New Technology Should Help Make Education More Accessible to People with Chronic Illnesses – by Anna Wood | 01 August 2019

August 1, 2019

Anna Wood is a part-time education researcher at the University of Edinburgh. She is interested in how technology can help to include more students with chronic illnesses like M.E. in education and has examined the need for new technology to better assist education provision.

Anna Wood

Her professional interest extends to understanding how students learn science, how technology such as ‘Lecture Capture’ impacts on students and their learning experience, and how dialogue is used in learning.

Anna has recently written a book chapter called ‘Inclusive Education for Students with Chronic Illness—Technological Challenges and Opportunities’ which was recently published by Springer.

“This chapter explores how artificial intelligence technologies can support the educational inclusion of people with chronic illness.”

“Drawing on my own experiences of living and studying with M.E., I will discuss the issues faced by students with chronic illnesses such as energy impairment, fluctuations in symptoms and cognitive difficulties and the educational challenges that these issues cause.”

“I then explore the examples of nascent, emergent and futuristic AI technologies, sourced from both personal experience and community knowledge, that could enable better inclusion of students with chronic illness in education.”

Unfortunately, the chapter is not open-access but you can read the complete abstract.

The chapter that I contributed was part of a book called ‘Artificial Intelligence and Inclusive Education: speculative futures and emerging practices.’

The book aims to bring together two hot topics in education – Artificial Intelligence (AI) and Inclusive Education, and to explore the similarities and differences in these two fields, such as personalisation, learner centredness as well as the ethical and moral dimensions.

The book is directed at professionals working in the field of AI who may normally be focused on the technological possibilities, and who therefore may not have considered the needs of more diverse students and the potential of AI to benefit those students.

My chapter focuses on exploring the problems faced by people with chronic illnesses and suggests AI technologies that could be developed in the future to make education and learning easier for this group of people.

By chronic illnesses I mean conditions which have global or systemic impairment, and which are often of a fluctuating nature, so this definitely includes M.E., but may also include Lupus, chronic pain and Ehlers-Danlos Syndromes etc.

The chapter also makes it clear that many chronic illnesses such as M.E. vary in severity and that there are people who are simply too ill to access education. For these students no amount of technology will be able to help.

However, I also use my own experiences of studying for an MSc (online, and part-time) with Severe M.E. to explain how technology can be helpful for some (see this previous blog for more detail).

The chapter starts by briefly explaining the ‘social model’ of disability, how it is helpful for driving change and why some people with chronic illness do not feel totally comfortable with it, as no matter what material changes there are, they are still limited by their illness.

I also talk about current technologies such as the AV1 robot that can go into schools in place of a child, enabling them to hear and take part in the lesson, and Nisai Virtual Academy which offers lessons online, which many young people with M.E. have benefited from.

I then discuss the key challenges that I feel that people with chronic illness face in relation to education, and how these affect their participation in education. These are:

  • Energy Impairment (i.e. not producing enough energy),
  • Energy Management (the need to pace),
  • Fluctuating Nature (the unpredictable nature of the illness),
  • Cognitive Difficulties (difficulties with concentration and memory), and,
  • Pain.

The rest of the chapter focusses on AI technologies that don’t yet exist but that could help students with chronic illnesses. To develop this section, I got opinions from people with chronic illnesses on Twitter, from friends and from colleagues about the sort of AI technologies that they would find helpful.

These technologies are split into 4 sections:

The first is ‘Understanding language and speech’ and in this category I envisage systems which could understand human language. While digital assistants like Siri and Alexa are getting quite good at this for basic requests, a student might want to make more complicated searches and also find things that aren’t text such as equations and diagrams in their textbooks or lecture notes. Systems also need to improve their ability to understand speech, as people with chronic illnesses may not be able to speak clearly, or may need to whisper, perhaps due to pain or fatigue.

Systems with improved understanding of language could also be used to help students to create other types of output. Lots of people, including those without chronic illness or disability use speech to text software – where you can create documents by speaking rather than typing. In this section I suggest that being able to easily create things like diagrams, graphs and mathematical notations by speaking rather than typing would be really useful for some people with chronic illnesses.

The second section is ‘Producing Speech: Text to speech’. Many people with chronic illnesses already use software that can read text out loud so they don’t have to read from a screen. I think the next generation of such systems would feature improvements to the production of spoken language from text documents to create more natural, human sounding speech, that would make them easier to listen to.

In the third section I discuss intelligent tutor systems. These are systems that are able to produce adaptive, tailored and interactive teaching. They are individualised and so are ideal for someone with a chronic illness who may not be able to study for the same length of time as their peers. I suggest that such systems could use biofeedback to detect when a student is getting tired, perhaps by measuring heartrate, and could then suggest that students take a break, possibly even before they themselves have realised they need to stop.

Finally, the fourth section looks at virtual reality systems. These are headsets that people wear to often play games which give the user a 3-dimensional view – and make them feel that they are really there. The system allows the user to move around and to interact with what they can see. VR could give students experiences that they might be otherwise be too ill to access in person, such as lab sessions in science and field trips to visit historic monuments or architecture.

Although the chapter was not written solely for people with M.E., I hope that some of the ideas it contains will help to make accessing education easier for students both young and old with chronic illnesses.

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