The application of Artificial intelligence (AI) is growing across industries. Healthcare, e-commerce, and retail operations are all major areas AI is being applied in. AI in education also offers immense potential.
The introduction of artificial intelligence in education is already impacting learning, teaching and education management. Yet, there are also many challenges.
AI has also helped advance personalisation in education. As an important, fast-emerging element in education today, personalisation can be divided into four general areas: personalisation of content, the pace of learning, learning progress and creating a unique process for every learner. Educational institutions are exploring personalised solutions at multiple levels. This exploration attempts to find the right balance with the conventional "one size fits all" approach.
For instance, a student excelling in language may require extra effort in math. Another student might be in the opposite situation. Both students have different learning needs that require using other learning/teaching approaches to ensure that the education has an impact. In such cases, AI can help ensure that students with varied needs are addressed more appropriately. AI can achieve this through differentiated learning pathways and instructions.
Why AI and Personalisation are Important:
With innovations like big data, machine learning, and artificial intelligence, many educational institutions are leaning towards personalised learning. Besides making personalised systems "student-centred", AI can achieve much else in the education sector; for example:
- AI offers predictive and diagnostic solutions easing the learning journey for students facing difficulties. It can be extremely overwhelming for a teacher to map how to meet the needs of every student in their classroom; this is where AI solutions can help educators deliver personalised instruction. For instance, adaptive learning can be applied to education. AI solutions can adapt to students' individual learning needs and generate a tailored, instruction-based learning pathway by assessing their present skill level based on their strengths and weaknesses.
- AI in assistive technology can also help learners with varied abilities and special needs by providing a more equitable and accessible learning experience. For instance, content providers can ensure that their content is accessible to people who have special needs or are differently abled. This can include accessibility features like Keyboard Navigation (compatible with assistive technologies that ensure that all keyboard users can interact with touch users, for instance), Transcripts and Closed Captions (which allow you to present content in multiple forms to support varied learning ability needs such as large fonts, speech or symbols), Accessible Color Palette User Interfaces (to support contrast requirements of the WCAG 2.1 guidelines), and Screen Reader Support (to ensure accessible content creation even via 3rd party tools).
- AI in education allows instructors to be facilitators and motivators, not just information providers. Technology can ensure that students can access the content they need. At the same time, educators can focus on more critical tasks, like working with students on problem-solving, to foster critical thinking and analysis skills. This allows teachers to step into the role of a facilitator and help students with personalised guidance, understand their challenges and work on more crucial areas.
- AI solutions can help provide insights into students' learning styles and comprehensively analyse their performance, learning patterns, and weak areas to ensure that students get better-directed goals to improve. AI can provide granular and detailed information. This can be useful for analysing specific strengths and weaknesses, thus providing more clarity in assessment and feedback.
The Importance of Data for AI and Personalisation:
Artificial intelligence is being incorporated into education and applied in various areas. It enhances learning, assists teachers, and drives more effective individualised learning.
While these benefits seem exciting, achieving them is predicated on several important elements. One of the key elements is data.
Data helps in providing tailored solutions to students and learners. It is critical to capture detailed user information - from learner profiles to their granular interactions in a course, from assessment results to what the learner is spending time on. This type of fine-grained data allows the AI framework to build customised learning paths that cater to the specific needs of individuals. In education, having an individual's digital footprint can help develop appropriate learning pathways, practices and learning material. For instance, a student who has already acquired first-level fluency in a language course could be introduced to more complex language rules, concepts & structures at a pace designed for that student.
A true, personalised learning environment can be achieved by continuously feeding the AI engines with large amounts of student and learning data. This allows the AI engine to learn, evolve, and improve with time.
With big-data learning analytics, modern learning stacks provide the necessary foundation for AI systems - capturing, managing, streaming & enriching data. An example of this is the comproDLS Learning Stack
Therefore, data is an essential element of the personalisation program.
Challenges for AI & Personalisation in Education:
Though there are many benefits that AI can deliver, it is important to address the challenges to the adoption and successful implementation of AI. Here are some to provide an idea of the issues being examined as AI heads towards a larger role in education.
Perhaps, one of the most critical challenges is ensuring people are trained and equipped with the proper skill-set and expertise for artificial intelligence deployment. There is currently a lack of knowledge in integrating AI technology in the teaching framework - and much still has to be done to ensure that practising educators are well versed with the technology they are using. This lack of knowledge is one common reason for institutions not adopting AI-based solutions that have the potential to provide better educational outcomes.
Another key point is the training of teachers to ensure they meet the quality benchmark required to teach students with these advanced technologies. Students rely on teachers to overcome issues they face in the classroom. When teachers are well aware of the technology, they can ensure that students are prepared for the new technology.
Data privacy is also an area of concern. As we have seen earlier, personalised learning requires a large amount of student data, and students globally are concerned about their data privacy. Therefore, ensuring that student data is collected and processed ethically, securely and transparently is critical. This process must be an integral part of a personalised learning strategy.
Issues like these are critical to addressing as personalised learning gains traction and makes its way into the mainstream of education.
Compro Technologies sponsored this post.
Engage, from Compro, is a next-generation ready-to-deploy digital publishing suite that leverages the power of personalisation for publishers and content creators. It includes capabilities like analytics, accessibility, interoperability and more. Check out the website or request a call.