Robot

Access Personalised Learning With Embibe Simple!

Click on Start Trial To Access Learning Outcomes Today

Impacting Learning Outcomes in the Education Sector Through Artificial Intelligence

The world has entered the digital age. Technology today touches every aspect of human life, be it business, communication, travel, health or education. Globally, the education sector is embracing technology wholeheartedly, and the implications of advanced technologies are creating wonders in this field. Chief among these rapidly evolving technologies is Artificial Intelligence in education, and its effects are far-reaching. While much of Artificial Intelligence’s theoretical basis is decades old, the proliferation of commodity computing hardware is making it more accessible and usable than ever before.

School-level education in India has made tremendous strides in recent decades with high-90s Gross Enrolment Ratios and better budgetary funding allocated to it than previously. However, reduced student retention rates and glacial improvements in learning outcomes still pose a challenge to bringing India at par with global standards. Most changes in India’s educational landscape typically start from within the private sector, and newer innovations powered by data-driven approaches are seeping into key schools in India.

Enhanced data mining, content understanding, student profiling and teacher task augmentation are showing promise for learning outcomes improvement and disruption of the traditional education paradigm for all three stakeholders of education, student, teacher and institute. Artificial Intelligence-based technology platforms are impacting the education sector across the country from the ground up by generating personalized curricula and learning recommendations for students. AI in education is helping to identify academic and behavioural gaps in learners and automating time-consuming, repetitive tasks so that teachers can focus on teaching better.

An Artificial Intelligence-powered EdTech platform built on the pillars of a well-designed educational knowledge base, intelligent content automation and curation, an educational data lake to capture granular interaction data of learners and intelligent teaching intervention systems can immensely impact students’ lives.

Download the complete article from here.

References

  1. Corbett, A. T. and Anderson, J. R. (1994), “Knowledge tracing: Modeling the acquisition of procedural knowledge,” User modeling and user-adapted interaction, vol. 4, no. 4, pp. 253–278, 1994
  2. Cukier, Kenneth (2019). “Ready for Robots? How to Think about the Future of AI”. Foreign Affairs. 98 (4): 192, August 2019.
  3. Faldu, K., Avasthi, A. and Thomas, A. (2018) “Adaptive learning machine for score improvement and parts thereof,” US20180090023A1, Mar 29, 2018.
  4. Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015). Learning entity and relation embeddings for knowledge graph completion. In 29th AAAI Conference on Artificial Intelligence, Feb 2015.