The Pathway to Implement Artificial Intelligence in Education

Artificial intelligence (AI) can teach us a lesson, literally. While data analysts are training machine learning models, these well-trained models can play a vital role in further imparting knowledge to students worldwide. After years of following traditional learning methodologies, the education industry is finally set to break through the monotony of theoretical learning.

With advancements in AI and machine learning technologies, educators across the globe are turning to new adaptive modes of imparting knowledge to students. Institutions from elementary to higher education are borrowing AI assistance to teach complex topics such as science, biology, and engineering. More so, in these difficult times of unprecedented lockdown, providers of artificial intelligence services are witnessing a high demand for virtual assistants, virtual tutoring, and administrative automation solutions to improve student success. 

Let’s explore some of the most effective and efficient ways to implement artificial intelligence in education systems and processes.

4 Significant Applications of Artificial Intelligence in Education

1. Automating Administrative Tasks

If artificial intelligence has achieved excellence at something, it is automating repetitive tasks that require a great deal of human effort and time. In the education industry, there are several non-teaching tasks being performed behind the classrooms that ultimately contribute to quality education.

Before exploring how AI can disrupt the education industry’s administrative department, let’s look at some of the education system’s most tedious administrative tasks-

  • Managing student records and paperwork
  • Maintaining financial budgets and service allocations
  • HR and personnel management
  • Organizing classroom materials, and
  • Looking after school’s infrastructure and power backup, etc.

Education administrators worldwide are beginning to incorporate  Robotic Process Automation (RPA) into organizational setups to accelerate and optimize operations. RPA can check and evaluate student application forms, assess the eligibility criteria, and categorize them as qualified and unqualified.

Administrative tasks

In addition to administrative tasks, RPA in education can also bring automation to everyday schooling tasks, such as-

  • Attendance tracking and maintaining annual reports, and
  • Maintaining daily assessment reports and progress reports
  • Scheduling academic board meetings
  • Processing financial transactions such as school fees and invoices, and
  • Planning educational events

The immediate benefits of integrating RPA into educational systems encompass reduced operational costs, improved efficiency, and increased productivity. Early adopters of RPA in the field of education are improving processes by almost 20% as suggested by global analysts.

2. Delivering Smart Content

In these uncertain times, where everything from industries to education is halted due to an inexorable virus, adaptive services are the need of the hour. Adaptive educational initiatives such as distance learning and smart classes are emerging as strong pillars aimed at customizing classroom learning.

However, the high demand for virtual courses and digitization of textbooks has unbearably increased the burden for teachers and administrators to supply digital content.

AI can assist educators in digitizing textbook content into structured content available online with Optical Character Recognition (OCR). The traditional technology can now be powered by machine learning algorithms to extract information from structured and unstructured physical documents, books, etc. Moreover, with machine learning abilities, OCR systems can create editable and searchable digital copies of learning material to facilitate online learning.

OCR System

With AI-powered OCR and Natural Language Processing technologies, educational service providers can create various types of intuitive smart content, such as-

  • Digitized study guides including chapter summaries
  • Topic-centered multiple-choice questions, and practice tests
  • Cloud-based interactive learning applications and platforms
  • AI-enabled content engines for planning lessons, and more
  • Simplified and gamified learning to explain complex topics with 3D visuals, and more.

In addition to OCR and NLP, other AI technologies such as machine learning and deep learning are propelling video analytics solutions to monitor students from remote locations. With video conferencing and video lectures, AI can assist teachers in streaming uninterrupted videos to students in far-off locations to improve accessibility.

3. Personalizing Learning Experience

Personalization has become a great catchword for a majority of global industries including eCommerce, video streaming, home decor, real estate, and more. IN the education industry, personalization is playing a vital role to enhance the learning journey for students and teachers alike.

With strong abilities to analyze patterns, AI can deduce the dynamic learning styles of students to develop tailored learning materials. Machine learning models can build custom learning profiles for each student to build virtual learning modules with automated evaluation and scoring functionalities.

According to global analyst firm Gartner, by this year 2020, at least 10% of higher education institutions are likely to employ smart machines to improve student success.

Personalized Learning

For practical implementation, personalized learning can be divided into four types of techniques as follows-

  • Adaptive Learning

Here AI and machine learning models analyze the unique needs of segmented learners to assign specific tutors or digital resources.

  • Individualized learning

AI can automatically adjust the pace of learning from comprehensive to quick brush-up as per the needs of individual students

  • Differentiated learning

Under differentiated learning, AI can adjust the approach to studying, i.e. suggesting laptops, tabs, videos, and exercise options to match student environments.

  • Competence-based learning

The last type triggers students to demonstrate their competency by building collaborative learning platforms wherein their classmates can join in to compete on similar tasks and activities.

4. Assisting Students with Virtual Tutors

As the world battles against the COVID-19 outbreak, teachers and students are struggling to adapt to the new normal of virtual classes. While imparting knowledge through robots is still a far-sighted vision, virtual tutorials and guides are emerging as impressive alternates.

With significant developments in 3D visualization and creatives, a new trend of creating 3D avatars of teachers is gaining momentum.

Virtual reality and augmented reality combined with intelligent software are joining forces to create human-like characters of teachers with the abilities to-

  • Act and interact with students like real teachers
  • Respond to students in a natural human-like manner
  • Captivate student attention with critical thinking and high order thinking questions.

These virtual tutors are efficient at providing quick assessment and tutoring feedback in student-friendly languages to improve performance. In addition, virtual tutors can employ conversational-style quizzes to engage students and motivate learning. Personalized hints, tips, study guides along with round-the-clock availability makes virtual tutoring a highly effective AI solution for the education industry.