In our 50th research seminar, Nicholas Gardella, a PhD candidate at the University of Virginia, shared insights from his research on the effects of AIDEs on beginner programmers’ skills.

Measuring AI’s impact on students

AI tools are becoming a big part of software development, but what does that mean for students learning to code? As tools like GitHub Copilot become more common, it’s crucial to ask: Do these tools help students to learn better and work more effectively, especially when time is tight?

This is precisely what Nicholas’s research aims to identify by examining the impact of AIDEs on four key areas:

• Performance (how well students completed the tasks)
• Workload (the effort required)
• Emotion (their emotional state during the task)
• Self-efficacy (their belief in their own abilities to succeed)

Nicholas conducted his study with 17 undergraduate students from an introductory computer science course, who were mostly first-time programmers, with different genders and backgrounds.

The students completed programming tasks both with and without the assistance of GitHub Copilot. Nicholas selected the tasks from OpenAI’s human evaluation data set, ensuring they represented a range of difficulty levels. He also used a repeated measures design for the study, meaning that each student had the opportunity to program both independently and with AI assistance multiple times. This design helped him to compare individual progress and attitudes towards using AI in programming.

Less workload, more performance and self-efficacy in learning

The results were promising for those advocating AI’s role in education. Nicholas’s research found that participants who used GitHub Copilot performed better overall, completing tasks with less mental workload and effort compared to solo programming.

However, the immediate impact on students’ emotional state and self-confidence was less pronounced. Initially, participants did not report feeling more confident while coding with AI. Over time, though, as they became more familiar with the tool, their confidence in their abilities improved slightly. This indicates that students need time and practice to fully integrate AI into their learning process. Students increasingly attributed their progress not to the AI doing the work for them, but to their own growing proficiency in using the tool effectively. This suggests that with sustained practice, students can gain confidence in their abilities to work with AI, rather than becoming overly reliant on it.

A particularly important takeaway from the talk was the reduction in workload when using AI tools. Novice programmers, who often find programming challenging, reported that AI assistance lightened the workload. This reduced effort could create a more relaxed learning environment, where students feel less overwhelmed and more capable of tackling challenging tasks.

However, while workload decreased, use of the AI tool did not significantly boost emotional satisfaction or happiness during the coding process. Nicholas explained that although students worked more efficiently, using the AI tool did not necessarily make coding a more enjoyable experience. This highlights a key challenge for educators: finding ways to make learning both effective and engaging, even when using advanced tools like AI.

AI as a tool for collaboration, not replacement

Nicholas’s findings raise interesting questions about how AI should be introduced in computer science education. While tools like GitHub Copilot can enhance performance, they should not be seen as shortcuts for learning. Students still need guidance in how to use these tools responsibly. Importantly, the study showed that students did not take credit for the AI tool’s work — instead, they felt responsible for their own progress, especially as they improved their interactions with the tool over time.

Students might become better programmers when they learn how to work alongside AI systems, using them to enhance their problem-solving skills rather than relying on them for answers. This suggests that educators should focus on teaching students how to collaborate with AI, rather than fearing that these tools will undermine the learning process.

Bridging research and classroom realities

Moreover, the study touched on an important point about the limits of its findings. Since the experiment was conducted in a controlled environment with only 17 participants, researchers need to conduct further studies to explore how AI tools perform in real-world classroom settings. For example, the role of internet usage plays a fundamental role. It will be relevant to understand how factors such as class size, prior varying experience, and the age of students affect their ability to integrate AI into their learning.

In the follow-up discussion, Nicholas also demonstrated how AI tools are becoming more accessible within browsers and how teachers can integrate AI-driven development environments more easily into their courses. By making AI technology more readily available, these tools are democratising access to advanced programming aids, enabling students to build applications directly in their web browsers with minimal setup.

The path ahead

Nicholas’s talk provided an insightful look into the evolving relationship between AI tools and novice programmers. While AI can improve performance and reduce workload, it is not a magic solution to all the challenges of learning to code.

Based on the discussion after the talk, educators should support students in developing the skills to use these tools effectively, shaping an environment where they can feel confident working with AI systems. The researchers and educators agreed that more research is needed to expand on these findings, particularly in more diverse and larger-scale educational settings. 

As AI continues to shape the future of programming education, the role of educators will remain crucial in guiding students towards responsible and effective use of these technologies, as we are only at the beginning.

Join our next seminar

In our current seminar series, we are exploring how to teach programming with and without AI technology. Join us at our next seminar on Tuesday, 10 December at 17:00–18:30 GMT to hear Leo Porter (UC San Diego) and Daniel Zingaro (University of Toronto) discuss how they are working to create an introductory programming course for majors and non-majors that fully incorporates generative AI into the learning goals of the course. 

To sign up and take part in the seminar, click the button below — we’ll then send you information about joining. We hope to see you there.

The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.

The post Does AI-assisted coding boost novice programmers’ skills or is it just a shortcut? appeared first on Raspberry Pi Foundation.

“In the near future, perhaps sooner than we think, virtually everyone will need a basic understanding of the technologies that underpin machine learning and artificial intelligence.” — from the 2018 Informatics Europe & EUACM report about machine learning

As the quote above highlights, AI and machine learning (ML) are increasingly affecting society and will continue to change the landscape of work and leisure — with a huge impact on young people in the early stages of their education.

But how are we preparing our young people for this future? What skills do they need, and how do we teach them these skills? This was the topic of last week’s online research seminar at the Raspberry Pi Foundation, with our guest speaker Juan David Rodríguez Garcia. Juan’s doctoral studies around AI in school complement his work at the Ministry of Education and Vocational Training in Spain.

Juan’s LearningML tool for young people

Juan started his presentation by sharing numerous current examples of AI and machine learning, which young people can easily relate to and be excited to engage with, and which will bring up ethical questions that we need to be discussing with them.

Of course, it’s not enough for learners to be aware of AI applications. While machine learning is a complex field of study, we need to consider what aspects of it we can make accessible to young people to enable them to learn about the concepts, practices, and skills underlying it. During his talk Juan demonstrated a tool called LearningML, which he has developed as a practical introduction to AI for young people.

Juan demonstrates image recognition with his LearningML tool

LearningML takes inspiration from some of the other in-development tools around machine learning for children, such as Machine Learning for Kids, and it is available in one integrated platform. Juan gave an enticing demo of the tool, showing how to use visual image data (lots of pictures of Juan with hats, glasses on, etc.) to train and test a model. He then demonstrated how to use Scratch programming to also test the model and apply it to new data. The seminar audience was very positive about the LearningML, and of course we’d like it translated into English!

Juan’s talk generated many questions from the audience, from technical questions to the key question of the way we use the tool to introduce children to bias in AI. Seminar participants also highlighted opportunities to bring machine learning to other school subjects such as science.

AI in schools — what and how to teach

Machine learning demonstrates that computers can learn from data. This is just one of the five big ideas in AI that the AI4K12 group has identified for teaching AI in school in order to frame this broad domain:

1. Perception: Computers perceive the world using sensors
2. Representation & reasoning: Agents maintain models/representations of the world and use them for reasoning
3. Learning: Computers can learn from data
4. Natural interaction: Making agents interact comfortably with humans is a substantial challenge for AI developers
5. Societal impact: AI applications can impact society in both positive and negative ways

One general concern I have is that in our teaching of computing in school (if we touch on AI at all), we may only focus on the fifth of the ‘big AI ideas’: the implications of AI for society. Being able to understand the ethical, economic, and societal implications of AI as this technology advances is indeed crucial. However, the principles and skills underpinning AI are also important, and how we introduce these at an age-appropriate level remains a significant question.

There are some great resources for developing a general understanding of AI principles, including unplugged activities from Computer Science For Fun. Yet there’s a large gap between understanding what AI is and has the potential to do, and actually developing the highly mathematical skills to program models. It’s not an easy issue to solve, but Juan’s tool goes a little way towards this.

At the Raspberry Pi Foundation, we’re also developing resources to bridge this educational gap, including new online projects building on our existing machine learning projects, and an online course. Watch this space!

AI in the school curriculum and workforce

All in all, we seem to be a long way off introducing AI into the school curriculum. Looking around the world, in the USA, Hong Kong, and Australia there have been moves to introduce AI into K-12 education through pilot initiatives, and hopefully more will follow. In England, with a computing curriculum that was written in 2013, there is no requirement to teach any AI or machine learning, or even to focus much on data.

Let’s hope England doesn’t get left too far behind, as there is a massive AI skills shortage, with millions of workers needing to be retrained in the next few years. Moreover, a recent House of Lords report outlines that introducing all young people to this area of computing also has the potential to improve diversity in the workforce — something we should all be striving towards.

We look forward to hearing more from Juan and his colleagues as this important work continues.

Next up in our seminar series

If you missed the seminar, you can find Juan’s presentation slides and a recording of his talk on our seminars page.

In our next seminar on Tuesday 2 June at 17:00–18:00 BST / 12:00–13:00 EDT / 9:00–10:00 PDT / 18:00–19:00 CEST, we’ll welcome Dame Celia Hoyles, Professor of Mathematics Education at University College London. Celia will be sharing insights from her research into programming and mathematics. To join the seminar, simply sign up with your name and email address and we’ll email the link and instructions. If you attended Juan’s seminar, the link remains the same.

The post Learning AI at school — a peek into the opaque box appeared first on Raspberry Pi Foundation.

Pleased to confirm the recipients of the #AlphaGo $1m prize! @UNICEF_uk, @CodeClub, and the American, European and Korean Go associations

— Demis Hassabis (@demishassabis) June 6, 2016

We’re absolutely delighted that Google DeepMind has chosen to make this generous donation to Code Club, and we’ll use the prize money to help even more children in the UK and worldwide to get into computing and coding.

Happy tech enthusiasts at a Code Club

Read more on Code Club’s blog, and if you’d like to support our work too, visit our websites to find out how you can volunteer in the UK and around the world.

The post Google’s AlphaGo donation will help Code Club grow appeared first on Raspberry Pi Foundation.