{"id":9237,"date":"2022-07-19T09:30:00","date_gmt":"2022-07-19T08:30:00","guid":{"rendered":"https:\/\/blogs.sussex.ac.uk\/tel\/?p=9237"},"modified":"2022-06-17T14:13:13","modified_gmt":"2022-06-17T13:13:13","slug":"ai-in-education-what-can-we-expect-in-the-future","status":"publish","type":"post","link":"https:\/\/blogs.sussex.ac.uk\/tel\/2022\/07\/19\/ai-in-education-what-can-we-expect-in-the-future\/","title":{"rendered":"AI in education \u2013 what can we expect in the future?"},"content":{"rendered":"\n
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The field of artificial intelligence (AI) is progressing rapidly and, as a data-driven technology, AI-powered tools lend themselves to a wide range of applications. In this blog we will look at potential opportunities for AI integration into teaching and learning, current case studies of successful use, and the explicit limitations and vulnerabilities of using this technology-driven approach.<\/p>\n\n\n\n\n\n\n\n

Using artificial intelligence<\/h2>\n\n\n\n

For assessment, AI-assisted marking and feedback software goes beyond what has been achieved through multiple-choice quizzes. In STEM subjects, AI-powered tools can help significantly in the semi-automation of marking. Rather than grading hundreds of calculus submissions, an AI tool is very fit for purpose, using a technology called \u2018replay grading\u2019 ensuring that similar submissions are not marked twice, and grouping similar answers so they can be given a consistent grade at the same time. Examples of software designed for this use are Gradescope<\/a>, Graide<\/a> or the Mobius<\/a> platform, which can automatically generate unique questions and answers (limiting academic misconduct) and these programs can even read handwritten equations using optical character recognition.<\/p>\n\n\n\n

AI is not just restricted to mathematical or engineering subjects but can also be applied to speech and written text. Speech recognition software which is AI-enhanced can be used to improve captioning on videos or live feeds which means we can provide more accessible content. Machine learning software such as Grammarly<\/a> is progressing to provide academic writing support and assisted marking. Tools within machine learning platforms such as TensorFlow<\/a> analyses text for toxic behaviours and therefore could  be applied on discussion forums or the like as a moderation tool.<\/p>\n\n\n\n

For employability, AI could be used in conjunction with virtual reality to improve students\u2019 soft skills and help to embed employability into the curriculum. Bodyswaps<\/a> is a platform that provides immersive simulations such as job interview training, using face emotion and body movement detection to help analyse performance. In one study using VR with AI analytics, 84% of students reported feeling better prepared in advance of their upcoming interviews (Bodyswaps, 2021<\/a>).<\/p>\n\n\n\n

Personally, one of the most exciting ways to use AI and machine learning is through adaptive learning platforms. Virtual Learning Environments produce a huge amount of usage data daily, and the increased use of digital technologies in the last few years has furthered this. But often this data is overlooked and underutilised. There is the potential for AI models to provide valuable insights that can support learners\u2019 development, through a fine-grained analysis, for example, providing autonomous learning recommendations, individualised instruction and personalised learning at scale.<\/p>\n\n\n\n

Case studies <\/h2>\n\n\n\n

There are a growing number of successful use cases for AI integration within the higher education sector and some examples are listed below:<\/p>\n\n\n\n