As artificial intelligence continues to shape our world, understanding how to teach about AI has never been more important. Our new research seminar series brings together educators and researchers to explore approaches to AI and data science education. In the first seminar, we welcomed Shuchi Grover, Director of AI and Education Research at Looking Glass Ventures. Shuchi began by exploring the theme of teaching using AI, then moved on to discussing teaching about AI in K–12 (primary and secondary) education. She emphasised that it is crucial to teach about AI before using it in the classroom, and this blog post will focus on her insights in this area.
An AI literacy framework
From her research, Shuchi has developed a framework for teaching about AI that is structured as four interlocking components, each representing a key area of understanding:
- Basic understanding of AI, which refers to foundational knowledge such as what AI is, types of AI systems, and the capabilities of AI technologies
- Ethics and human–AI relationship, which includes the role of humans in regard to AI, ethical considerations, and public perceptions of AI
- Computational thinking/literacy, which relates to how AI works, including building AI applications and training machine learning models
- Data literacy, which addresses the importance of data, including examining data features, data visualisation, and biases
This framework shows the multifaceted nature of AI literacy, which involves an understanding of both technical aspects and ethical and societal considerations.
Shuchi emphasised the importance of learning about AI ethics, highlighting the topic of bias. There are many ways that bias can be embedded in applications of AI and machine learning, including through the data sets that are used and the design of machine learning models. Shuchi discussed supporting learners to engage with the topic through exploring bias in facial recognition software, sharing activities and resources to use in the classroom that can prompt meaningful discussion, such as this talk by Joy Buolamwini. She also highlighted the Kapor Foundation’s Responsible AI and Tech Justice: A Guide for K–12 Education, which contains questions that educators can use with learners to help them to carefully consider the ethical implications of AI for themselves and for society.
Computational thinking and AI
In computer science education, computational thinking is generally associated with traditional rule-based programming — it has often been used to describe the problem-solving approaches and processes associated with writing computer programs following rule-based principles in a structured and logical way. However, with the emergence of machine learning, Shuchi described a need for computational thinking frameworks to be expanded to also encompass data-driven, probabilistic approaches, which are foundational for machine learning. This would support learners’ understanding and ability to work with the models that increasingly influence modern technology.
Example activities from research studies
Shuchi shared that a variety of pedagogies have been used in recent research projects on AI education, ranging from hands-on experiences, such as using APIs for classification, to discussions focusing on ethical aspects. You can find out more about these pedagogies in her award-winning paper Teaching AI to K-12 Learners: Lessons, Issues and Guidance. This plurality of approaches ensures that learners can engage with AI and machine learning in ways that are both accessible and meaningful to them.
Shuchi shared examples of activities from two research projects that she has led:
- CS Frontiers engaged high school students in a number of activities involving using NetsBlox and accessing real-world data sets. For example, in one activity, students participated in data science activities such as creating data visualisations to answer questions about climate change.
- AI & Cybersecurity for Teens explored approaches to teaching AI and machine learning to 13- to 15-year-olds through the use of cybersecurity scenarios. The project aimed to provide learners with insights into how machine learning models are designed, how they work, and how human decisions influence their development. An example activity guided students through building a classification model to analyse social media accounts to determine whether they may be bot accounts or accounts run by a human.
Closing thoughts
At the end of her talk, Shuchi shared some final thoughts addressing teaching about AI to K–12 learners:
- AI learning requires contextualisation: Think about the data sets, ethical issues, and examples of AI tools and systems you use to ensure that they are relatable to learners in your context.
- AI should not be a solution in search of a problem: Both teachers and learners need to be educated about AI before they start to use it in the classroom, so that they are informed consumers.
Join our next seminar
In our current seminar series, we are exploring teaching about AI and data science. Join us at our next seminar on Tuesday 11 March at 17:00–18:30 GMT to hear Lukas Höper and Carsten Schulte from Paderborn University discuss supporting middle school students to develop their data awareness.
To sign up and take part in the seminar, click the button below — we will then send you information about joining. We hope to see you there.
I want to join the next seminarThe schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.
Website: LINK
