An article you might like: The assistant all educators must have now


Sooi Yung


Working in the education industry can be meaningful but educators often find themselves tasked with many responsibilities they did not sign up for which hinders their capacity to make an impact in the lives of their students. 64% of teachers in the United Kingdom reported feeling stressed and what is more concerning is that.

81% of teachers have thought of leaving the profession due to the high workload.” – National Education Union, UK

The pressure on educators by institutions and parents to teach better is rising yet it seems globally, the resources allocated to advance the industry isn’t matching up. As Barbara Kurshan highlighted in her article on Forbes, efforts to augment AI in the education industry “pale in comparison to advancements in the non-education space”. How can technology and AI fill the gap?

In Singapore, the Ministry of Education and Infocomm Development Authority (now IMDA) both published reports that acknowledge the importance of personalised learning in the education industry. For education to be effective, it no longer suffices to teach in a class of 40 and expect equal retention because every student learns differently. Educators thus need to spend time and energy making education personal.

Learning from other industries

Observing how other industries provide personalised services, you will notice that the retail and health & wellness industries are at the forefront of using AI to personalise their services with the help of recommendation engines. Health and fitness startup, CureFit, uses AI to analyse users’ interests and goals so as to recommend personalised meal and workout regimes. In the retail scene, retail and tech giant Amazon, whose aim is to be Earth’s most customer centric company, is leveraging recommendation engines to drive ROI. It is estimated that recommendation engines drove 35% of all sales.

3 ways recommendation engines can assist educators to improve learning:

1. Recommendation analytics can help drive a learning-centred pedagogy

An educator’s first worry is often the welfare and education of their students. However, given that every student is unique and comes with a different set of challenges, educators often find it difficult to assess the effectiveness of their teaching for every individual. Feedback from students is thus necessary but often comes only at the end of the term which would be too late and students might not be genuine in their feedback. How then can educators best solicit feedback on what style of teaching their students would best benefit from?

Recommendation engines are capable of analysing students’ behaviour and thus infer their strengths, weaknesses and learning patterns. These real-time data can be easily viewed by educators on a dashboard for them to gain insights to their students’ learning behaviour. With such data, educators adopting a teacher-centred pedagogy can adapt to changing learning environments, and adopt a more learning-centred pedagogy. A robust recommendation engine can provide educators with prescriptive recommendations, helping them adapt their pedagogy to be more effective and appropriate for their students in different contexts.

A research done by the University Research Centre in the Open University of Hong Kong, revealed that the use of learning analytics increased student-instructor interaction and students showed better academic performance, retention and graduation rates in Northern Arizona University. Student attrition rates also dropped from 18 to 12% in the University of New England.

Office workers analyzing and researching business data vector illustration. Marketing analysts developing strategy. Business people studying infographics and diagrams on dashboard

2. Tailoring a unique schedule to foster inquiry-based learning

Every student is different. Their paces of learning, passions for different subjects, learning styles, and even parental influences all vary widely. Yet, much of modern education still fails to fully appreciate their distinctions. Despite efforts in decreasing class sizes, encouraging collaborative learning and more, academia is largely still deductively prescribed. Acknowledging that the limitations in the structure of teaching and learning could reside in the very nature of human teaching, recommendation engines have begun to be introduced to pedagogy, to pursue learning that is more tailored and optimized to keep students truly engaged.

A key case study that experimented with this emergent technology is a math program called School of One, which utilizes a learning algorithm via an online portal. Dubbed as the ‘School of Tomorrow’, the algorithm references each student’s academic profile and demands to tailor a unique ‘playlist’ in their learning plan. These playlists toy around with varying combinations of instruction modalities, including variables like group collaboration sizes, asynchronous learning and precisely timed assessments. The efficiencies of digital technology are what affords the programme to nimbly adjust its recommendations and on a daily basis—pushing the boundaries of personalisation in pedagogy. Results were shocking as students in the program learned 60% more than their peers who were traditionally taught—which effectively amounted to one and a half year’s worth of teaching.

Digitalization through AI could provide a simple answer to the ongoing revamps in traditional pedagogy. The scope and intelligence of such technology lower the barrier to embedding greater constructivist influences, to advance towards more inquiry-based learning that places authentic and active learning at the forefront. As explained by Arinya Talerngsri, Managing Director at Southeast Asia’s leading Executive, Leadership and Innovation Capability Development Center, there is a fundamental difference between studying and learning, and their polarity is akin to the pushing and pulling of content. Studying denotes a push, which is valuable when students are not aware to decide on topics that they would benefit from learning. Yet, there could have negative implications when students feel obligated to adhere to preset curricula. On the other hand, true learning occurs when students themselves pull in the topics that catch their interests. Where the infusion of genuine inquiry to offer truly personalised learning is logically less practical in traditional pedagogy, recommendation engines make it possible to bypass the former’s hurdles, bringing the two polar ends of a push and a pull slightly closer for a more balanced approach to teaching.

Group of students watching online webinar. Young men and women sitting on stacks of books, using laptop computers. Vector illustration for internet course, video lecture, education concept

3. Personalised AI chatbots enhances education as a social leveller

A HSBC survey ranked Singapore parents as the third highest spender on education behind Hong Kong and the United Arab Emirates. Diving deeper, the Household Expenditure Survey in Singapore indicated that the top 20% of households by income spend as much as four times as much on tuition as those in the bottom 20%. The difference in price is relative to the exposure and quality of tutors that students have access to, if any, for that matter. Research shows that individually tutored students perform better than 98% of their traditionally taught peers and it is no fault of parents who desire to do more for their children.

How then can education be a social leveller when students have varying exposures to education? How can educational institutions help students who are struggling to keep up with their peers despite limited teaching resources? Personalised AI chatbots can help, giving 24/7 access to quality assistance catered to each individual’s learning needs, hence levelling the playing field.

Concept Chatbot and future marketing concept, support for web page, social media. Vector illustration chatting with chat bot on laptop, People ask questions for the chatbot, User ask robot

Recommending the way the world learns

Recommendations are now the way the world discovers information and will soon be the way the way the world learns. There is therefore tremendous potential in the education industry to make learning more personal and help our educators teach more effectively as highlighted by many speakers during the International Conference on Artificial Intelligence and Education organised by UNESCO.

Recommendation engines will help realize the vision of education being a social leveller by making learning more effective and attainable regardless of socioeconomic status or learning abilities. It is thus, a strategic tool that helps both students and educators learn. It’s time to empower all students and educators with a personal assistant.

Speak with us to explore how we can help your organisation and your students revolutionise learning.


Your browser is unable to display our site correctly.
It is best viewed on a modern browser, such as Chrome, Safari or Firefox.