How AI will help advance testing education

It is 10 am on a Monday morning and the results are about to be announced for the tests in India which we conducted on Sunday. Satish, director of studies, is busy with his fleet of professors preparing the results to be announced.

Not only must the results be announced, but the differentiator (compared to other academies) is that the faculty must also define the action plan for improvement based on the chapter grades that the student obtains.

Take the case of Rahul, a consistent high percentile student. His subject teachers review his mistakes in the test, analyzing the time spent on various questions, the type of questions he failed at, a trend of his previous assessments made, and then suggest a study plan of concept videos and lessons. reading material, with an aim to score even better.

They should also prepare for the follow-up assessments for the coming weeks on the topics where Rahul had made mistakes with new similar questions so that he can master those topics.

It would have been fantastic; but doing it for all students is practically impossible to do given the time, effort and also the skill level of the faculty. This is where data analysis using artificial intelligence (AI) tools can be leveraged continuously, to personalize each student’s study plan.

Personalized AI-powered learning

The goal of personalized AI-based learning is to analyze information about a student’s style, attributes, strengths and weaknesses, and to create a learning path that would help a student master the subject.

AI models are based on a few major pillars.

  • The first pillar is the student, who is profiled from different angles – demographic, academic, emotional, motivation, metacognition, self-regulation, etc.
  • The second pillar is content – the format, duration, type of teaching, level of difficulty, interrelationships with other content elements and other parameters.
  • The third pillar is about the curriculum – basically how students are expected to navigate knowledge and experience as part of learning.
  • The fourth pillar is about assessments – data that informs the model about specific student performance as they progress through the course.
  • The fifth pillar concerns the teacher: progression in the content, planned corrections, dissemination of the content, engagement, etc.

One or more of these pillars are used to rank students, recommend learning paths, and provide feedback. Constructed learning paths are constantly evolving based on various pillars to improve learning outcomes.

The two-sigma problem, in which it is observed that students who receive such individual attention are likely to have two standard deviations better than students learning in a classroom. This opportunity for improvement has been the driving force behind research into personalized adaptive learning models using AI.

How does AI help advance education and improve learning outcomes?

The use of these technologies has large-scale benefits that help create better learning environments, tools and digital content.

Below are some of the areas where AI could be harnessed to become the differentiators.

Learning gap analysis

A system-driven automated analysis can find specific areas that need improvement.

Virtual tutor

An AI-based virtual tutor creating personalized learning paths for each learner, previously limited to a few toppers, would benefit all learners to improve their fluency, and therefore improve learning outcomes at scale and democratize learning. education.

Business scalability

As the assessment is automated, the lack of good teachers will no longer be a constraint. Thus, businesses can expand beyond cities at all levels, while continuing to provide personal attention and improvement plans, in a cost-effective manner.

Faculty productivity

The system can automate most of the grading of student work, repetitive tasks and analysis of student performance, etc.


In summary, AI promises to be an integral part of the teaching and learning process. It is certain that the way forward will be difficult, not only for technologists making technical breakthroughs, but also for educators, who will have to imagine new digital pedagogies from which innovation in AI will be extended to the field of education. .

Partnerships between research, academics, government and industry will result in the next paradigm shifts in education, driving scale and making education personalized and improving learning outcomes for all.

Edited by Saheli Sen Gupta

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)

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