How Machine Learning Will Transform eLearning

How Machine Learning Will Transform eLearning
Summary: Implementation of AI technology into eLearning will help all stakeholders, from teachers to students and from parents to communities.

4 Ways In Which Machine Learning Will Transform eLearning

Ever since the packet switching network known as ARPANET was demilitarized and turned over to academic researchers in the late 1970s, the fields of Information Technology and education have crossed paths and formed partnerships for the benefit and improvement of society. The activities we know as eLearning and online education today will become the standards of academic instruction tomorrow, and the manner courses are delivered will be determined by Artificial Intelligence.

Before discussing how AI technology will shape eLearning in the near future, it helps to review some of the ways IT and education have intersected until now:

  • Experiential learning
    This was one of the first educational enhancements achieved by technology in the classroom. A couple of decades ago, flight simulation software helped students understand avionics and instrumentation; these days, students use Virtual Reality headsets for an even more realistic flight experience. In other words, technology makes it easier to design a curriculum that favors "learning by doing".
  • Access to extensive educational resources
    Online encyclopedias, internet libraries, Massively Online Open Courses (MOOCs), and educational social networks are just some of the many resources students can access today.
  • Online tutoring
    Soon after messaging and video conferencing apps, such as Skype, came on the market, one-on-one tutoring sessions became popular. These days, internet tutoring networks offer everything from foreign language learning to musical instruction, and from math practice to engineering discussion.
  • Academic connectivity
    ARPANET, the interconnected computing platform that preceded the internet, was originally utilized to allow remote collaboration between university research departments. These days, classrooms in Iowa can connect to sister classrooms in Belize for the purpose of exchanging cultural knowledge and promoting a more diverse worldview.

The technological advances listed above have unfolded over four decades; academic researchers believe that the next wave of tech progress in education will involve machine learning and other fields of AI development. The first ripples of this wave are already here, and they involve algorithms and natural language processing. Botsify, for example, is a smart chatbot platform designed specifically for the education sector, but this is only the beginning. Here are some of the ways AI and machine learning are shaping the future of eLearning:

1. Advanced Grading And Measurement

Automated test scoring and grading has been in use for some time in the form of optical recognition. The same can be said about eLearning courses that feature online tests graded upon completion, but the integration of AI technology complements grading with smart assessments that teachers can utilize for two purposes: adjust classroom work for maximum efficiency and recommend individual tutoring. AI linguistic developers are currently working on algorithms and machine learning constructs that could soon review and score written assignments, such as essays.

2. Creating A Comprehensive Curriculum

Deep learning is an advanced form of machine learning that is already being used in the legal field to review documents and court cases; the objective is to make software smarter so that it can recommend lawsuits and other legal strategies. A similar approach is being applied in the education sector for the purpose of developing course material that can be incorporated into a comprehensive learning curriculum. One example would be designing a curriculum for students pursuing technical writing courses; in this case, the works of Joseph Chapline, author of manuals for the UNIVAC computer, would be added to the study materials. The aforementioned advanced grading tools can also serve as a guide in comprehensive curriculum design.

3. Crowdsourced Learning

There is no question that Wikipedia has been one of the most successful developments of the Information Age, and its concept of crowdsourced knowledge sharing and collaboration is being augmented with AI technology. Brainly is an educational question-and-answer platform similar to the Quora social network; however, a machine learning layer within the platform has been developed to act as a moderator, filter spam, assign the quality to the content, and structure answers. In a few years, Brainly will be able to provide automated answers to certain questions; this concept would be similar to the answers being provided today by the Google search engine, but geared towards primary and secondary education.

4. Smart Learning Software

The success of the Jumpstart learning software for children between the ages of three and eight is prompting AI developers to make the applications smarter. The next wave of eLearning software being developed today features machine learning constructs that are monitoring the way students navigate programs, their reactions, and their proficiency; in other words, AI routines are learning at the same time children are learning, and the goal is to develop future apps that will be even more efficient than they are today. Naturally, the eLearning software of the future will not only be developed with AI technology, but also delivered through cloud computing platforms.

In the end, the implementation of AI technology into eLearning will help all stakeholders, from teachers to students and from parents to communities, to benefit from a more effective educational infrastructure, but there are loftier goals being pursued. The reality of the 21st century is that 50% of the global population does not have access to comprehensive education, and this will be the next task for AI developers to focus on.