The IDEMS Podcast

In this episode, we are pleased to introduce guest speaker Helen Crompton, who shares her research and teaching experiences focused on AI in education. Helen and David suggest productive ways that lecturers and students can engage with AI to support and enhance their skills and teaching effectiveness.

In this episode, Lily and David discuss the current state of education, and the potentials that AI creates for the future. They look at what it means to treat AI as a friend for lecturers, and then what it means to treat it as a foe, using case studies. They then question how lecturers can productively approach teaching given the current AI context. Note that this podcast is also the third episode of our five-part series on Responsible AI for Lecturers (RAILect).

In this episode, Lily and David aim to demystify data science across various disciplines. They use the Post Office to illustrate the power of AI systems, and then look at other case studies to highlight some limitations of AI systems. This episode is part of the series aimed at Responsible AI for Lecturers, but is still relevant for those not following the course.

R-Instat is a free open-source statistics package. In this episode, Lily and David introduce R-Instat and discuss the motivations for creating it, based upon the need for a user-friendly, comprehensive tool for low-resource environments that can handle multilevel data efficiently. They consider its broader implications for data literacy and statistical education, emphasising the ongoing ambition to make complex analyses more accessible and intuitive.

Santiago and David continue their discussion on the intersection of gaming and advertising, exploring innovative business models that prioritize social impact and inclusivity over traditional revenue streams. They consider the concept of providing digital services related to games that are accessible to all, including those who cannot afford to pay, aiming to develop a system where educational and socially beneficial services can sustain themselves financially while remaining open and accessible.

Santiago and David discuss the conventional business models in the gaming industry, which rely on advertising or paywalls. Motivated by the potential educational value of games, they envision an innovative business model that prioritises social impact over revenue.

Does anyone? Directors David Stern and Kate Fleming discuss the inherent uncertainties faced by early-stage companies and the broader world. The conversation explores how social enterprises must balance impact with profitability, the challenges of sustainable tech development, and the need for innovative business models that serve society, emphasising the importance of deep observation and scientific thinking. They highlight their unique approach at IDEMS, stressing the vital role of building sustainability from the start while ambitiously scaling for social impact.

Lily and David discuss the importance of working with multi-level data. The conversation highlights the need for integrating the concept of multi-level data into data literacy education, from basic to advanced levels, to help people better analyze and interpret complex data sets. They also touch on the practical implications of ignoring multi-level data, such as in the 2020 UK examination algorithm controversy, and consider the relevance to AI.

David Stern interviews Polly Noyce about the history and impact of Manor House, a Kenyan institution focused on teaching sustainable agriculture to smallholder farmers. Polly shares her experiences starting the project in the 1980s and the transformations it has undergone over the past 40 years. They discuss the importance of agroecology and how contemporary technologies and approaches have influenced rural farming.

Can AI-generated information be trusted? In this episode, Lily and David dive into the issue of AI-generated “hallucitations”, where generative AI models like ChatGPT provide ostensible citations referring to sources that do not exist. They discuss the implications of such misinformation, including defamation cases. They emphasize the importance of responsible AI systems and the challenges of funding and prioritizing research to ensure accuracy and reliability in AI outputs.