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Sahan Bulathwela, María Pérez-Ortiz, Catherine Holloway, John Shawe-Taylor
This paper starts by synthesising how AI might change how we learn and teach, focusing specifically on the case of personalised learning companions, and then move to discuss some socio-technical features that will be crucial for avoiding the perils of these AI systems worldwide (and perhaps ensuring their success). This paper also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We also emphasise the need for collectively designing human-centered, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as support new emerging pedagogies.
Workshop on Machine Learning for the Developing World (ML4D) at the Conference on Neural Information Processing Systems 2021; 2021
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Themes
Tabish Ahmed, Sahan Bulathwela
The informational needs of people are highly contextual and can depend on many different factors such as their current knowledge state, interests and goals [1, 2, 3]. However, an effective information retrieval companion should minimise the human effort required in i) expressing a human information need and ii) navigating a lengthy result set. Using topical representations of the user history (e.g. [4]) can immensely help formulating zero shot queries and refining short user queries that enable proactive information retrieval (IR). While the world has digital textual information in abundance, it can often be noisy (e.g. extracted through Automatic Speech Recognition (ASR), PDF text extraction etc.), leading to state-of-the-art neural models being highly sensitive to the noise producing sub-optimal results [5]. This demands denoising steps to refine both query and document representation. In this paper, we argue that Wikipedia, an openly available encyclopedia, can be a humanly intuitive knowledge base [6] that has the potential to provide the world view many noisy information Retrieval systems need.
Published at the First Workshop on Proactive and Agent-Supported Information Retrieval at CIKM 2022; 2022
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Themes
This article was featured in Nature and discusses tools that help visually impaired scientists read data and Journals. Innovation Manager, Daniel Hajas, was interviewed as part of this piece and highlights the need for an ecosystem approach, and access to data / visualisations for blind members of the research and science community.
Nature; 2023
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Themes
Research Group
Troy Bodkin
Doctoral Thesis. This work is one of four multidisciplinary research studies conducted by members of this research cluster, focusing on the area of Computer Aided Design (CAD) for improving the interface with Additive Manufacture (AM) to solve some of the challenges presented with improving prosthetic socket design, with an aim to improve and streamline the process to enable the involvement of clinicians and patients in the design process.
Loughborough University
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Themes
Research Group
Giulia Barbareschi
Doctoral Thesis. This thesis makes two contributions to facilitate wheelchair users’ engagement in the participatory design process for ATs, while being mindful of the burden of participation. The first contribution is a framework that provides a modular structure guiding the participatory design process from initial problem identification and analysis to facilitating collaborations between wheelchair users and designers. The framework identifies four factors determining the need and adoption process for ATs: (i) People focuses on the target population, (ii) Person includes personal characteristics, (iii) Activity refers to the challenges associated with the task, and (iv) Context encompasses the effect of the environment in which the activity takes place. The second contribution constitutes a rich picture of personal and external elements influencing real world wheelchair transfers that emerged from four studies carried out to investigate the effect of the framework factors on the design process for ATs.
UCL (University College London)
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Research Group
Charlotte Pyatt
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy at Loughborough University.
Loughborough University