Public deliberation, as in open discussion of issues of public concern, often suffers from scattered and shallow discourse, poor sensemaking, and a disconnect from actionable policy outcomes. This paper introduces BCause, a discussion system leveraging generative AI and human-machine collaboration to transform unstructured dialogue around public issues (such as urban living, policy changes, and current socio-economic transformations) into structured, actionable democratic processes. We present three innovations: (i) importing and transforming unstructured transcripts into argumentative discussions, (ii) geo-deliberated problem-sensing via a Telegram bot for local issue reporting, and (iii) smart reporting with customizable widgets (e.g., summaries, topic modelling, policy recommendations, clustered arguments). The system's human-AI partnership preserves critical human participation to ensure ethical oversight, contextual relevance, and creative synthesis.

Thank you for joining us on "The cloud hub: From cloud chaos to clarity." John Domingue, computer science professor with the Knowledge Media Institute at Open University, shares how evolving AI technology is democratizing education by allowing the customization of more than 300 courses at the largest university in the UK.

Analyzing data is now essential to success in education, employment, and other areas of activity in the knowledge society. Even though several frameworks describe the competencies and skills needed to meet current and future challenges, no data analytics competency framework exists to describe the importance of specific skills to succeed in data analytics assignments.

At FutureLearn we work in short sprints & regularly share, reflect on and iterate on our work. This helps us focus on shipping small, iterative changes and responding quickly to changing business or user needs. We care about work/life balance and supporting learning at work. The Data Platform Team builds and maintains tooling and infrastructure that supports decision making processes across the business and enables product improvements by providing a complete and consistent view of our business data. Our tech stack consists of an ETL process written in Ruby and managed by Airflow which sources data from our production database (MySQL), our email provider (Sendgrid), application logs, and other operational data sources.

Russian researchers from HSE University and Open University for the Humanities and Economics have demonstrated that artificial intelligence is able to infer people's personality from'selfie' photographs better than human raters do. Conscientiousness emerged to be more easily recognizable than the other four traits. Personality predictions based on female faces appeared to be more reliable than those for male faces. The technology can be used to find the'best matches' in customer service, dating or online tutoring. The article, "Assessing the Big Five personality traits using real-life static facial images," will be published on May 22 in Scientific Reports.

Researchers based at the STFC Hartree Centre worked with the Scotch Whisky Research Institute (SWRI) using data analytics to tackle counterfeiting across the sector. Without efficient data processing techniques, the time cost prevents such techniques ever becoming part of a routine authentication provision within the Scotch Whisky sector. The SWRI carries out pre-competitive fundamental research on behalf of its members, representing approximately 90% of the production capacity of the sector. SWRI offers analytical services, using traditional techniques such as GC-FID (Gas Chromatography – Flame Ionisation Detector) or GC-MS (Gas chromatography–Mass Spectrometry) to detect counterfeits. Using these methods to distinguish between different samples can be challenging given the complexity resulting from the number of different compounds present in the vapour above a whisky sample.

The Open University is the UK's largest university, a world leader in flexible part-time education combining a mission to widen access to higher education with research excellence, transforming lives through education. We are seeking to appoint a Lecturer or Senior Lecturer in Artificial Intelligence to join the School of Languages and Applied Linguistics. The candidate will work in one or more of the topics of central importance to the School of Languages and Applied Linguistics, with special attention to workplace communication and/or the interface between social media and AI. The successful candidate will have a master's degree or equivalent in linguistics or a related field, at Senior Lecturer level you will also have a PHD or equivalent in interactional linguistics, preferably ethnomethodological conversation analysis and/or interactional linguistics. Evidence of an emerging research profile in AI and human communication is also essential for this post.

In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual learning environment (VLE) -- General Unary Hypotheses Automaton (GUHA) and Markov chain-based analysis -- and we explain how this analysis can be relevant for module tutors and other student support staff. We show that both methods are a valid approach to modelling student activities. An advantage of the Markov chain-based approach is in its graphical output and in the possibility to model time dependencies of the student activities.

A funny thing happened to me at IJCAI-81. I went to a panel on "Education in AI" and stepped back into an argument that I had thought settled several years ago. The debate was between the "scruffies," led by Roger Schank and Ed Feignbaum, and the "neats," led by Nils Nilsson. The neats argued that no education in AI was complete without a strong theoretical component, containing, for instance, courses on predicate logic and automata theory. The scruffies maintained that such a theoretical component was not only unnecessary, but harmful.

Between 1946 and 1956, a number of BBC radio broadcasts were made by pioneers in the fields of computing, artificial intelligence and cybernetics. Although no sound recordings of the broadcasts survive, transcripts are held at the BBC's Written Archives Centre at Caversham in the UK. This paper is based on a study of these transcripts, which have received little attention from historians. The paper surveys the range of computer-related broadcasts during 1946-1956 and discusses some recurring themes from the broadcasts, especially the relationship of'artificial intelligence' to human intelligence.