Mr Jeremie Clos' Outputs (17)

Investigating the Impact of Generative AI on Students and Educators: Evidence and Insights from the Literature (2024)
Presentation / Conference Contribution
Clos, J., & Chen, Y. Y. (2024, September). Investigating the Impact of Generative AI on Students and Educators: Evidence and Insights from the Literature. Presented at Second International Symposium on Trustworthy Autonomous Systems (TAS ’24), Austin, Texas

Generative artificial intelligence (AI) has become one of the main concerns of knowledge workers due to its ability to mimic realistic human reasoning and creativity. However, this integration raises critical concerns about trust and ethics, which ar... Read More about Investigating the Impact of Generative AI on Students and Educators: Evidence and Insights from the Literature.

LOOM: a Privacy-Preserving Linguistic Observatory of Online Misinformation (2024)
Presentation / Conference Contribution
Clos, J., McClaughlin, E., Barnard, P., Tom, T., & Yajaman, S. (2024, September). LOOM: a Privacy-Preserving Linguistic Observatory of Online Misinformation. Presented at Second International Symposium on Trustworthy Autonomous Systems (TAS ’24), Austin, Texas, USA

Online misinformation is an ever-growing challenge that can have a negative impact on individuals, societies, and democracies. We report on LOOM, a project that aims to build and validate a browser-based tool to detect and respond to misinformation i... Read More about LOOM: a Privacy-Preserving Linguistic Observatory of Online Misinformation.

A Multimethod Analysis of US Perspectives towards Trustworthy Autonomous Systems (2024)
Presentation / Conference Contribution
Barnard, P., Boudouraki, A., & Clos, J. (2024, September). A Multimethod Analysis of US Perspectives towards Trustworthy Autonomous Systems. Presented at Second International Symposium on Trustworthy Autonomous Systems (TAS '24), Austin, Texas

The Trustworthy Autonomous Systems (TAS) Hub is a collabora-tive platform that aims to guide the development of autonomous systems that are safe, reliable, and ultimately trusted by society. Its mission is to address the challenges surrounding the gr... Read More about A Multimethod Analysis of US Perspectives towards Trustworthy Autonomous Systems.

Examining the Feasibility of AI-Generated Questions in Educational Settings (2024)
Presentation / Conference Contribution
Zeghouani, O., Ali, Z., Simson Van Dijkhuizen, W., Wei Hong, J., & Clos, J. (2024, September). Examining the Feasibility of AI-Generated Questions in Educational Settings. Presented at Second International Symposium on Trustworthy Autonomous Systems (TAS '24), Austin, Texas, USA

Educators face ever-growing time constraints, leading to poor work-life balance and a negative impact on work quality. Through their language generation capabilities, large language models offer an interesting avenue to ease this academic workload, a... Read More about Examining the Feasibility of AI-Generated Questions in Educational Settings.

“The ChatGPT bot is causing panic now – but it’ll soon be as mundane a tool as Excel”: analysing topics, sentiment and emotions relating to ChatGPT on Twitter (2024)
Journal Article
Heaton, D., Clos, J., Nichele, E., & Fischer, J. E. (2024). “The ChatGPT bot is causing panic now – but it’ll soon be as mundane a tool as Excel”: analysing topics, sentiment and emotions relating to ChatGPT on Twitter. Personal and Ubiquitous Computing, 28, 875–894. https://doi.org/10.1007/s00779-024-01811-x

ChatGPT, a sophisticated chatbot system by OpenAI, gained significant attention and adoption in 2022 and 2023. By generating human-like conversations, it attracted over 100 million monthly users; however, there are concerns about the social impact of... Read More about “The ChatGPT bot is causing panic now – but it’ll soon be as mundane a tool as Excel”: analysing topics, sentiment and emotions relating to ChatGPT on Twitter.

A Privacy-Preserving Observatory of Misinformation using Linguistic Markers - A Work in Progress (2023)
Presentation / Conference Contribution
Clos, J., McClaughlin, E., Barnard, P., Tom, T., & Yajaman, S. (2023, July). A Privacy-Preserving Observatory of Misinformation using Linguistic Markers - A Work in Progress. Presented at TAS '23: First International Symposium on Trustworthy Autonomous Systems, Edinburgh, UK

Online misinformation is a serious problem that can have a negative impact on individuals, societies, and democracies. It can lead to the spread of false information, the erosion of trust in institutions, and the polarisation of political discourse.... Read More about A Privacy-Preserving Observatory of Misinformation using Linguistic Markers - A Work in Progress.

Digital twins for human-assistive robot teams in ambient assisted living (2023)
Presentation / Conference Contribution
Chen, J., Clos, J., Price, D., & Caleb-Solly, P. (2023, July). Digital twins for human-assistive robot teams in ambient assisted living. Presented at TAS '23: First International Symposium on Trustworthy Autonomous Systems, Edinburgh, UK

Digital twins are virtual replicas of physical systems that simulate real-world scenarios to optimize system performance, reduce physical losses, and ensure user safety. Although digital twins have been widely adopted in industrial settings, there is... Read More about Digital twins for human-assistive robot teams in ambient assisted living.

Communicating health threats: Linguistic evidence for effective public health messaging during the Covid-19 pandemic (2023)
Report
Adolphs, S., McAuley, D., Vilar-Lluch, S., Knight, D., McClaughlin, E., Nichele, E., Clos, J., Barnard, P., Parnell, T., & Lang, A. (2023). Communicating health threats: Linguistic evidence for effective public health messaging during the Covid-19 pandemic. Nottingham: University of Nottingham

Benchmarking framework for machine learning classification from fNIRS data (2023)
Journal Article
Benerradi, J., Clos, J., Landowska, A., Valstar, M. F., & Wilson, M. L. (2023). Benchmarking framework for machine learning classification from fNIRS data. Frontiers in Neuroergonomics, 4, Article 994969. https://doi.org/10.3389/fnrgo.2023.994969

Background: While efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of... Read More about Benchmarking framework for machine learning classification from fNIRS data.

The reception of public health messages during the COVID-19 pandemic (2022)
Journal Article
McClaughlin, E., Vilar-Lluch, S., Parnell, T., Knight, D., Nichele, E., Adolphs, S., Clos, J., & Schiazza, G. (2023). The reception of public health messages during the COVID-19 pandemic. Applied Corpus Linguistics, 3(1), Article 100037. https://doi.org/10.1016/j.acorp.2022.100037

Understanding the reception of public health messages in public-facing communications is of key importance to health agencies in managing crises, pandemics, and other health threats. Established public health communications strategies including self-... Read More about The reception of public health messages during the COVID-19 pandemic.

The relationship between trust and attitudes towards the COVID-19 digital contact-tracing app in the UK (2022)
Journal Article
Dowthwaite, L., Wagner, H. G., Babbage, C. M., Fischer, J. E., Barnard, P., Nichele, E., Perez Vallejos, E., Clos, J., Portillo, V., & McAuley, D. (2022). The relationship between trust and attitudes towards the COVID-19 digital contact-tracing app in the UK. PLoS ONE, 17(10), Article e0276661. https://doi.org/10.1371/journal.pone.0276661

During the COVID-19 pandemic, digital contact-tracing has been employed in many countries to monitor and manage the spread of the disease. However, to be effective such a system must be adopted by a substantial proportion of the population; therefore... Read More about The relationship between trust and attitudes towards the COVID-19 digital contact-tracing app in the UK.

Involving psychological therapy stakeholders in responsible research to develop an automated feedback tool: Learnings from the ExTRAPPOLATE project (2022)
Journal Article
Andrews, J. A., Rawsthorne, M., Manolescu, C., Burton McFaul, M., French, B., Rye, E., McNaughton, R., Baliousis, M., Smith, S., Biswas, S., Baker, E., Repper, D., Long, Y., Jilani, T., Clos, J., Higton, F., Moghaddam, N., & Malins, S. (2022). Involving psychological therapy stakeholders in responsible research to develop an automated feedback tool: Learnings from the ExTRAPPOLATE project. Journal of Responsible Technology, 11, Article 100044. https://doi.org/10.1016/j.jrt.2022.100044

Understanding stakeholders’ views on novel autonomous systems in healthcare is essential to ensure these are not abandoned after substantial investment has been made. The ExTRAPPOLATE project applied the principles of Responsible Research and Innovat... Read More about Involving psychological therapy stakeholders in responsible research to develop an automated feedback tool: Learnings from the ExTRAPPOLATE project.

Public health messaging by political leaders: a corpus linguistic analysis of COVID-19 speeches delivered by Boris Johnson (2021)
Report
McClaughlin, E., Nichele, E., Adolphs, S., Barnard, P., Clos, J., Knight, D., McAuley, D., & Lang, A. (2021). Public health messaging by political leaders: a corpus linguistic analysis of COVID-19 speeches delivered by Boris Johnson. Nottingham: UKRI/AHRC

This study analyses the language of speeches about COVID-19 delivered by Boris Johnson, the Prime Minister of the United Kingdom between 3rd March 2020 and 5th April 2021.

We use transcribed speeches to construct a digitised body of texts called a... Read More about Public health messaging by political leaders: a corpus linguistic analysis of COVID-19 speeches delivered by Boris Johnson.

Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks (2019)
Presentation / Conference Contribution
Benerradi, J., Maior, H. A., Marinescu, A., Clos, J., & Wilson, M. L. (2019, November). Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks. Presented at Halfway to the Future, Nottingham, UK

Functional Near-Infrared Spectroscopy (fNIRS) has shown promise for being potentially more suitable (than e.g. EEG) for brain-based Human Computer Interaction (HCI). While some machine learning approaches have been used in prior HCI work, this paper... Read More about Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks.

Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning (2019)
Presentation / Conference Contribution
Clos, J., Qu, R., & Atkin, J. (2019, December). Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning. Presented at International Conference on Innovative Techniques and Applications of Artificial Intelligence (SGAI 2019: Artificial Intelligence XXXVI), Cambridge, UK

© 2019, Springer Nature Switzerland AG. Policy making involves an extensive research phase during which existing policies which are similar to the one under development need to be retrieved and analysed. This phase is time-consuming for the following... Read More about Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning.

Using online news comments to gather fast feedback on issues with public health messaging: The Guardian as a case study
Report
McClaughlin, E., Nichele, E., Adolphs, S., Barnard, P., Clos, J., Knight, D., McAuley, D., & Lang, A. (2021). Using online news comments to gather fast feedback on issues with public health messaging: The Guardian as a case study. Arts and Humanities Research Council (AHRC), grant reference AH/V015125/1

This study uses corpus linguistics to analyse opinions on messaging and public health measures from one resource—comments posted in response to articles containing references to borders from The Guardian online. Overall, commenters made international... Read More about Using online news comments to gather fast feedback on issues with public health messaging: The Guardian as a case study.

Privacy Preserving Corpus Linguistics: Investigating the Trajectories of Public Health Messaging Online
Report
McClaughlin, E., Nichele, E., Adolphs, S., Barnard, P., Clos, J., Knight, D., McAuley, D., Aydt, M., Tom, T., & Lang, A. (2022). Privacy Preserving Corpus Linguistics: Investigating the Trajectories of Public Health Messaging Online. University of Nottingham: AHRC/UKRI

The Coronavirus Discourses project supports public health partners Public Health Wales, Public Health England, and NHS Education for Scotland in addressing key challenges that the coronavirus pandemic presents in terms of understanding the flow and i... Read More about Privacy Preserving Corpus Linguistics: Investigating the Trajectories of Public Health Messaging Online.