Blog: AI: Changing the Education Landscape
With artificial intelligence taking the spotlight as an emerging technology to watch, major industries clamour for their piece of the pie.
In education, AI is yet to become standard issue, but its potential to revolutionise learning can’t be overstated. Opportunity exists to introduce a more personalised, more effective way of learning for students, one that can’t be found in a textbook.
This article explores how AI is being applied in an educational context today, and look to what the future might hold.
Tomorrow’s teaching assistant
Much of the discussion around AI centres around job losses brought about by automation. In the classroom, however, there are fewer such worries. Experts in education and tech agree that the human teacher is neither desirable nor possible to replace.
Instead, AI is finding a spot easing the administrative burden. According to the BBC, 40% of teachers in the UK plan to leave the profession, with most citing huge loads of admin as a major factor. Thankfully, this is due to change.
AI software providers are focusing on ways to digitally grade papers, letting teachers spend time adding value to students. Even simple but time-consuming data processing tasks can be delegated to digital, letting educators focus on what matters most.
Making content smarter
Interactive content, or content repurposed towards a specific goal, can have a huge positive impact on students’ experience. Particularly in computer science education, this smart content can help with topics like visualisation and simulation.
Despite a slower uptake in other disciplines, research to improve the dissemination of smart content is taking great leaps forward.
In the meantime, technologies exist to digitise physical textbooks and automatically index them by chapter for ease of navigation. Likewise, products are already on the market allowing teachers to create a syllabus which adapts to different mediums and devices.
Matching learners to learning
In the same way as media streaming services adapt to user behaviour and recommend items the viewer might like, recommender systems can be used to recommend teaching material to users, in-line with their learning patterns and how they’re generally performing.
Personalised learning helps each student move at their own pace and focus on areas within a subject which would benefit them most. Products like Brightspace record and process student data from a range of systems, building a behaviour model which the teacher can then use to assist each individual learner.
This isn’t restricted to students either. The next step for personalised learning will allow teachers to aggregate data, building an approach that works for each class rather than recycling the same curriculum each year.
Teachers without borders
The rapid rise of eLearning, in both industry and academic contexts, is also benefitting from an AI boost. Tools which translate lectures and presentations in real time can help teachers reach across language and geographical boundaries.
Plugins like Presentation Translator provide live lecture transcripts and, of course, translations. Remote learners can interact digitally in their native languages to ensure nobody gets left behind.
This technology also has the potential to revolutionise learning for the deaf and visually impaired, integrating with screen readers to provide an end-to-end experience.
The classroom of tomorrow
Having explored just a few ways in which AI is entering the classroom right now, what does the future hold?
Innovation foundation, Nesta, conducted a two-month study of existing AI literature, research and tools already being used in schools. The result was a snapshot of nine education challenges which AI could address:
- The admin burden facing teachers
- Rigid, pre-ordained paths through the system
- Slow improvements to best practice
- Entrenched social immobility
- High staff turnover and low recruitment rates
- Not preparing students for today’s society
- Failure to cater for special educational needs
- Excessive focus on assessment rather than teaching
- One homogenous curriculum without room for personalisation
The solutions to these challenges Nesta saw already in development go far beyond the applications already in use, those we’ve explored here today. In fact, they saw three broad categories of education AI emerging.
Learner-facing applications have the potential to:
- Curate content for students
- Give automated feedback
- Assess as the student learns
- Bring students together to collaborate
Educator-facing applications are focusing on:
- Improving automated assessment and feedback
- Providing for special educational needs
- Spotting plagiarists
- Providing general assistance to teachers
Finally, we have system-facing applications, with the power to:
- Establish and advance best practice
- Bring education into the Fourth Industrial Revolution
- Streamline and handle admissions processes
- Conduct school inspections
All of this shows enormous promise, but it has to start somewhere. Fittingly, AI for education begins with education in AI. Early 2019 saw a summit at MIT where panellists called for greater investment in the field.
They estimated around 300,000 AI professionals worldwide, compared to millions of available roles. With so much untapped potential, the time to think about a career in data science for the education sector is now.
Ready to take the next step? Contact Cambridge Spark and let’s get started.
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