Connexion
Imagine this scenario:
Alex, a teenaged learner, reads a homework assignment, ponders a little, and then opens an AI chatbot. A quick prompt produces a structured, well-written answer within seconds. Alex pastes the text into a document, tweaks a few words, and submits the work.
For many young people, this is becoming the norm.
Recent reports show that, in countries including the US and UK, over half of all teens use AI tools for homework, and 1 in 10 says they do most or all of their homework with chatbots. “I use it every day,” stated one 17-year-old student in a recent study (Pew Research, 2026), and went on to say how she relies on chatbots for everything from homework to life decisions. In another study, a teenager openly admitted to using AI to cheat on assignments, essays, and book reports (Harvard GSE, 2024).
This raises an important question: when AI technology does the work, what happens to the learning?
Researchers are increasingly concerned that such uncritical use of AI tools may lead to cognitive offloading, where learners outsource their thinking, and the weakening of higher-order thinking skills in young people, such as problem-solving, critical thinking and creativity.
To address these concerns, the Raspberry Pi Foundation and Google DeepMind have teamed up to create a new unit of research-informed lessons on large language models (LLMs) as part of Experience AI. Rather than focusing simply on how to use AI tools, the new unit helps learners understand how LLMs work, evaluate their outputs, and use them strategically to support their own learning.
The unit, designed for 13- to 16-year-old learners, supports a learning-focused approach, drawing on metacognition, critical thinking, feedback literacy, and learning-centered prompt engineering. The five lessons in it support young people’s cognitive development, strengthen their metacognitive skills, and bolster their ability to strategically use LLMs for their learning. They draw on extensive research within Google DeepMind’s Learning team on how to scaffold around AI use to preserve ‘productive struggle’ and learner agency.
The new ‘Using LLMs strategically for learning’ unit is grounded in learning science research, including feedback literacy. Feedback literacy refers to the skill of actively interpreting, judging and using information when learning, instead of passively receiving information.
In the unit, learners explore three types of information, also called ‘feedback types’:
Learners discover that deep learning requires a blend of all three feedback types. They also analyse LLM outputs and discover that LLMs default to providing the answer, usually in a ‘tell’ format.
To help learners strategically use LLMs for their learning, they are provided with prompting strategies that elicit LLM outputs in ‘guide’ and ‘challenge’ formats. This helps learners engage more deeply with their own learning and develop higher-order thinking skills.
To help young people strategically use LLMs for their learning, the lessons include learning-centred, platform-agnostic prompting techniques. These include strategies borrowed from research papers on the most effective prompting frameworks. To make sure that the prompting strategies in the lessons remain effective over time and can be translated into different languages when we expand the resources, we did not use acronyms, unlike most popular prompting frameworks.
There are several industry reports highlighting the future-proof skills in the age of AI, such as problem solving, communication, critical thinking, collaboration and creativity skills. In the ‘Large language models’ unit, learners reflect on which skills they want to build and consider how their use of LLMs can support or hinder their development, helping them make responsible and intentional choices about using LLMs in ways that build their long-term capabilities rather than replace them.
There are also activities in the lesson resources that help learners understand the long-term impacts of cognitive offloading and uncritical LLM use on the development of their future skills. The resources encourage learners to stay actively engaged in their learning and be reflective of their LLM use when learning.
Many young people (Pew Research, 2026) use LLMs as search engines, believing that LLMs produce outputs that are always accurate. To address that misconception, the lessons include activities that question where the training data comes from, and how accurate those sources of data are.
For example, when young people discover that a portion of data that LLMs are trained on come from social media platforms like Reddit and Facebook, they are encouraged to question the quality and factuality of these sources through classroom-wide discussions. Through discussions and thinking exercises, learners are also encouraged to question whose voices, languages, cultures and perspectives are (and are not) represented in these AI models.
As AI tools become more integrated in young people’s lives, many learners, like Alex, are tempted to turn to AI tools for quick answers, convenience, and support.
The ‘Using LLMs strategically for learning’ unit equips young people with the skills to use LLMs effectively, critically and strategically, in ways that are most helpful to their learning. Through research-informed lessons, they explore how LLMs are created, why their outputs are not always accurate and how to evaluate AI-generated responses.
Just as importantly, learners are encouraged to reflect on their own use of AI: when using an LLM supports their learning, when it does not, and how they can remain in control of their thinking, learning and skills development.
With these resources, young people like Alex are not discouraged from using LLMs, but encouraged to pause, rewrite their prompts in ways that are more helpful to their learning, evaluate the outputs they receive, and stay actively involved in their own thinking.
As we prepare young people for a future we do not yet know, the most important skill we can teach them is how to keep thinking. Who does the thinking, gets the learning.
The unit is available on the Experience AI website.
We would like to thank all the educators across South Africa, Nigeria, Kenya, the United Kingdom, India and beyond who piloted these resources and shared their thoughtful feedback. Their contributions helped shape and strengthen the unit. We are also grateful to Google DeepMind for their continued support in the development of this unit.
The post Encouraging learners to think first, prompt second: Using large language models to learn appeared first on Raspberry Pi Foundation.
As computing technologies continue to rapidly evolve in today’s digital world, computing education is becoming increasingly essential. Arto Hellas and Juho Leinonen, researchers at Aalto University in Finland, are exploring how innovative teaching methods can equip students with the computing skills they need to stay ahead. In particular, they are looking at how generative AI tools can enhance university-level computing education.
In our monthly seminar in September, Arto and Juho presented their research on using AI tools to provide personalised learning experiences and automated feedback to help requests, as well as their findings on teaching students how to write effective prompts for generative AI systems. While their research focuses primarily on undergraduate students — given that they teach such students — many of their findings have potential relevance for primary and secondary (K-12) computing education.
Generative AI consists of algorithms that can generate new content, such as text, code, and images, based on the input received. Ever since large language models (LLMs) such as ChatGPT and Copilot became widely available, there has been a great deal of attention on how to use this technology in computing education.
Arto and Juho described generative AI as one of the fastest-moving topics they had ever worked on, and explained that they were trying to see past the hype and find meaningful uses of LLMs in their computing courses. They presented three studies in which they used generative AI tools with students in ways that aimed to improve the learning experience.
An important strand of computing education research investigates how to engage students by personalising programming problems based on their interests. The first study in Arto and Juho’s research took place within an online programming course for adult students. It involved developing a tool that used GPT-4 (the latest version of ChatGPT available at that time) to generate exercises with personalised aspects. Students could select a theme (e.g. sports, music, video games), a topic (e.g. a specific word or name), and a difficulty level for each exercise.
Arto, Juho, and their students evaluated the personalised exercises that were generated. Arto and Juho used a rubric to evaluate the quality of the exercises and found that they were clear and had the themes and topics that had been requested. Students’ feedback indicated that they found the personalised exercises engaging and useful, and preferred these over randomly generated exercises.
Arto and Juho also evaluated the personalisation and found that exercises were often only shallowly personalised, however. In shallow personalisations, the personalised content was added in only one sentence, whereas in deep personalisations, the personalised content was present throughout the whole problem statement. It should be noted that in the examples taken from the seminar below, the terms ‘shallow’ and ‘deep’ were not being used to make a judgement on the worthiness of the topic itself, but were rather describing whether the personalisation was somewhat tokenistic or more meaningful within the exercise.
The findings suggest that this personalised approach may be particularly effective on large university courses, where instructors might struggle to give one-on-one attention to every student. The findings further suggest that generative AI tools can be used to personalise educational content and help ensure that students remain engaged.
How might all this translate to K-12 settings? Learners in primary and secondary schools often have a wide range of prior knowledge, lived experiences, and abilities. Personalised programming tasks could help diverse groups of learners engage with computing, and give educators a deeper understanding of the themes and topics that are interesting for learners.
Another key aspect of Alto and Juho’s work is exploring how LLMs can be used to generate responses to students’ requests for help. They conducted a study using an online platform containing programming exercises for students. Every time a student struggled with a particular exercise, they could submit a help request, which went into a queue for a teacher to review, comment on, and return to the student.
The study aimed to investigate whether an LLM could effectively respond to these help requests and reduce the teachers’ workloads. An important principle was that the LLM should guide the student towards the correct answer rather than provide it.
The study used GPT-3.5, which was the newest version at the time. The results found that the LLM was able to analyse and detect logical and syntactical errors in code, but concerningly, the responses from the LLM also addressed some non-existent problems! This is an example of hallucination, where the LLM outputs something false that does not reflect the real data that was inputted into it.
The finding that LLMs often generated both helpful and unhelpful problem-solving strategies suggests that this is not a technology to rely on in the classroom just yet. Arto and Juho intend to track the effectiveness of LLMs as newer versions are released, and explained that GPT-4 seems to detect errors more accurately, but there is no systematic analysis of this yet.
In primary and secondary computing classes, young learners often face similar challenges to those encountered by university students — for example, the struggle to write error-free code and debug programs. LLMs seemingly have a lot of potential to support young learners in overcoming such challenges, while also being valuable educational tools for teachers without strong computing backgrounds. Instant feedback is critical for young learners who are still developing their computational thinking skills — LLMs can provide such feedback, and could be especially useful for teachers who may lack the resources to give individualised attention to every learner. Again though, further research into LLM-based feedback systems is needed before they can be implemented en-masse in classroom settings in the future.
Finally, Arto and Juho presented a study where they introduced the idea of ‘Prompt Problems’: programming exercises where students learn how to write effective prompts for AI code generators using a tool called Promptly. In a Prompt Problem exercise, students are presented with a visual representation of a problem that illustrates how input values will be transformed to an output. Their task is to devise a prompt (input) that will guide an LLM to generate the code (output) required to solve the problem. Prompt-generated code is evaluated automatically by the Promptly tool, helping students to refine the prompt until it produces code that solves the problem.
Feedback from students suggested that using Prompt Problems was a good way for them to gain experience of using new programming concepts and develop their computational thinking skills. However, students were frustrated that bugs in the code had to be fixed by amending the prompt — it was not possible to edit the code directly.
How these findings relate to K-12 computing education is still to be explored, but they indicate that Prompt Problems with text-based programming languages could be valuable exercises for older pupils with a solid grasp of foundational programming concepts.
At the end of the presentation, Arto and Juho summarised their work and hypothesised that as society develops more and more AI tools, computing classrooms may lose some of their community aspects. They posed a very important question for all attendees to consider: “How can we foster an active community of learners in the generative AI era?”
In our breakout groups and the subsequent whole-group discussion, we began to think about the role of community. Some points raised highlighted the importance of working together to accurately identify and define problems, and sharing ideas about which prompts would work best to accurately solve the problems.
As AI technology continues to evolve, its role in education will likely expand. There was general agreement in the question and answer session that keeping a sense of community at the heart of computing classrooms will be important.
The Raspberry Pi Computing Education Research Centre and Faculty of Education at the University of Cambridge have recently published a teacher guide on the use of generative AI tools in education. The guide provides practical guidance for educators who are considering using generative AI tools in their teaching.
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, 12 November at 17:00–18:30 GMT to hear Nicholas Gardella (University of Virginia) discuss the effects of using tools like GitHub Copilot on the motivation, workload, emotion, and self-efficacy of novice programmers. 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 Using generative AI to teach computing: Insights from research appeared first on Raspberry Pi Foundation.
At the Raspberry Pi Foundation, we believe everyone belongs in computer science, and that it is a much more varied field than is commonly assumed. One of the ways we want to promote inclusivity and highlight the variety of skills and interests needed in computer science is through our ‘I belong’ campaign. We do this because the tech sector lacks diversity. Similarly, in schools, there is underrepresentation of students in computing along the axes of gender, ethnicity, and economic situation. (See how researchers describe data from England, and data from the USA.)
The ‘I belong’ campaign is part of our work on Isaac Computer Science, our free online learning platform for GCSE and A level students (ages 14 to 18) and their teachers, funded by the Department for Education. The campaign celebrates young computer scientists and how they came to love the subject, what their career journey has been so far, and what their thoughts are about inclusivity and belonging in their chosen field.
These people are role models who demonstrate that everyone belongs in computer science, and that everyone can bring their interests and skills to bear in the field. In this way, we want to show young people that they can do much more with computing than they might think, and to inspire them to consider how computing could be part of their own life and career path.
Salome is studying Computer Science with Digital Technology Solutions at the University of Leeds and doing a degree apprenticeship with PricewaterhouseCoopers (PwC).
“I was quite lucky, as growing up I saw a lot about women in STEM which inspired me to take this path. I think to improve the online community, we need to keep challenging stereotypes and getting more and more people to join, thereby improving the diversity. This way, a larger number of people can have role models and identify themselves with someone currently there.”
“Another thing is the assumption that computer science is just coding and not a wide and diverse field. I still have to explain to my friends what computer science involves and can become, and then they will say, ‘Wow, that’s really interesting, I didn’t know that.'”
Devyani is a third-year degree apprentice at Cisco.
“It was at A level where I developed my programming skills, and it was more practical rather than theoretical. I managed to complete a programming project where I utilised PHP, JavaScript, and phpMyAdmin (which is a database). It was after this that I started looking around and applying for degree apprenticeships. I thought that university wasn’t for me, because I wanted a more practical and hands-on approach, as I learn better that way.”
“At the moment, I’m currently doing a product owner role, which is where I hope to graduate into. It’s a mix between both a business role and a technical role. I have to stay up to speed with the current technologies we are using and developing for our clients and customers, but also I have to understand business needs and ensure that the team is able to develop and deliver on time to meet those needs.”
Omar is a Mexican palaeontologist who uses computer science to study dinosaur bones.
“I try to bring aspects that are very well developed in computer science and apply them in palaeontology. For instance, when digitising the vertebrae, I use a lot of information theory. I also use a lot of data science and integrity to make sure that what we have captured is comparable with what other people have found.”
“What drove me to computers was the fact you are always learning. That’s what keeps me interested in science: that I can keep growing, learn from others, and I can teach people. That’s the other thing that makes me feel like I belong, which is when I am able to communicate the things I know to someone else and I can see the face of the other person when they start to grasp a theory.”
Tasnima is a computer science graduate from Queen Mary University of London, and is currently working as a software engineer at Credit Suisse.
“During the pandemic, one of the good things to come out of it is that I could work from home, and that means working with people all over the world, bringing together every race, religion, gender, etc. Even though we are all very different, the one thing we all have in common is that we’re passionate about technology and computer science. Another thing is being able to work in technology in the real world. It has allowed me to work in an environment that is highly collaborative. I always feel like you’re involved from the get-go.”
“I think we need to also break the image that computer science is all about coding. I’ve had friends that have stayed away from any tech jobs because they think that they don’t want to code, but there’s so many other roles within technology and jobs that actually require no coding whatsoever.”
Aleena is a software engineer who works at a health tech startup in London and is also studying for a master’s degree in AI ethics at the University of Cambridge.
“I do quite a lot of different things as an engineer. It’s not just coding, which is part of it but it is a relatively small percentage, compared to a lot of other things. […] There’s a lot of collaborative time and I would say a quarter or third of the week is me by myself writing code. The other time is spent collaborating and working with other people and making sure that we’re all aligned on what we are working on.”
“I think it’s actually a very diverse field of tech to work in, once you actually end up in the industry. When studying STEM subjects at a college or university level it is often not very diverse. The industry is the opposite. A lot of people come from self-taught or bootcamp backgrounds, there’s a lot of ways to get into tech and software engineering, and I really like that aspect of it. Computer science isn’t the only way to go about it.”
Alice is a final-year undergraduate student of Computer Science with Artificial Intelligence at the University of Brighton. She is also the winner of the Global Challenges COVID-19 Research Scholarship offered by Santander Universities.
“[W]e need to advertise computer science as more than just a room full of computers, and to advertise computer sciences as highly collaborative. It’s very creative. If you’re on a team of developers, there’s a lot of communication involved.”
“There’s something about computer science that I think is so special: the fact that it is a skill anybody can learn, regardless of who they are. With the right idea, anybody can build anything.”
Help us spread the message that everyone belongs in computer science: share this blog with schools, teachers, STEM clubs, parents, and young people you want to inspire.
Whether you’re studying or teaching computer science GCSE or A levels in the UK (or thinking about doing so!), or you’re a teacher or student in another part of the world, Isaac Computer Science is here to help you achieve your computer science goals. Our high-quality learning platform is free to use and open to all. As a student, you can register to keep track of your progress. As a teacher, you can sign up to guide your students’ learning.
And for younger learners, we have lots of fun project guides to try out coding and creating with digital technologies.
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A portable, affordable Raspberry Pi-powered blood analyser is helping to “establish a robust healthcare ecosystem” in remote parts of India. Samples can be tested in just 30 seconds, and the cost and size of the parts make it an attractive solution for rural and resource-strapped areas.
It is the work of researchers Sangeeta Palekar and Jayu Kalambe from the Department of Electronics Engineering at Shri Ramdeobaba College of Engineering and Management.
Regular blood tests are vital in the tracking and elimination of many diseases, but there is a huge time and monetary cost currently tied to this type of laboratory work.
The researchers’s device measures light absorbance through a blood sample, a common type of analysis, and they harnessed the processing capability of Raspberry Pi 4 Model B to analyse the absorbance data. Their Raspberry Pi-powered solution was found to perform on a par with the kind of expensive lab-based blood test typically used.
Sangeeta and Jayu’s analyser is not only cheaper to build and maintain than the lab-based version, it also does the job better. Using the lab-based method means that samples from patients in rural areas must be sent away for analysis, with results communicated back to patients at a much later date. In contrast, Sangeeta and Jayu’s device can process blood samples there and then. All you need is an electricity source. Patients get their results immediately, and there is no need to transport delicate samples across rural terrain.
Incorporating an IoT element into their design, which would allow for remote monitoring, is the next step for the researchers. They also intend to develop their invention to allow it to carry out different types of blood analyses.
The full research paper is behind a paywall, but the abstract does a great job succinctly explaining all the science. Sangeeta herself also explains a lot of the magic behind her creation in this interview with IEEE Spectrum.
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Inspired by NASA’s attempt to launch a helicopter on Mars, one maker made an Earth-bound one of her own. And she tells Rosie Hattersley all about it in the latest issue of The MagPi Magazine, out now.
Like millions of us, in April Avra Saslow watched with bated breath as NASA’s Perseverance rover touched down on the surface of Mars.
Like most of us, Avra knew all about the other ground-breaking feat being trialled alongside Perseverance: a helicopter launch called Ingenuity, that was to be the first flight on another planet – “a fairly lofty goal”, says Avra, since “the atmosphere on Mars is 60 times less dense than Earth’s.”
With experience of Raspberry Pi-based creations, Avra was keen to emulate Ingenuity back here on earth.
NASA chose to use open-source products and use commercially available parts for its helicopter build. It just so happened that Avra had recently begun working at SparkFun, a Colorado-based reseller that sells the very same Garmin LIDAR-Lite v3 laser altimeter that NASA’s helicopter is based on. “It’s a compact optical distance measurement sensor that gives the helicopter ‘eyes’ to see how far it hovers above ground,” Avra explains.
NASA posted the Ingenuity helicopter’s open-source autonomous space-flight software, written specifically for use with Raspberry Pi, on GitHub. Avra took all this as a sign she “just had to experiment with the same technology they sent to Mars.”
Her plan was to see whether she could get GPS and lidar working within NASA’s framework, “and then take the sensors up on a drone and see how it all performed in the air.” Helpfully, NASA’s GitHub post included a detailed F Prime tutorial based around Raspberry Pi. Avra says understanding and using F Prime (F´) was the hardest part of her Epigone drone project. “It’s a beast to take on from an electronics enthusiast standpoint,” she says. Even so, she emphatically encourages others to explore F´ and the opportunity to make use of NASA’s code.
The Epigone Drone is built around Raspberry Pi 4 Model B; Garmin’s LIDAR-Lite v4, which connects to a Qwiic breakout board and has a laser rather than an LED; a battery pack; and a DJI Mini 2 drone borrowed from a videographer colleague. Having seen how small the drone was, Avra realised 3D-printing an enclosure case would make everything far too heavy. As it was, positioning the Epigone onto its host drone was challenging enough: the drone’s rotors passed worryingly close to the project’s Raspberry Pi, even when precisely positioned in the centre of the drone’s back. The drone has its own sensors to allow for controlled navigation, which meant Avra’s design had to diverge from NASA’s and have its lidar ‘eyes’ on its side rather than underneath.
Although her version piggybacks on an existing drone, Avra was amazed when her Epigone creation took flight:
“I honestly thought [it] would be too heavy to achieve lift, but what do ya know, it flew! It went up maybe 30 ft and we were able to check the sensors by moving it close and far from the SparkFun HQ [where she works].”
While the drone’s battery depleted in “a matter of minutes” due to its additional load, the Epigone worked well and could be deployed to map small areas of land such as elevation changes in a garden, Avra suggests.
You can grab the brand-new issue right now from the Raspberry Pi Press store, or via our app on Android or iOS. You can also pick it up from supermarkets and newsagents. There’s also a free PDF you can download.
The post ‘Epigone drone’ pays homage to NASA’s Mars Helicopter | The MagPi #107 appeared first on Raspberry Pi.
Imagine walking through a doorway, and your entrance music begins playing. You’re a pro wrestler, or perhaps a character in a 1940’s hardboiled crime drama? Or the Queen, who no doubt gets to hear the national anthem on a regular basis.
Ty and Gig Builds have a tonne of cool Raspberry Pi projects on their YouTube channel, but we especially liked this one. Why? Because it’s the perfect device to welcome everyone to Raspberry Pi Towers each day. At least, once the time comes to leave our home offices and head back to Pi Towers.
A Python script running on a Raspberry Pi continuously searches for nearby Bluetooth devices in a ‘known’ list. It uses the Bluetooth MAC address of each detected phone in range to determine which entrance music its owner wants. All ‘known’ device owners have already nominated a chosen track, which is stored on the Raspberry Pi, and when it recognises one from its list, the Raspberry Pi plays the owner’s chosen audio file.
Raspberry Pi uses the Bluez stack to communicate over Bluetooth, it’s this service that lets the Raspberry Pi talk to your phone. Ty admits this was the fiddliest part of the build, but he explains it all very clearly, step-by-step, from this point in the video. A sound card, connected via USB, lets Raspberry Pi output audio through a big speaker. But you could also make the sound play through an HDMI TV.
Wanna walk into the house with your own entrance music playing? Here’s the github repo with everything you’ll need. A Raspberry Pi 4 powers this project, but any of our boards with Bluetooth support would work.
You’re also going to need a separate sound card and here’s the one used in this build.
Some suggestions from the soon-to-return to Raspberry Pi Towers team:
Comment with your entrance tune of choice.
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The Orient Express. The Flying Scotsman. Ivor the Engine. All juggernauts of the rail community, but none powered by our microcontroller and all, thus, inferior in our eyes. Raspberry Pi Pico has been used in cooler and more interesting ways every day since its launch in January this year, but this is the first time we’ve seen it powering a miniature railway. KushagraK7 shared this compact application on Instructables, and we ended up down a rabbit hole of model trains enthusiasm.
KushagraK7’s Raspberry Pi Pico controls the track voltage to control the speed of the train using pulse-width modulation (PWM). PWM is a method of reducing the average power delivered by an electrical signal. A motor driver powers the locomotive itself.
This particular setup is designed to make the train start off slowly then speed up gradually each time it travels over a sensored segment of the track — that is, a segment equipped with an infrared sensor to detect whether a train is there. A therapeutic loop of the speeding-up process plays from this point in KushagraK7’s YouTube video.
Once the train reaches its top speed, it slows down again, coming to a complete halt after it passes the sensored track section once more. The train stops for a set amount of time, then starts up again. Fast, faster, slow, stop. Fast, faster, slow, stop. And on and on and on again. All without any human interaction needed – you can just watch. Super satisfying.
KushagraK7 has created an illustrated step-by-step tutorial for other miniature railway enthusiasts to follow, including when you should tidy up your wires, plus ideas to tinker with the code to adjust speed and stopping patterns.
Point us to your Raspberry Pi-powered model railway projects in the comments. Choo choooooooo.
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If you liked the look of yesterday’s Raspberry Pi Roon Endpoint Music Streamer but thought: “Hey, you know what would be great? If it had a touchscreen,” then look no further. Home Theater Fanatics has built something using the same RoPieee software, but with the added feature of a screen, for those who need one.
The build cost for this is a little higher than the $150 estimate to recreate yesterday’s project, given the inclusion of a fancier Digital Audio Decoder and the touchscreen itself.
The brilliant Home Theater Fanatics show you how to put all of this together from this point in the build video, before moving on to the software install. They take care to go through all of the basics of the hardware in case you’re not familiar with things like ribbon cables or fans. It’s a really nice bird’s-eye view walkthrough, so beginners aren’t likely to have any problems following along.
Same as yesterday’s build:
At this point in the build video, Home Theater Fanatics go through the three steps you need to take to get the RoPieee and Roon software sorted out, then connect the DAC. Again, it’s a really clear, comprehensive on-screen walkthrough that beginners can be comfortable with.
Aside from being able to see the attributed artwork for the music you’re currently listening to, this touchscreen solution provides easy song switching during home workouts. It’s also a much snazzier-looking tabletop alternative to a plugged-in phone spouting a Spotify playlist.
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Our friend Mike Perez at Audio Arkitekts is back to show you how to build PiFi, a Raspberry Pi-powered Roon Endpoint Music Streamer. The whole build costs around $150, which is pretty good going for such a sleek-looking Roon-ready end product.
Roon is a platform for all the music in your home, and Roon Core (which works with this build) manages all your music files and streaming content. The idea behind Roon is to bring all your music together, so you don’t have to worry about where it’s stored, what format it’s in, or where you stream it from. You can start a free trial if you’re not already a user.
Fix the HiFiBerry DAC2 Pro into the top of the case with the line output and headphone outputs poking out. A Raspberry Pi 4 Model B is the brains of the operation, and slots nicely onto the HiFiBerry. The HiFiBerry HAT is compatible with all Raspberry Pi models with a 40-pin GPIO connector and just clicks right onto the GPIO pins. It is also directly powered by the Raspberry Pi so, no additional power supply needed.
Next, secure the bottom half of the case, making sure all the Raspberry Pi ports line up with the case’s ready-made holes. Mike did the whole thing by hand with just a little help from a screwdriver right at the end.
Download the latest RoPieee image onto your SD card to make it a Roon Ready End Point, then slot it back into your Raspberry Pi. Now you have a good-looking, affordable audio output ready to connect to your Roon Core.
And that’s it. See – told you it was easy. Don’t forget, Audio Arkitekts’ YouTube channel is a must-follow for all audiophiles.
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When we heard that James Dawson had rescued a load of well-worn Raspberry Pi 1 Model B and Model A computers from eBay, refurbished them, and sold them on, we felt warm and fuzzy knowing that some of our oldest devices would be finding new homes.
But the feels really hit when we learned that James is donating the money from those resales to us for our Learn at Home campaign, where we get Raspberry Pis into the hands of UK young people who need them the most.
We decided to learn a little more about the guy behind this generous idea.
I’m a 25-year-old guy from Newcastle Upon Tyne. I’ve always been into computers and started weekend work experience in a computer repair shop, which turned into an apprenticeship and then a full-time job, giving me a basic knowledge of board-level repairs and hardware diagnostics.
Around the time the first Raspberry Pi (the Model B) came out in 2012, the company I worked for took on a large client in their business IT support division that ran Linux based servers. I immediately purchased a Raspberry Pi and set about learning my way around the Linux terminal and picked it up pretty quickly.
I ended up supporting the aforementioned Linux-based servers for several years before moving on. Seven years later I’m a Senior Linux System Administrator / Platform engineer for a multinational company, and I’m not sure I’d be in this position if it wasn’t for Raspberry Pi!
This isn’t something I had planned to do, it just happened! I was looking for some Raspberry Pi accessories on eBay one night, when I came across a box of 200+ broken Raspberry Pis. I had to have them and save them from becoming e-waste, but I didn’t have a plan for them, or even know if they were in a fixable state.
Once I found out the condition and performed some diagnostics, I realised that well over half of them were repairable. Using a cheap 3.5″ TFT Raspberry Pi display and a hacky bash script, I created a diagnostic tool that tested the USB ports, Ethernet port, and display output.
The technical side of the repairs are detailed in a five-part (so far) blog. Get started on Part 1.
I initially decided to see if I could donate the refurbished units to schools or maker spaces, but it turns out donating seven-year-old hardware is harder than it sounds!
Thankfully, there are still a lot of people out there who are interested in early Raspberry Pi models, so I decided to sell them and donate the money. The Raspberry Pi Foundation, specifically their Learn at Home campaign, stood out to me.
The first batch I repaired sold out in two days. That raised £400, which has already been donated. I hope to raise around £800 in total, and the next batch will be listed for sale soon.
Keep up with James’s tech projects on his blog, or follow him on Twitter.
His latest refurbished batch of Raspberry Pi Model A computers is for sale on eBay, as well as a ton of Raspberry Pi Model B units.
Since last summer, we’ve been distributing free Raspberry Pi computers to young people in the UK who don’t have access to a computer at home to do their schoolwork. The £800 that James is raising will allow us to give four disadvantaged young people free Raspberry Pi computer kits and ongoing support so they can continue learning while at home during the pandemic.
Find out how you can donate to our Learn at Home campaign to help solve this urgent issue.
The post Raspberry Pi 1 Model B units brought back to life for charity appeared first on Raspberry Pi.
In the latest issue of HackSpace Magazine, out now, @MrPJEvans shows you how to add voice commands to your projects with a Raspberry Pi 4 and a microphone.
It’s amazing how we’ve come from everything being keyboard-based to so much voice control in our lives. Siri, Alexa, and Cortana are everywhere and happy to answer questions, play you music, or help automate your household.
For the keen maker, these offerings may not be ideal for augmenting their latest project as they are closed systems. The good news is, with a bit of help from Google, you can add voice recognition to your project and have complete control over what happens. You just need a Raspberry Pi 4, a speaker array, and a Google account to get started.
For a home assistant device, being able to hear you clearly is an essential. Many microphones are either too low-quality for the task, or are unidirectional: they only hear well in one direction. To the rescue comes Seeed’s ReSpeaker, an array of four microphones with some clever digital processing to provide the kind of listening capability normally found on an Amazon Echo device or Google Assistant. It’s also in a convenient HAT form factor, and comes with a ring of twelve RGB LEDs, so you can add visual effects too. Start with a Raspberry Pi OS Lite installation, and follow these instructions to get your ReSpeaker ready for use.
You’ll see later on that we can add the power of Google’s speech-to-text API by streaming audio over the internet. However, we don’t want to be doing that all the time. Snowboy is an offline ‘hotword’ detector. We can have Snowboy running all the time, and when your choice of word is ‘heard’, we switch to Google’s system for accurate processing. Snowboy can only handle a few words, so we only use it for the ‘trigger’ words. It’s not the friendliest of installations so, to get you up and running, we’ve provided step-by-step instructions.
As we’ve just mentioned, we can have a hotword (or trigger word) to activate full speech recognition so we can stay offline. To do this, Snowboy must be trained to understand the word chosen. The code that describes the word (and specifically your pronunciation of it) is called the model. Luckily, this whole process is handled for you at snowboy.kitt.ai, where you can create a model file in a matter of minutes and download it. Just say your choice of words three times, and you’re done. Transfer the model to your Raspberry Pi 4 and place it in your home directory.
After the trigger word is heard, we want Google’s fleet of super-servers to help us transcribe what is being said. To use Google’s speech-to-text API, you will need to create a Google application and give it permissions to use the API. When you create the application, you will be given the opportunity to download ‘credentials’ (a small text file) which will allow your setup to use the Google API. Please note that you will need a billable account for this, although you get one hour of free speech-to-text per month. Full instructions on how to get set up can be found here.
To use Google’s API, we need to install the firm’s speech-to-text SDK for Python so we can stream audio and get the results. On the command line, run the following:pip3 install google-cloud-speech(If you get an error, run sudo apt install python3-pip then try again).
Remember that credentials file? We need to tell the SDK where it is:export GOOGLE_APPLICATION_CREDENTIALS="/home/pi/[FILE_NAME].json"(Don’t forget to replace [FILE_NAME] with the actual name of the JSON file.)
Now download and run this test file. Try saying something and see what happens!
Now we can talk to our Raspberry Pi, it’s time to link the hotword system to the Google transcription service to create our very own virtual assistant. We’ve provided sample code so that you can see these two systems running together. Run it, then say your chosen hotword. Now ask ‘what time is it?’ to get a response. (Don’t forget to connect a speaker to the audio output if you’re not using HDMI.) Now it’s over to you. Try adding code to respond to certain commands such as ‘turn the light on’, or ‘what time is it?’
Each month, HackSpace magazine brings you the best projects, tips, tricks and tutorials from the makersphere. You can get it from the Raspberry Pi Press online store, The Raspberry Pi store in Cambridge, or your local newsagents.
Each issue is free to download from the HackSpace magazine website.
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Welcome to October, the month in which spiderwebs become decor and anything vaguely gruesome is considered ‘seasonal’. Such as bats. Bats are in fact cute, furry creatures, but as they are part of the ‘Halloweeny animal’ canon, I have a perfect excuse to sing their praises.
Tegwyn Twmffat was tasked with doing a bat survey on a derelict building, and they took to DesignSpark to share their Raspberry Pi–powered solution.
UK law protects nesting birds and roosting bats, so before you go knocking buildings down, you need a professional to check that no critters will be harmed in the process.
The problem with bats, compared to birds, is they are much harder to spot and have a tendency to hang out in tiny wall cavities. Enter this big ultrasonic microphone.
After the building was declared safely empty of bats, Tegwyn decided to keep hold of the expensive microphone (the metal tube in the image above) and have a crack at developing their own auto-classification system to detect which type of bats are about.
The ultrasonic mic picks up the audio data using an STM M0 processor and streams it to Raspberry Pi via USB. Raspberry Pi runs Alsa driver software and uses the bash language to receive the data.
It turns out there are no publicly available audio records of bats, so Tegwyn took to their own back garden and found 6 species to record. And with the help of a few other bat enthusiasts, they cobbled together an audio dataset of 9 of the 17 bat species found in the UK!
Tegwyn’s original post about their project features a 12-step walkthrough, as well as all the code and commands you’ll need to build your own system. And here’s the GitHub repository, where you can check for updates.
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8 Bits and a Byte created this voice-controllable, interactive, storytelling device, hidden inside a 1960s radio for extra aesthetic wonderfulness.
A Raspberry Pi 3B works with an AIY HAT, a microphone, and the device’s original speaker to run chatbot and speech-to-text artificial intelligence.
This creature is a Bajazzo TS made by Telefunken some time during the 1960s in West Germany, and this detail inspired the espionage-themed story that 8 Bits and a Byte retrofitted it to tell. Users are intelligence agents whose task is to find the evil Dr Donogood.
The device works like one of those ‘choose your own adventure’ books, asking you a series of questions and offering you several options. The story unfolds according to the options you choose, and leads you to a choice of endings.
8 Bits and a Byte designed a decision tree to provide a tight story frame, so users can’t go off on question-asking tangents.
When you see the ‘choose your own adventure’ frame set out like this, you can see how easy it is to create something that feels interactive, but really only needs to understand the difference between a few phrases: ‘laser pointer’; ‘lockpick’; ‘drink’; take bribe’, and ‘refuse bribe’.
Google Dialogflow is a free natural language understanding platform that makes it easy to design a conversational user interface, which is long-speak for ‘chatbot’.
There are a few steps between the user talking to the radio, and the radio figuring out how to respond. The speech-to-text and chatbot software need to work in tandem. For this project, the data flow runs like so:
1: The microphone detects that someone is speaking and records the audio.
2-3: Google AI (the Speech-To-Text box) processes the audio and extracts the words the user spoke as text.
4-5: The chatbot (Google Dialogflow) receives this text and matches it with the correct response, which is sent back to the Raspberry Pi.
6-7: Some more artificial intelligence uses this text to generate artificial speech.
8: This audio is played to the user via the speaker.
The post Raspberry Pi turns retro radio into interactive storyteller appeared first on Raspberry Pi.
Nandu Vadakkath was inspired by a line-following robot built (literally) entirely from salvage materials that could wait patiently and purchase beer for its maker in Tamil Nadu, India. So he set about making his own, but with the goal of making it capable of slightly more sophisticated tasks.
Nandu had ambitious plans for his robot: navigation, speech and listening, recognition, and much more were on the list of things he wanted it to do. And in order to make it do everything he wanted, he incorporated a lot of software, including:
So how did Nandu go about trying to make the robot do some of the things on his wishlist?
The engine uses spaCy to analyse sentences, classify all the elements it identifies, and store all this information in a MySQL database. When the robot encounters a sentence with a series of possible corresponding actions, it weighs them to see what the most likely context is, based on sentences it has previously encountered.
The robot has been trained to follow Nandu around but it can get to know other people too. When it meets a new person, it takes a series of photos and processes them in the background, so it learns to remember them.
Nandu didn’t like the thought of a basic robotic voice, so he searched high and low until he came across the MBROLA UK English voice. Have a listen in the videos above!
The robot has an excellent group photo function: it looks for a person, calculates the distance between the top of their head and the top of the frame, then tilts the camera until this distance is about 60 pixels. This is a lot more effort than some human photographers put into getting all of everyone’s heads into the frame.
Nandu has created a YouTube channel for his robot companion, so be sure to keep up with its progress!
The post Nandu’s lockdown Raspberry Pi robot project appeared first on Raspberry Pi.
Technology should be for everyone, but it has to be built by everyone to be for everyone. At Raspberry Pi, we work to empower everyone to become a tech creator and shape our collective digital future, and we hope that our work will help to increase the tech sector’s diversity.
Today, part of our team is attending WeAreTheCity’s WeAreTechWomen conference to spread the word about our free programming courses and encourage more women to share their digital skills with the next generation.
I asked Carrie Anne Philbin, our Director of Educator Support, and Vanessa Vallely OBE, Managing Director at WeAreTheCity, about their thoughts on how we can make the tech sector more diverse, and what part role models, education, and professional development play in this.
Vanessa, WeAreTheCity helps organisations foster a strong female workforce, and provides opportunities for women to network and develop their skills. Why do you think it’s important for women and people from minority backgrounds to support each other in the professional world?
Vanessa Vallely: I believe it is important for everyone to support each other. It is important that we work as a collective and collaborate, as at the end of the day we are all trying to achieve the same goal. 17% women in tech [in the UK] is not enough.
“We want more women in tech, and we want them to represent all aspects of society.” – Vanessa Vallely OBE
We cannot be what we cannot see, therefore asking women who are already working in tech to stand up and own their role model status is a great start.
What can individuals do to address the lack of diversity in the tech sector?
Carrie Anne Philbin: Firstly, let’s recognise that we need the tech sector to be more representative of the population of the world. It’s problematic to have a small subsection of society be the controllers of a growing digital world.
Then, we need to be the change we want to see in the industry. Let’s try different avenues and then let’s be open about our challenges and successes.
VV: I believe every woman in the tech sector is a role model to future generations. There are a number of things individuals can do, for example go back to their schools and tell their tech stories, or contribute/write blogs. This doesn’t just raise their profile, it puts their story out there for others to aspire to. I think this is really important, especially if the individual is from a background where role models are less visible. There are lots of different organisations and networks that facilitate individuals getting involved in their school or early career initiatives which has made it easier to get involved and give back.
CAP: As a woman in the computing field, I think it is important that I hold the door open for other women coming through in my wake, and that I highlight where I can, great work by others.
Ever since I realised that my skills and knowledge in computing were useful and allowed me to be creative in a whole new way, I’ve championed computer science as a subject that everyone should experience. Once you’ve created your first computer program or built your first network, you’ll never want to stop.
Carrie Anne, how does your coding session at WATC’s WeAreTechWomen conference today tie into this?
CAP: At the Raspberry Pi Foundation, I spend a lot of time thinking about how to teach computing well, and about how young people can have great learning experiences so they can become the makers and creators of tomorrow.
“Technology is not a mystery, nor is it hard to learn. I want to dispel this myth for everyone regardless of gender, ethnicity, or economic status.” – Carrie Anne Philbin
During my session at WeAreTechWomen, I hope to support attendees to write their first creative python program, based on a project I wrote for Code Club to create a virtual pet. It is my hope that the session will be the spark of inspiration that gets more women and men from diverse backgrounds excited about being creators of technology.
You’ve built a career in tech education as a teacher, YouTuber, and Director at Raspberry Pi. How can beginners get comfortable creating with tech?
CAP: There isn’t anything magical about technology, and once you know this, you can start to explore with confidence, much like our ancestors when they learned that the earth was round and not flat.
“Phrases like ‘I’m not good with technology’ or ‘It’s all too complicated for me’ are reassuring to say in a society where the accepted view is that maths and science are hard, and where this view is reinforced by our media. But it is OK to be a beginner, it is OK to learn something new, and it is OK to play, explore, fail, and succeed on the journey.” – Carrie Anne Philbin
However you like to learn, be it on your own or with others, there is a way that suits you! I’ve always been quite project-minded: I have ideas about things I want to make, and then go and see if I can. This is how I stumbled across the Raspberry Pi in 2012 — it seemed like an accessible and cheap way to make my automation dreams come true. It also wasn’t too bad at randomly generating poems.
Aside from teacher-led instruction or independent exploration, another way is to learn with others in a relaxed and informal setting. If you’re a young person, then clubs like Code Club and CoderDojo are perfect. If you’re an adult, then attending a Raspberry Jam or conferences like WeAreTechWomen can provide a supportive environment.
“By being kinder to ourselves and seeing ourselves as life-long learners, it is easier to overcome insecurity and build confidence.” – Carrie Anne Philbin
A great way to approach new learning is at your own pace, and thanks to technology, we have access to online training courses with great videos, exercises, and discussion — many of these are completely free and let you connect with a community of learners as well.
How do you think educating the next generation about computing will change the makeup of the tech sector?
CAP: We’re in an exciting phase for computing education. The world has woken up to the importance of equipping our young people with the knowledge and skills for an ever increasing digital landscape. This means computer science is gaining more prominence in school curricula and giving all children the opportunity to discover the subject.
“Education can be democratising, and I expect to see the makeup of the tech sector reflect this movement in the next five to twenty years.” – Carrie Anne Philbin
Unlike physics or music, computing is still a relatively young field, so we need to do more research into what is encouraging and what isn’t, particularly when we work with young people in schools or clubs.
We’re still learning how to teach computing, and particularly programming, well to encourage greater diversity, so it’s great to see such a vast Gender Balance in Computing research project underway as part of the National Centre for Computing Education here in England. It’s not too late for schools in England to get involved in this project either…
The post “17% women in tech is not enough” appeared first on Raspberry Pi.
All across the UK, you’ll find train departure boards on station platforms that look like this:
They’ve looked this way for as long as I can remember (before they were digital dot-matrix displays, they were made from those flappy bits of plastic with letters of the alphabet and numbers printed on them, which whirled round like a Rolodex; they still look very similar). If you’re a frequent train traveller in the UK, you probably have a weird emotional response to seeing one of these. Mine is largely one of panic about being late.
Some people have a more…benign relationship with trains than I do, like Chris Crocker-White, who has adapted a build tweeted by Chris Hutchinson to make a miniature departure board for his desk. Here’s the tweet that started it all:
Chris Hutchinson on Twitter
Pretty hyped about my most recent @Raspberry_Pi project – a realistic, real-time, train departure board I’ve open sourced the software over at: https://t.co/vGQzagsSpi Next step: find a case and make it a permanent fixture! https://t.co/HEXgzdH8TS
Chris C-W’s build is similar, but has a couple of very neat upgrades, including some back-end software work (his build runs in Docker on balenaCloud, to make configuration easier), and some work on the display, which he’s tweaked to use 1:1 pixel mapping of the fonts and avoid any scaling, so the tiny board looks more like the dot-matrix LED displays you’ll see when you visit the station. You can see the difference in the image below:
Chris seems to be using his board as a piece of desktop furniture, where it looks terrific, but model train or narrow-gauge enthusiasts should be all over this project too; it’s a lovely way to inject some realism into a miniature setup. You can find a very complete guide to making your own here.
Now, if you’ll excuse me, I have a train to catch.
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