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Natural language processing in education: Benefits and use-cases.

Natural language processing, NLP, is progressively changing the facing of education systems globally. The rise of technological advancement has only given an increase to the use of intelligent learning systems.

A significant component of these intelligent e-learning systems is conversational artificial intelligence, which helps streamline communication to facilitate natural dialogue between teaching systems and learners.

In this post, we will discuss the significant benefits of NLP in education as a part of conversational AI and notable use cases. Read on for more.

What is Natural Language Processing?

Natural Language Processing is a component of Artificial Intelligence that helps facilitate the interaction between machines and humans by using human language as the medium of engagement.

NLP is the technology behind search engines, voice assistants, and machine translation services operations. Although the term was first used to describe computers' ability to read, it has become an umbrella word for every computational language.

Natural language processing has two major categories, including Natural Language Generation, which is a computer's ability to generate words, and Natural Language Understanding, which refers to the ability to identify mispronunciation, slang, and different linguistic variations.

NLP has a lot of applications across various sectors, from chatbots in the business industry to social media monitoring and analysis. But one industry that is reaping from it currently is education.

What are the top benefits of natural language processing in education?

Fast implementation of NLP in learning can allow educators to get long-term operational and strategic benefits of language processing in education. Listed below are the benefits you need to expect.

1. Resource accessibility.

With conventional learning approaches, learners have to look for good instructors, while teachers have to wait for adequate students in the classroom to start teaching. National language processing in education bridges the gap of resource accessibility by availing sessions that can be accessed and conducted remotely and improve classroom management.

Natural language processing in education allows education stakeholders to quickly improve their digital learning systems and thereby increasing engagement rates because the system does not require complex infrastructure from the student's angle.

Students have valuable resources and platforms like chatbots where they can easily engage and have a great learning experience. Also, features that break long study sessions into short plans provide students with the ease of learning, allowing them to maintain their focus during lessons.

2. Intelligent teaching systems.

The use of artificial intelligence in learning systems changes static teaching methods into intelligent, adaptive, and dynamic teaching and learning sphere that smartly changes to various learners' requirements. One of the notable examples is the NLP-enabled voice assistant.

The concept of NLP is gaining prominence since a lot of people are looking to leverage the benefits of digital learning to improve their skills. Based on the need to provide personalized learning, chatbots are able to tailor their programs based on the skill level of your learners.

NLP supports Intelligent Tutoring Systems (ITSs), which have been gaining eminence in creating learning methods that can show methods of a concept explanation. Leading learning institutions have been seriously leveraging NLP to build ITSs, since it builds a better one-on-one learning interaction mediums.

1. Contextual assistance.

In real-world scenarios, educators sometimes find it challenging to address some pertinent learner requirements. However, natural language processing in education presents solutions to these problems.

For instance, NLP can help create learning solutions that efficiently enable teachers to meet the needs of students with disability. Similarly, it operates as a data supply system to deep learning algorithms for students' psychometrics.

2.  Adaptable learning environments.

NLP can pick communications representing repetitive interactions, such as queries related to courses, assignment submissions, and examinations, without hassles. Usually seen as tedious tasks for teachers, these mundane admin tasks can be handled efficiently by the ever-evolving NLP solutions, allowing staff to focus majorly on the core teaching activities.

As a performance enhancer, conversational AI makes learners to engage interactively with the learning systems. They grade and evaluate students through various channels such as videos, texts, and pictures.

Natural Language Processing provides the dynamism required in the current teaching ecosystem. Similarly, chatbots improve the feedback mechanism for assisting students in adopting the improvement path.

3. Ease of development.

The most important thing about NLP in education is that it does not need a lot of resources and can be implemented with ease in a short period. To be a little more precise, teachers can implement artificial intelligence in learning at 10% of the amount required to build a learning institution.

With adequate professional guidance, educators can easily plan, design, and create personalized interfaces. For example, with a stable NLP platform, teachers can access cheaper and customized e-learning solutions.

Effectively exploiting advanced course analysis, query-driven, and topic-level sentiment analysis categorization can be your final shop to facilitate the teaching and learning process.

Applications of NLP in education.

Most communications and activities happening in educational setup happens through text and speech. This, therefore, makes National Language Processing in education benefits and uses is a logical match. The following section discusses the applications of NLP.

a. Text summarization.

For the enormous amount of unstructured information found in the academic sector, NLP allows automatic text summarization is a pivotal factor in reducing the content to its essential elements to convey only the most relevant data.

This method in AI is known as extraction-based summarization used to produce a summary of any piece of work. In this technique, NLP employs the use of deep learning strategies to unstructured records to paraphrase and generate sentences or paragraphs that did not exist before.

You can apply automatic summarization at a documentation or research stage to get the most relevant details of what you need from the data source and enter them automatically into your document.

b. Machine translation.

With the increasing use of technology to break barriers between countries, NLP solutions for machine translations use deep learning techniques to translate texts and speeches into different languages.

Generic platforms offering Machine translation include Microsoft Translator, Google Translate, and Facebook Translation software. For more specific and specialized learning applications, you can design a customized machine translation tool based on your needs.

The systems should be aligned to a given language domain or trained to comprehend the terms used in a given profession, such as medicine, law, finance, etc.

c. Used to improve Academic writing.

Traditionally, NLP has had great success in the education world, spotting students' word and grammar mechanics issues and giving holistic grades for a five-paragraph essay. Apart from these limited uses, NLP systems can help students by offering actionable answers to certain sections of texts and speeches.

Students can apply this extra feature to improve their writing skills beyond grammar and word techniques when revising. For example, NLP analysis can identify whether basic elements in the content, such as the main topic, evidence, or argument, are present or absent in the work.

Alongside Automatic Writing Evaluation, AWE, NLP tools can provide students with low-level suggestions like vocabulary tips or higher-level feedback affecting the flow and structure of the content.

d. Used in behavior motivation in learners.

One of the difficult tasks teachers face is inspiring students to learn and do their work. A student only needs to feel bored for a split time before losing focus on the entire lesson. And this is why it is important to keep pupils motivated and engaged for maximum results.

Natural language processing and machine learning can analyze teachers' and students' language in class to analyze their mental status in every lesson. Educators can use this data to determine how learners respond to various learning approaches and find a way to make them more engaging.

 A robust NLP platform for educators can help identify students having difficulty mastering lesson concepts and suggest one who may need extra support. This can be achieved by NLP features that track, record, and analyze learners' behaviors and the mental status during class hours.

e. Improving students' reading skills.

If your students are struggling with reading, there are many NLP solutions for the education sector that can help you. AI algorithms can detect errors in the students reading skills and provide suggestions that can be used to solve the flaw in real time.

In real life, teachers cannot afford the time and resources to move around a class full of learners to give a similar suggestion to improve their reading abilities. This is why natural language processing is a real game changer.

Conclusion.

The adoption of artificial intelligence in learning is on the rise. In the quest to provide a personalized learning experience and improved flexibility in education, stakeholders are coming up with ways to create an intelligent e-learning ecosystem, such as the use of natural language processing in education.

Therefore, as an education stakeholder, prioritizing your operations around NLP is an important step towards giving a bespoke learning experience. Remember to analyze your needs and resources if you want to implement NLP for your education needs.