Outputs (6)

Fusing ECG signals and IRT models for task difficulty prediction in computerised educational systems (2023)
Journal Article
Arevalillo-Herráez, M., Katsigiannis, S., Alqahtani, F., & Arnau-González, P. (2023). Fusing ECG signals and IRT models for task difficulty prediction in computerised educational systems. Knowledge-Based Systems, 280, Article 111052. https://doi.org/10.1016/j.knosys.2023.111052

Accurately assessing task difficulty is a critical aspect to achieve adaptation in computer-based educational systems. In real-world scenarios, task difficulty estimation can be personalised for individuals by leveraging Item Respon... Read More about Fusing ECG signals and IRT models for task difficulty prediction in computerised educational systems.

Reimagining AI Governance: a Response by AGENCY to the UK Government's White Paper AI Regulation (2023)
Report
Owens, R., Copilah-Ali, J., Kolomeets, M., Malviya, S., Markeviciute, K., Olabode, S., …Farrand, B. (2023). Reimagining AI Governance: a Response by AGENCY to the UK Government's White Paper AI Regulation. SSRN: AGENCY project

In March 29, 2023, the UK Government released a white paper outlining its plans to implement a pro-innovation approach to Artificial Intelligence (AI) regulation and strengthen the UK's position as a global leader in AI.

As part of the white paper... Read More about Reimagining AI Governance: a Response by AGENCY to the UK Government's White Paper AI Regulation.

AGENCY—written evidence (LLM0028) for House of Lords Communications and Digital Select Committee inquiry: Large language models (2023)
Report
Malviya, S., Owens, R., Copilah-Ali, J., Elliot, K., Farrand, B., Neesham, C., Shi, L., Vlachokyriakos, V., Katsigiannis, S., & van Moorsel, A. (2023). AGENCY—written evidence (LLM0028) for House of Lords Communications and Digital Select Committee inquiry: Large language models. House of Lords Communications and Digital Select Committee

AGENCY is a multidisciplinary research team of academics with expertise in computer science (natural language processing, cybersecurity, artificial intelligence, human-computer interaction), law, business, economics, social sciences and media studies... Read More about AGENCY—written evidence (LLM0028) for House of Lords Communications and Digital Select Committee inquiry: Large language models.

Deep learning for Crack Detection on Masonry Façades using Limited Data and Transfer Learning (2023)
Journal Article
Katsigiannis, S., Seyedzadeh, S., Agapiou, A., & Ramzan, N. (2023). Deep learning for Crack Detection on Masonry Façades using Limited Data and Transfer Learning. Journal of Building Engineering, 76, Article 107105. https://doi.org/10.1016/j.jobe.2023.107105

Crack detection in masonry façades is a crucial task for ensuring the safety and longevity of buildings. However, traditional methods are often time-consuming, expensive, and labour-intensive. In recent years, deep learning techniques have been appli... Read More about Deep learning for Crack Detection on Masonry Façades using Limited Data and Transfer Learning.

Towards Automatic Tutoring of Custom Student-Stated Math Word Problems (2023)
Presentation / Conference Contribution
Arnau-González, P., Serrano-Mamolar, A., Katsigiannis, S., & Arevalillo-Herráez, M. (2023, July). Towards Automatic Tutoring of Custom Student-Stated Math Word Problems. Presented at International Conference on Artificial Intelligence in Education (AIED), Tokyo, Japan

Math Word Problem (MWP) solving for teaching math with Intelligent Tutoring Systems (ITSs) faces a major limitation: ITSs only supervise pre-registered problems, requiring substantial manual effort to add new ones. ITSs cannot assist with student-gen... Read More about Towards Automatic Tutoring of Custom Student-Stated Math Word Problems.

Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems (2023)
Journal Article
Arnau-González, P., Serrano-Mamolar, A., Katsigiannis, S., Althobaiti, T., & Arevalillo-Herráez, M. (2023). Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems. IEEE Access, 11, 67030-67039. https://doi.org/10.1109/access.2023.3290478

Math Word Problem (MWP) solving, which involves solving math problems in natural language, is a prevalent approach employed by Intelligent Tutoring Systems (ITS) for teaching mathematics. However, one major drawback of ITS is the complexity of encodi... Read More about Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems.