Franca Hoffmann is an Assistant Professor in Computing and Mathematical Sciences at Caltech and International Scientific Advisor at Quantum Leap Africa, at the African Institute for Mathematical Sciences (AIMS). In this episode, David Stern talks to Franca about their recent experiences in Kenya, including a workshop in Kisumu organized by the American Institute of Mathematics and the African STACK Conference in Nairobi. Franca recounts her journey from participating in Maths Camps in Africa to playing a significant role in math education initiatives across the continent.

In August 2024, the American Institute of Mathematics (AIM) organised a workshop in Kenya, bringing together participants from various countries to explore the use of open-source tools like STACK and WebWork in mathematics education. Santiago and David reflect on how the event facilitated meaningful collaborations between African and international educators, offering insights into the unique challenges and opportunities within low-resource environments. The workshop highlighted the importance of layered ownership of educational resources and the potential for cross-contextual learning, with participants from high-resource settings gaining valuable perspectives from their African counterparts.

IDEMS is guided by its principle ‘Open by Default’, a well-developed concept in technology and software fields, but less developed in research, science and data. David and Lucie explore the differences between open science and open data, and question whether or how open science might create inequalities.

In this episode, we explore the future of AI in education through a discussion with Lily and David, focusing on the potential and limitations of AI technologies. They break down the future into four key categories: tasks that AI will never achieve perfectly, tasks that are a matter of "when" not "if", transformative innovations on the horizon, and areas with substantial unknowns. The conversation includes examples like the challenges of AI detecting AI-generated content, the potential of AI-based personal tutors, and the complexities of maintaining historical accuracy while promoting diversity. This is the fifth episode in the Responsible AI for Lecturers (RAILect) series on the IDEMS podcast.

How do you build data systems for contexts where there is low digital literacy? As part of IDEMS’ support for Manor House Agricultural Centre in Kenya, we’re looking to create digital systems which can help them manage the complex farm and training centre. David and Lucie discuss the origins of this idea, the practical implications, and the opportunities.

In this episode, we dive into how AI can revolutionise education by shifting the focus from traditional methods to fostering critical thinking skills. Lily and David discuss the importance of adapting questions in a way that leverages AI capabilities, ensuring students develop deeper analytical skills and greater ownership of their work. They explore various case studies and examples of AI applications in education, highlighting the potential of AI to enhance human intelligence and creativity. This is the fourth episode out of five of the Responsible AI for Lecturers (RAILect) series on 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.