental handicap can readily perceive the world around him. What seems to be a difficult message to read turns out to be straightforward text. Things that were once impossible or implausible for them are now routinely handy to them. 

Students with impairments can now enter a world where their constraints are understood and taken into account, thanks to artificial intelligence. With artificial intelligence accessibility, technology adapts and helps transform the world into a more inclusive one. There is a sense of equality, as AI places everyone on an equal footing, with or without handicap.

For students who are blind or visually impaired- 

VoiceOver is a screen reader that is built into iPhones. Although its primary function is to enunciate any email or text message, VoiceOver also uses artificial intelligence to describe app icons, battery levels, and even partial photos. For Android smartphones, there's TalkBack, which is similar to VoiceOver. It allows students to get the most out of their digital devices. 

Siri is the virtual assistant on the iPhone. Voice control allows visually impaired students to simply verbalize their requests, such as conducting a Google search, writing an article, or dictating a text message to a buddy. Students with vision impairments can utilize Siri to communicate with others.

For students who are deaf or hard of hearing-

Ava is an instant transcription tool that employs artificial intelligence to instantly transcribe a group of people's discussion. Its algorithm incorporates punctuation, the speaker's name, and relevant words from the user's dictionary. 

A simple technique for those with hearing impairments to participate in and follow a conversation with multiple people without having to lip-read. RogerVoice is a French group discussion transcription tool similar to Ava that is accessible in 90 languages. It functions similarly to Ava.

Apart from these tools, the failure to provide constant feedback is one of the most significant challenges in teaching students with learning disabilities. It can be difficult to slow down in a large classroom to assist a small group of children. With artificial intelligence's quick growth, students will be able to receive more reliable feedback that is directly related to their performance. Students will not be able to go on until they have demonstrated mastery of the idea, and they will be able to work at their own pace if necessary.

Conclusion-

Seeing the development in the current education system, we all can agree that the future of education i.e. Edtech is a blessing for the generations to come. Edtech guarantees the most effective and efficient knowledge by allowing the learning experience to extend far and wide. AI in the education business has ensured better learning experiences in the future by ensuring time management, customization, and much more.

Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure