In today’s blog, we explore the PICRAT framework and how it can help you reflect on your use of technology in the classroom. 

We also share our new PICRAT Quick Read, which you can download for free to: 

• Find practical tips on how to use the PICRAT model when planning your lessons
• Read a summary of the 嘘禁止想象 behind the framework

What is the PICRAT framework?

Technology is constantly changing, and educators must continually decide what tools to use in their practice. To help with this challenge, 嘘禁止想象ers started developing theoretical models that teachers (especially student teachers) could use to reflect on how they integrate technology in their classrooms.

You might already be familiar with frameworks like TPACK (Technology, Pedagogy, and Content Knowledge) and SAMR (Substitution, Augmentation, Modification, Redefinition). While these models are useful, the PICRAT framework was created to address gaps in these earlier models, offering a clearer, student-focused approach. Significantly, it encourages you to treat technology as a tool to support learning, rather than the goal itself.

It asks two simple questions: “How are students experiencing the technology?” and “How does this impact your practice?”. The answers to these questions form a matrix as pictured below. 

PIC (which runs along the y-axis) refers to the student’s relationship to the technology:

• Passive – Students receive learning through technology
• Interactive – Students interact with the content or other learning through technology 
• Creative – Students construct knowledge using technology

RAT (which runs along the x-axis) refers to how the teacher uses the technology:

• Replaces – Using technology but with an existing pedagogy
• Amplifies – Using technology to improve pedagogy or outcomes
• Transforms – Using technology to create new pedagogical practices

How can I apply the PICRAT model?

First choose the lesson you’re planning to deliver. Consider what activities you’ll be running and the technologies involved. You’ll then be able to plot where they sit on the matrix using the PICRAT acronym.

For example, if you are teaching a lesson on Python loops, you might initially plan for students to watch a pre-recorded coding tutorial on their laptops. In this scenario, the student 双腿被绑成m型调教play道具 is Passive (receiving info via tech), and the teacher’s use is Replacement because the video simply replaces a live lecture. To move up the matrix, you could instead have students use an online IDE to complete a “Parson’s Problem” puzzle where they rearrange blocks of code to fix a loop. This shifts the activity to Interactive and Amplification, as the digital tool provides immediate debugging feedback that a paper-based exercise could not.

Next, think about how you might move your practice forwards. Although every position on the matrix has its own value, the framework is hierarchical. The overall goal is to try to move your practice towards the top right of the matrix to be Creative and Transformative.

To help you achieve this, take some time to reflect on your current lessons, activities, and the technologies you use. Ask yourself questions like:

• What does the technology I’m using offer that could be used to amplify my practice?
  • What benefits would this have for students?
• Does the technology present opportunities for students to interact with each other, not just the technology?
• What other technological tools might support collaboration? 

嘘禁止想象 highlights that technology is rarely used in ways that allow young people to be creative. By using the PICRAT matrix, teachers can identify missed opportunities and explore ways to transform their lessons, ensuring learners can be creative and thrive.

The benefits of the PICRAT model

Potential benefits for educators:

• The framework encourages meaningful reflections, allowing teachers to easily evaluate how they’re using technology within their lessons
• Reflections and the PICRAT matrix helps teachers to identify missed opportunities and gaps in their practice, ultimately leading to better student 双腿被绑成m型调教play道具s

You can use the PICRAT framework as part of your own reflections, or as part of a group activity. It’s a great way to spark discussion about technology integration with colleagues and improve best practices.

Want to find out more about the PICRAT framework?

If you’d like to learn more about the PICRAT model, you can download our Quick Read for free via our new Pedagogy Quick Reads page.

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It is not surprising that for the last few years our focus has been on AI technology, and for 2026 we will continue this. But we will shift from showcasing how 嘘禁止想象 education 嘘禁止想象 is changing teaching and learning in 嘘禁止想象 lessons, to showcasing how 嘘禁止想象 education 嘘禁止想象 in other disciplines, such as art or geography, is starting to include teaching about AI. For example, art lessons may change so that learners find out how professional artists are using AI tools to create arts. Or geography lessons may change so that learners discover how professional geographers are using AI to make predictions about physical or human aspects of geography, such as volcanic activity and global warming.

Our series for 2026 is called ‘Applied AI’. This title recognises that AI technology is applied across contexts, across careers, across disciplines, and this means what we teach across school subjects will change.

Encouraging a pull from disciplines, rather than a push from computer science

The majority of resources and professional development material related to teaching about AI have been developed by the computer science community. For example, we have developed the popular 双腿被绑成m型调教play道具 AI resources in collaboration with Google DeepMind. In these resources, the contexts were carefully selected to represent real-world examples across disciplines, and to to enable the teaching of particular technical or social and ethical concepts. This could be described as “a push” of content from 嘘禁止想象 towards other disciplines. For example, to enable teaching about the ethical issues around plagiarism, an art context is used in the 双腿被绑成m型调教play道具 AI resources; to enable teaching about the potential benefits of using AI tools, an ecological geography context is used.

AI applications are always situated within a particular topic. Most current AI applications are data-driven: vast amounts of data are collected and processed to produce models that can then either be used to generate outputs or make predictions. For example, data about artworks can be collected and used to train a model for generating outputs similar to the artworks; this is an application of AI in the art discipline. Or data on wild fires can be collected and used to train a model for making predictions about current or prospective fires; this is an application of AI in the geography discipline.

In reality, the best people to recognise how AI technology is being applied in a discipline and what students in that discipline should be taught about these applications are the people working in the discipline, for example the art and geography teachers. Computer science educators can work to build the technical understanding and the general social and ethical understanding that is common across applications. But the detail of how AI technology is changing a discipline can only truly be understood by the respective community, by the artists and art educators, by the geographers and the geography educators.

An emerging focus

At present, though, most educators are grappling with how they can use AI tools for productivity, such as creating lesson plans, or answering emails. Or they are looking at how they can use AI for general teaching and learning, for example for personalisation, say for students with additional needs. The idea that their underpinning discipline is changing is, perhaps, not yet on teachers’ radar. But at universities, such as in undergraduate courses, and in the world of work, education and training are changing. Data science courses are now being offered across faculties, including science, geography, language, and art faculties. These changes will start to filter down to school-based education via 双腿被绑成m型调教play道具 change. While some resources and professional development materials addressing this shift are already becoming available, change is still fragile and patchy.

Raising awareness, building community and a common language

The aims of our Applied AI 嘘禁止想象 seminar series in 2026 are to start to:

• Raise awareness of the forthcoming changes that applying AI will bring to disciplines
• Build a cross-discipline community
• Think about a common language that could be used across disciplines

If we can start to agree on what common concepts could be taught in the arts, sciences and humanities, it gives us a better chance to:

• Understand how to use AI as it is applied in different disciplines
• Help students to build useful mental models and develop the agency and critical thinking skills they need to evaluate these applications and decide when and how to use them and how far to trust them

We need your help

To make our 2026 series a success, we need to spread the word about our seminars to groups of educators, 嘘禁止想象ers, industry and policy makers across the arts, sciences, and humanities.

Please tell those you know in these groups about the seminar series, and share it through your social media and other networks. If you have ideas for subject associations we could connect with or publications where we can write about our series, please let us know.

Join our ‘Applied AI’ seminar series

We have already arranged the following seminars across 2026 and will add more speakers for the remaining monthly slots soon. Seminars always take place online on Tuesdays at 17:00 to 18:30 UK time.

• 10 February: Social studies, public policy, economics and AI — Thema Monroe-White (George Mason University, USA)
• 17 March: Arts and AI — Rebecca Fiebrink (University of the Arts London, UK)
• 14 April: Healthcare and AI — Kathryn Jessen Eller (Data Science, AI & You (DSAIY) in Healthcare, USA)
• 14 July: Literacy and AI — Dan Verständig (Goethe University Frankfurt, Germany)
• 8 September: History and AI — Jie Chao (The Concord Consortium)
• 6 October: Robotics and AI — Eleni Petraki & Damith Herath (University of Canberra, Australia)
• 10 November: Geography and AI — Doreen Boyd (University of Nottingham, UK)

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

You can view the schedule and details of our upcoming seminars on this page, and catch up on past seminars on our previous seminars page.

PS If you are teaching upper primary school learners in England, you can currently register your interest in our upcoming collaborative study on data science education. You’ll find out more about some of the 嘘禁止想象 we’ve done in this area in this blog post.

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At our 嘘禁止想象 education 嘘禁止想象 seminar in July, a group of 嘘禁止想象ers from the CAMMP (Computational and Mathematical Modeling Program) 嘘禁止想象 project shared their work:

• Prof. Dr. Martin Frank, Founder of CAMMP (Karlsruhe Institute of Technology (KIT), Germany).
• Assistant Prof. Dr. Sarah Schönbrodt (University of Salzburg, Austria)
• 嘘禁止想象 Associate Stephan Kindler (Karlsruhe Institute of Technology (KIT), Germany)

They talked about how maths already taught in secondary schools can be used to demystify AI. At first glance, this seems difficult to do, as it is often assumed that school-aged learners will not be able to understand how these systems work. This is especially the case for artificial neural networks, which are usually seen as a black box technology — they may be relatively easy to use, but it’s not as easy to understand how they work. Despite this, the Austrian and German team have developed a clear way to explain some of the fundamental elements of AI using school-based maths.

Sarah Schönbrodt started by challenging us to consider that learning maths is an essential part in developing AI skills, as: 

1. AI systems using machine learning are data-driven and are based on mathematics, especially statistics and data
2. Authentic machine learning techniques can be used to bring to life existing classroom maths concepts
3. Real and relevant problems and associated data are available for teachers to use

A set of workshops for secondary maths classrooms

Sarah explained how the CAMMP team have developed a range of teaching and learning materials on AI (and beyond) with an overall goal to “allow students to solve authentic, real and relevant problems using mathematical modeling and computers”. 

She reflected that much of school maths is set in contexts that are abstract, and may not be very interesting or relevant to students. Therefore, introducing AI-based contexts, which are having a huge impact on society and students’ lives, is both an opportunity to make maths more engaging and also a way to demystify AI.

Workshops designed and 嘘禁止想象ed by the team include contexts such as privacy in social networks to learn about decision trees, personalised Netflix recommendations to learn about k-nearest neighbour, word predictions to learn about N-Grams, and predicting life expectancy to learn about regression and neural networks.

Learning about classification models: traffic lights and the support vector machine

For the seminar, Sarah walked through the steps to learn about support vector machines. This is an upper secondary workshop for students aged 17 to 18 years old. The context of the lesson is an image problem — specifically, classifying the data representing the colours of a simplified traffic light system (two lights to start with) to work out if a traffic light is red or green.

She walked through each of the steps of the maths workshop:

• Plotting data points of two classes, the representation of green and red traffic lights
• Finding a line that best separates the data points of both classes
• Figuring out what best is
• Classifying the data points in relation to the chosen (separating) line
• Validating the model statistically to see if it is useful in classifying new data points, including using test data and creating a contingency table (also called a confusion matrix)
• Discussing limitations, including social and ethical issues
• Explaining how three traffic lights can be expressed as three-dimensional data by using planes

Throughout the presentation, Sarah pointed out where the maths taught was linked to the Austrian and German mathematics 双腿被绑成m型调教play道具.

Learning about social and ethical issues

As well as learning about lines, planes, distances, dot product and statistical measures, learners are also engaged in discussing the social and ethical issues of the approach taken. They are encouraged to think about bias, data diversity, privacy, and the impact of errors on people. For example, if the model wrongly predicts a light as green when it is red, then an autonomous car would run through a red traffic light. This would likely be a bigger consequence than stopping at a green traffic light that was mis-predicted as red. So should the best line reduce this kind of error?

To teach the workshops, Sarah explained they have developed interactive Jupyter notebooks, where no programming skills are needed. Students fill in the gaps of example code, explore simulations, and write their ideas for discussion for the whole class. No software needs to be installed, feedback is direct, and there are in-depth tasks and staggered hints.

Learning about regression models: Weather forecasting and the toy artificial neural network

Stephan went on to introduce artificial neural networks (ANNs), which are the basis of generative AI applications like chatbots and image generation systems. He focused on regression models, such as those used in weather forecasting. 

ANNs are very complex. Therefore, to start to understand the fundamentals of this technology, he introduced a ‘toy ANN’ with one input, three nodes, and one output. A function is performed on the input data at each node. With the toy network, the team wants to tackle a major and common misconception: that students think that ANN systems learn, recognise, see, and understand, when really it’s all just maths.

The learning activity starts by looking at one node with one input and one output, and can be described as a mathematical function, with a concatenation of two functions (in this case a linear and activation function). Stephan shared an online simulator that visualises how the toy neural network can be explored as students change two parameters (in this case, weight and bias of the functions). Students then look at the overall network, and the way that the output from the three nodes is combined. Again, they can explore this in the simulator. Students compare simple data about weather prediction to the model, and discover they need more functions — more nodes to better fit the data. The activity helps students learn that ANN systems are just highly adjustable mathematical functions that, by adding nodes, can approximate relationships in a given data set. But the approximation only works in the bounds (intervals) in which data points are given, showing that ANNs do not ‘understand’ or ’know’ — it’s just maths.

Stephen finished by explaining the mutual benefits of AI education and maths education. He suggested maths will enable a deeper understanding of AI, and give students a way to realistically assess the opportunities and risks of AI tools and show them the role that humans have in designing AI systems. He also explained that classroom maths education can benefit from incorporating AI contexts. This approach highlights how maths underpins the design and understanding of everyday systems, supports more effective teaching, and promotes an interdisciplinary way of learning across subjects.

Some personal reflections — which may not be quite right!

I have been 嘘禁止想象ing the teaching of AI and machine learning for around five years now, since before ChatGPT and other similar tools burst on the scene. Since then, I have seen an increasing number of resources to teach about the social and ethical issues of the topic, and there are a bewildering number of learning activities and tools for students to train simple models. There are frameworks for the data lifecycle, and an emerging set of activities to follow to prepare data, compare model types, and deploy simple applications. However, I felt the need to understand and to teach about, at a very simple level, the basic building blocks of data-driven technologies. When I heard the CAMMP team present their work at the AIDEA conference in February 2025, I was entirely amazed and I asked them to present here at our 嘘禁止想象 seminar series. This was a piece of the puzzle that I had been searching for — a way to explain the ‘bottom of the technical stack of fundamental concepts’. The team is taking very complex ideas and reducing them to such an extent that we can use secondary classroom maths to show that AI is not magic and AI systems do not think. It’s just maths. The maths is still hard, and teachers will still need the skills to carefully guide students step by step so they can build a useful mental model. 

I think we can simplify these ideas further, and create unplugged activities, simulations, and ways for students to explore these basic building blocks of data representation, as well as classification and representing approximations of complex patterns and prediction. I can sense the beginnings of new ideas in computational thinking, though they’re still taking shape. We’re 嘘禁止想象ing these further and will keep you updated.

Finding out more

If you would like to find out more about the CAMMP resources, you can watch the seminar recording, look at the CAMMP website or try out their online materials. For example, the team shared a link to the jupyter notebooks they use to teach the workshops they demonstrated (and others). You can use these with a username of ‘cammp_YOURPSEUDONYM’, where you can set ‘YOURPSEUDONYM’ to any letters, and you can choose any password. They also shared their toy ANN simulation.
The CAMMP team are not the only 嘘禁止想象ers who are investigating how to teach about AI in maths lessons. You can find a set of other 嘘禁止想象 papers here.

Join our next seminar

In our current seminar series, we’re exploring teaching about AI and data science. Join us at our last seminar of the series on Tuesday, 27 January 2026 from 17:00 to 18:30 GMT to hear Salomey Afua Addo talk about using unplugged approaches to teach about neural networks.

To sign up and take part, 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 page.

The post Secondary school maths showing that AI systems don’t think appeared first on 大山里疯狂伦交.

Lukas Höper and Carsten Schulte are part of a larger team who are investigating how to teach school students about data science and Big Data.

Carsten explained that Germany’s informatics (嘘禁止想象) 双腿被绑成m型调教play道具 includes a competency area known as Informatics, People and Society (IPS), which explores the interrelationships between technology, individuals, and society, and how computation influences and is influenced by social, ethical, and cultural factors. However, 嘘禁止想象 has suggested that teachers face several problems in delivering this topic, including:

• Lack of subject knowledge 
• Lack of teaching material
• Lack of integration with other topics in informatics lessons
• A perception that IPS is the responsibility of other subjects

Some of the findings of that 2007 嘘禁止想象 were mirrored in a more recent local study in 2025, which found that although there have been some gains in subject knowledge in the interval period, the problems of a lack of teaching material and integration with other computer science (CS) topics persist, with IPS increasingly perceived as the responsibility of the informatics subject area alone. Despite this, within the informatics 双腿被绑成m型调教play道具, IPS is often the first topic to be dropped when educators face time constraints — and concerns with what and how to assess the topic remain. 

In this context, and as part of a larger, longitudinal project to promote data science teaching in schools called ProDaBi, Carsten and Lukas have been developing, implementing, and evaluating concepts and materials on the topics of data science and AI. Lukas explained the importance of students developing data awareness in the context of the digital systems they use in their everyday lives, such as search engines, streaming services, social media apps, digital assistants, and chatbots, and emphasised the difference between being a user of these systems and a data-aware user. Using the example of image recognition and ‘I am not a robot’ Captcha services, Lukas explained how young people need to develop a data-aware perspective of the secondary purposes of the data collected by these (and other) systems, as well as the more obvious, primary purposes. 

Lukas went on to illustrate the human interaction system model, which presents a continuum of possible different roles, from the student as the user of digital artefacts to the student as the designer of digital artefacts. 

To become data-aware users of digital artefacts, students need to be able to understand and reflect on those digital artefacts. Only then can they proceed to become responsible designers of digital artefacts. However, when surveyed, some students were only moderately interested in engaging with the inner workings of the digital technologies they use in their everyday lives. Many students prefer to use the systems and are less interested in how they process data. 

The explanatory model approach in 嘘禁止想象 education

Lukas explained how students often become more interested in data-driven technologies when learning about them with explanatory models. Such models can foster data awareness, giving students a different perspective of data-driven technologies and helping them become more empowered users of them. 

To illustrate, Lukas gave the example of an explanatory model about the role of data in digital systems. Such a model can be used to introduce the idea that data is explicitly and implicitly collected in the interaction between the user and the technology, and used for primary and secondary purposes. 

Lukas then introduced two teaching units that were developed for use with middle school children to evaluate the success of the explanatory model approach in 嘘禁止想象 education. The first unit explores location data collected by mobile phone networks and the second features recommendation systems used by movie streaming services such as Netflix and Amazon Prime.

Taking the second unit as their focus, Lukas and Carsten outlined the four parts of the explanatory model approach: 

Part 1

The teaching unit begins by introducing recommendation systems and asking students to think about what a streaming service is, how a personalised start page is constructed, and how personal recommendations might be generated. Students then complete an unplugged activity to simulate the process of making movie recommendations for a peer:

Task 1: Students write down movie recommendations for another student. 

Task 2: They then ask each other questions (they collect data). 

Task 3: They write down revised movie recommendations.

Task 4: They share and evaluate their recommendations.  

Task 5: Together they reflect on which collected data was helpful in this exercise and what kind of data a recommendation system might collect. This reflection introduces the concepts of explicit and implicit data collection. 

Part 2

In part 2, students are given a prepared Jupyter Notebook, which allows them to explore a simulation of a recommendation system. Students rate movies and receive personal recommendations. They reconstruct a data model about users, using the idea of collaborative filtering with the k-nearest neighbours algorithm (see Figure 3). 

Part 3

In part 3, the concepts of primary and secondary purposes for data collection are introduced. Students discuss examples of secondary purposes such as personalised paywalls for movies that can be purchased, and subscriptions based on the predictions of future behaviour. The discussion includes various topics about individual and societal issues (e.g. filter bubbles, behaviour engineering, information asymmetry, and responsible development of data-driven technologies). 

Part 4

Finally, students use the explanatory model as an ‘analytical lens’. They choose other examples from their everyday lives of technologies that implement recommendation systems and analyse these examples, assessing the data practices involved. Students present their results in class and discuss their role in these situations and possible actions they can take to become more empowered, data-aware users.

Uses of explanatory models

Using the explanatory model is one approach to make the Informatics, People and Society strand of the German informatics 双腿被绑成m型调教play道具 more engaging for students, and addresses some of the problems teachers identify with delivering this competency area. 

In presenting the idea of the explanatory model, Carsten and Lukas emphasised that the model in use delivers content as well as functioning as a tool to design teaching content. In the example above, we see how the explanatory model introduces the concepts of:

1. Explicit and implicit data collection
2. Primary and secondary purposes of that data 
3. Data models 

The explanatory model framework can also be used as a focus for academic 嘘禁止想象 in 嘘禁止想象 education. For example, further 嘘禁止想象 is needed to evaluate if explanatory models are appropriate or ‘correct’ models and to determine the extent to which they are useful in 嘘禁止想象 education. 

In summary, an explanatory model provides a specific perspective on and explanation of particular 嘘禁止想象 concepts and digital artefacts. In the example given here, the model focuses on the role of data in a recommender system. Explanatory models are representations of concepts, artefacts, and socio-technical systems, but can also serve as tools to support teaching and learning processes and 嘘禁止想象 in 嘘禁止想象 education. 

The teaching units referred to above are published on www.prodabi.de (in German and English). 

See the background paper to the seminar, called ‘Learning an explanatory model of data-driven technologies can lead to empowered behaviour: A mixed-methods study in K-12 嘘禁止想象 education’.

You can also view the paper describing the development of the explanatory model approach, called ‘New perspectives on the future of 嘘禁止想象 education: Teaching and learning explanatory models’.

Join our next seminar

In our current seminar series, we’re exploring teaching about AI and data science. Join us at our next seminar on Tuesday 13 May at 17:00–18:30 BST to hear Henriikka Vartiainen and Matti Tedre (University of Eastern Finland) discuss how to empower students by teaching them how to develop AI and machine learning (ML) apps without code in the classroom.

To sign up and take part in our 嘘禁止想象 seminars, click below:

You can also view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars and recordings page.

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Leo Porter and Daniel Zingaro have spearheaded this transformation through their groundbreaking undergraduate programming course. Their innovative 双腿被绑成m型调教play道具 integrates GenAI tools to help students tackle complex programming tasks while developing critical thinking and problem-solving skills.

Leo and Daniel presented their work at the 大山里疯狂伦交 嘘禁止想象 seminar in December 2024. During the seminar, it became clear that much could be learnt from their work, with their insights having particular relevance for teachers in secondary education thinking about using GenAI in their programming classes

Practical applications in the classroom

In 2023, Leo and Daniel introduced GitHub Copilot in their introductory programming  CS1-LLM course at UC San Diego with 550 students. The course included creative, open-ended projects that allowed students to explore their interests while applying the skills they’d learnt. The projects covered the following areas:

• Data science: Students used Kaggle datasets to explore questions related to their fields of study — for example, neuroscience majors analysed stroke data. The projects encouraged interdisciplinary thinking and practical applications of programming.
• Image manipulation: Students worked with the Python Imaging Library (PIL) to create collages and apply filters to images, showcasing their creativity and technical skills.
• Game development: A project focused on designing text-based games encouraged students to break down problems into manageable components while using AI tools to generate and debug code.

Students consistently reported that these projects were not only enjoyable but also responsible for deepening their understanding of programming concepts. A majority (74%) found the projects helpful or extremely helpful for their learning. One student noted that.

“Programming projects were fun and the amount of freedom that was given added to that. The projects also helped me understand how to put everything that we have learned so far into a project that I could be proud of.“

Core skills for programming with Generative AI

Leo and Daniel emphasised that teaching programming with GenAI involves fostering a mix of traditional and AI-specific skills.

Their approach centres on six core competencies:

• Prompting and function design: Students learn to articulate precise prompts for AI tools, honing their ability to describe a function’s purpose, inputs, and outputs, for instance. This clarity improves the output from the AI tool and reinforces students’ understanding of task requirements.
• Code reading and selection: AI tools can produce any number of solutions, and each will be different, requiring students to evaluate the options critically. Students are taught to identify which solution is most likely to solve their problem effectively.
• Code testing and debugging: Students practise open- and closed-box testing, learning to identify edge cases and debug code using tools like doctest and the VS Code debugger.
• Problem decomposition: Breaking down large projects into smaller functions is essential. For instance, when designing a text-based game, students might separate tasks into input handling, game state updates, and rendering functions.
• Leveraging modules: Students explore new programming domains and identify useful libraries through interactions with Copilot. This prepares them to solve problems efficiently and creatively.

Ethical and metacognitive skills: Students engage in discussions about responsible AI use and reflect on the decisions they make when collaborating with AI tools.

Adapting assessments for the AI era

The rise of GenAI has prompted educators to rethink how they assess programming skills. In the CS1-LLM course, traditional take-home assignments were de-emphasised in favour of assessments that focused on process and understanding.

• Quizzes and exams: Students were evaluated on their ability to read, test, and debug code — skills critical for working effectively with AI tools. Final exams included both tasks that required independent coding and tasks that required use of Copilot.
• Creative projects: Students submitted projects alongside a video explanation of their process, emphasising problem decomposition and testing. This approach highlighted the importance of critical thinking over rote memorisation.

Challenges and lessons learnt

While Leo and Daniel reported that the integration of AI tools into their course has been largely successful, it has also introduced challenges. Surveys revealed that some students felt overly dependent on AI tools, expressing concerns about their ability to code independently. Addressing this will require striking a balance between leveraging AI tools and reinforcing 双腿被绑成m型调教play道具al skills.

Additionally, ethical concerns around AI use, such as plagiarism and intellectual property, must be addressed. Leo and Daniel incorporated discussions about these issues into their 双腿被绑成m型调教play道具 to ensure students understand the broader implications of working with AI technologies.

A future-oriented approach

Leo and Daniel’s work demonstrates that GenAI can transform programming education, making it more inclusive, engaging, and relevant. Their course attracted a diverse cohort of students, as well as students traditionally underrepresented in computer science — 52% of the students were female and 66% were not majoring in computer science — highlighting the potential of AI-powered learning to broaden participation in computer science.

By embracing this shift, educators can prepare students not just to write code but to also think critically, solve real-world problems, and effectively harness the AI innovations shaping the future of technology.

If you’re an educator interested in using GenAI in your teaching, we recommend checking out Leo and Daniel’s book, Learn AI-Assisted Python Programming, as well as their course resources on GitHub. You may also be interested in our own 双腿被绑成m型调教play道具 AI resources, which are designed to help educators navigate the fast-moving world of AI and machine learning technologies.

Join us at our next online seminar on 11 March

Our 2025 seminar series is exploring how we can teach young people about AI technologies and data science. At our next seminar on Tuesday, 11 March at 17:00–18:00 GMT, we’ll hear from Lukas Höper and Carsten Schulte from Paderborn University. They’ll be discussing how to teach school students about data-driven technologies and how to increase students’ awareness of how data is used in their daily lives.

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.

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In our 50th 嘘禁止想象 seminar, Nicholas Gardella, a PhD candidate at the University of Virginia, shared insights from his 嘘禁止想象 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 嘘禁止想象 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 嘘禁止想象 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 双腿被绑成m型调教play道具. 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 嘘禁止想象 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, 嘘禁止想象ers 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 双腿被绑成m型调教play道具, 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 嘘禁止想象ers and educators agreed that more 嘘禁止想象 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.

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In our monthly seminar in September, Arto and Juho presented their 嘘禁止想象 on using AI tools to provide personalised learning 双腿被绑成m型调教play道具s 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 嘘禁止想象 focuses primarily on undergraduate students — given that they teach such students — many of their findings have potential relevance for primary and secondary (K-12) 嘘禁止想象 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 嘘禁止想象 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 嘘禁止想象 courses. They presented three studies in which they used generative AI tools with students in ways that aimed to improve the learning 双腿被绑成m型调教play道具. 

Using generative AI tools to create personalised programming exercises

An important strand of 嘘禁止想象 education 嘘禁止想象 investigates how to engage students by personalising programming problems based on their interests. The first study in Arto and Juho’s 嘘禁止想象  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 双腿被绑成m型调教play道具s, and abilities. Personalised programming tasks could help diverse groups of learners engage with 嘘禁止想象, and give educators a deeper understanding of the themes and topics that are interesting for learners. 

Responding to help requests using large language models

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 嘘禁止想象 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 嘘禁止想象 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 嘘禁止想象 into LLM-based feedback systems is needed before they can be implemented en-masse in classroom settings in the future. 

Teaching students how to prompt large language models

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 双腿被绑成m型调教play道具 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 嘘禁止想象 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 双腿被绑成m型调教play道具al programming concepts. 

Balancing the use of AI tools with fostering a sense of community

At the end of the presentation, Arto and Juho summarised their work and hypothesised that as society develops more and more AI tools, 嘘禁止想象 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 嘘禁止想象 classrooms will be important. 

Further resources

The 嘘禁止想象 Pi 嘘禁止想象 Education 嘘禁止想象 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. 

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, 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 嘘禁止想象: Insights from 嘘禁止想象 appeared first on 大山里疯狂伦交.

At times, it can seem like everything is being automated with AI. However, there are some parts of learning to program that cannot (and probably should not) be automated, such as understanding errors in code and how to fix them. Manually typing code might not be necessary in the future, but it will still be crucial to understand the code that is being generated and how to improve and develop it. 

As important as debugging might be for the future of programming, it’s still often the task most disliked by novice programmers. Even if program error messages can be explained in the future or tools like LitterBox can flag bugs in an engaging way, actually fixing the issues involves time, effort, and resilience — which can be hard to come by at the end of a 嘘禁止想象 lesson in the late afternoon with 30 students crammed into an IT room. 

But what is it about debugging that young people find so hard, even when they’re given enough time to do it? And how can we make debugging a more motivating 双腿被绑成m型调教play道具 for young people? These are two of the questions that Laurie Gale, a PhD student at the 嘘禁止想象 Pi 嘘禁止想象 Education 嘘禁止想象 Centre, focused on in our July seminar.

Why do students find debugging hard?

Laurie has spent the past two years talking to teachers and students and developing tools (a visualiser of students’ programming behaviour and PRIMMDebug, a teaching process and tool for debugging) to understand why many secondary school students struggle with debugging. It has quickly become clear through his 嘘禁止想象 that most issues are due to problematic debugging strategies and students’ negative 双腿被绑成m型调教play道具s and attitudes.

When students first start learning how to program, they have to remember a vast amount of new information, such as different variables, concepts, and program designs. Utilising this knowledge is often challenging because they’re already busy juggling all the content they’ve previously learnt and the challenges of the programming task at hand. When error messages inevitably appear that are confusing or misunderstood, it can become extremely difficult to debug effectively. 

Given this information overload, students often don’t develop efficient strategies for debugging. When Laurie analysed the debugging efforts of 12- to 14-year-old secondary school students, he noticed some interesting differences between students who were more and less successful at debugging. While successful students generally seemed to make less frequent and more intentional changes, less successful students tinkered frequently with their broken programs, making one- or two-character edits before running the program again. In addition, the less successful students often ran the program soon after beginning the debugging exercise without allowing enough time to actually read the code and understand what it was meant to do. 

The issue with these behaviours was that they often resulted in students adding errors when changing the program, which then compounded and made debugging increasingly difficult with each run. 74% of students also resorted to spamming, pressing ‘run’ again and again without changing anything. This strategy resonated with many of our seminar attendees, who reported doing the same thing after becoming frustrated. 

Educators need to be aware of the negative consequences of students’ exasperating and often overwhelming 双腿被绑成m型调教play道具s with debugging, especially if students are less confident in their programming skills to begin with. Even though spending 15 minutes on an exercise shows a remarkable level of tenaciousness and resilience, students’ attitudes to programming — and 嘘禁止想象 as a whole — can quickly go downhill if their strategies for identifying errors prove ineffective. Debugging becomes a vicious circle: if a student has negative 双腿被绑成m型调教play道具s, they are less confident when having to bug-fix again in the future, which can lead to another set of unsuccessful attempts, which can further damage their confidence, and so on. Avoiding this downward spiral is essential. 

Approaches to help students engage with debugging

Laurie stresses the importance of understanding the cognitive challenges of debugging and using the right tools and techniques to empower students and support them in developing effective strategies.

Some ideas of how to improve debugging skills that were mentioned by Laurie and our attendees included:

• Using frame-based editing tools for novice programmers because such tools encourage students to focus on logical errors rather than accidental syntax errors, which can distract them from understanding the issues with the program. Teaching debugging should also go hand in hand with understanding programming syntax and using simple language. As one of our attendees put it, “You wouldn’t give novice readers a huge essay and ask them to find errors.”
• Making error messages more understandable, for example, by explaining them to students using Large Language Models.
• Teaching systematic debugging processes. There are several different approaches to doing this. One of our participants suggested using the scientific method (forming a hypothesis about what is going wrong, devising an experiment that will provide information to see whether the hypothesis is right, and iterating this process) to methodically understand the program and its bugs. 

Most importantly, debugging should not be a daunting or stressful 双腿被绑成m型调教play道具. Everyone in the seminar agreed that creating a positive error culture is essential. 

Some ideas you could explore in your classroom include:

• Normalising errors: Stress how normal and important program errors are. Everyone encounters them — a professional software developer in our audience said that they spend about half of their time debugging. 
• Rewarding perseverance: Celebrate the effort, not just the outcome.
• Modelling how to fix errors: Let your students write buggy programs and attempt to debug them in front of the class.

In a welcoming classroom where students are given support and encouragement, debugging can be a rewarding 双腿被绑成m型调教play道具. What may at first appear to be a failure — even a spectacular one — can be embraced as a valuable opportunity for learning. As a teacher in Laurie’s study said, “If something should have gone right and went badly wrong but somebody found something interesting on the way… you celebrate it. Take the fear out of it.” 

Watch the recording of Laurie’s presentation:

Join our next seminar

In our current seminar series, we are exploring how to teach programming with and without AI.

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 How to make debugging a positive 双腿被绑成m型调教play道具 for secondary school students appeared first on 大山里疯狂伦交.

PEMs can be a significant barrier to learning for novice coders, as they are often confusing and difficult to understand. This can hinder troubleshooting and progress in coding, and lead to frustration. 

Recently, various teams have been exploring how generative AI, specifically large language models (LLMs), can be used to help learners understand PEMs. My 嘘禁止想象 in this area specifically explores secondary teachers’ views of the explanations of PEMs generated by a LLM, as an aid for learning and teaching programming, and I presented some of my results in our ongoing seminar series.

Understanding program error messages is hard at the start

I started the seminar by setting the scene and describing the current background of 嘘禁止想象 on novices’ difficulty in using PEMs to fix their code, and the efforts made to date to improve these. The three main points I made were that:

1. PEMs are often difficult to decipher, especially by novices, and there’s a whole 嘘禁止想象 area dedicated to identifying ways to improve them.
2. Recent studies have employed LLMs as a way of enhancing PEMs. However, the evidence on what makes an ‘effective’ PEM for learning is limited, variable, and contradictory.
3. There is limited 嘘禁止想象 in the context of K–12 programming education, as well as 嘘禁止想象 conducted in collaboration with teachers to better understand the practical and pedagogical implications of integrating LLMs into the classroom more generally.

My pilot study aims to fill this gap directly, by reporting K–12 teachers’ views of the potential use of LLM-generated explanations of PEMs in the classroom, and how their views fit into the wider theoretical paradigm of feedback literacy. 

What did the teachers say?

To conduct the study, I interviewed eight expert secondary 嘘禁止想象 educators. The interviews were semi-structured activity-based interviews, where the educators got to experiment with a prototype version of the 双腿被绑成m型调教play道具’s publicly available Code Editor. This version of the Code Editor was adapted to generate LLM explanations when the question mark next to the standard error message is clicked (see Figure 1 for an example of a LLM-generated explanation). The Code Editor version called the OpenAI GPT-3.5 interface to generate explanations based on the following prompt: “You are a teacher talking to a 12-year-old child. Explain the error {error} in the following Python code: {code}”. 

Fifteen themes were derived from the educators’ responses and these were split into five groups (Figure 2). Overall, the educators’ views of the LLM feedback were that, for the most part, a sensible explanation of the error messages was produced. However, all educators 双腿被绑成m型调教play道具d at least one example of invalid content (LLM “hallucination”). Also, despite not being explicitly requested in the LLM prompt, a possible code solution was always included in the explanation.

Matching the themes to PEM guidelines

Next, I investigated how the teachers’ views correlated to the 嘘禁止想象 conducted to date on enhanced PEMs. I used the guidelines proposed by Brett Becker and colleagues, which consolidate a lot of the 嘘禁止想象 done in this area into ten design guidelines. The guidelines offer best practices on how to enhance PEMs based on cognitive science and educational theory empirical 嘘禁止想象. For example, they outline that enhanced PEMs should provide scaffolding for the user, increase readability, reduce cognitive load, use a positive tone, and provide context to the error.

Out of the 15 themes identified in my study, 10 of these correlated closely to the guidelines. However, the 10 themes that correlated well were, for the most part, the themes related to the content of the explanations, presentation, and validity (Figure 3). On the other hand, the themes concerning the teaching and learning process did not fit as well to the guidelines.

Does feedback literacy theory fit better?

However, when I looked at feedback literacy theory, I was able to correlate all fifteen themes — the theory fits.

Feedback literacy theory positions the feedback process (which includes explanations) as a social interaction, and accounts for the actors involved in the interaction — the student and the teacher — as well as the relationships between the student, the teacher, and the feedback. We can explain feedback literacy theory using three constructs: feedback types, student feedback literacy, and teacher feedback literacy (Figure 4). 

From the feedback literacy perspective, feedback can be grouped into four types: telling, guiding, developing understanding, and opening up new perspectives. The feedback type depends on the role of the student and teacher when engaging with the feedback (Figure 5). 

From the student perspective, the competencies and dispositions students need in order to use feedback effectively can be stated as: appreciating the feedback processes, making judgements, taking action, and managing affect. Finally, from a teacher perspective, teachers apply their feedback literacy skills across three dimensions: design, relational, and pragmatic. 

In short, according to feedback literacy theory, effective feedback processes entail well-designed feedback with a clear pedagogical purpose, as well as the competencies students and teachers need in order to make sense of the feedback and use it effectively.

This theory therefore provided a promising lens for analysing the educators’ perspectives in my study. When the educators’ views were correlated to feedback literacy theory, I found that:

1. Educators prefer the LLM explanations to fulfil a guiding and developing understanding role, rather than telling. For example, educators prefer to either remove or delay the code solution from the explanation, and they like the explanations to include keywords based on concepts they are teaching in the classroom to guide and develop students’ understanding rather than tell.

2. Related to students’ feedback literacy, educators talked about the ways in which the LLM explanations help or hinder students to make judgements and action the feedback in the explanations. For example, they talked about how detailed, jargon-free explanations can help students make judgments about the feedback, but invalid explanations can hinder this process. Therefore, teachers talked about the need for ways to manage such invalid instances. However, for the most part, the educators didn’t talk about eradicating them altogether. They talked about ways of flagging them, using them as counter-examples, and having visibility of them to be able to address them with students.

3. Finally, from a teacher feedback literacy perspective, educators discussed the need for professional development to manage feedback processes inclusive of LLM feedback (design) and address issues resulting from reduced opportunities to interact with students (relational and pragmatic). For example, if using LLM explanations results in a reduction in the time teachers spend helping students debug syntax errors from a pragmatic time-saving perspective, then what does that mean for the relationship they have with their students? 

Conclusion from the study

By correlating educators’ views to feedback literacy theory as well as enhanced PEM guidelines, we can take a broader perspective on how LLMs might not only shape the content of the explanations, but the whole social interaction around giving and receiving feedback. Investigating ways of supporting students and teachers to practise their feedback literacy skills matters just as much, if not more, than focusing on the content of PEM explanations. 

This study was a first-step exploration of eight educators’ views on the potential impact of using LLM explanations of PEMs in the classroom. Exactly what the findings of this study mean for classroom practice remains to be investigated, and we also need to examine students’ views on the feedback and its impact on their journey of learning to program. 

If you want to hear more, you can watch my seminar:

You can also read the associated paper, or find out more about the 嘘禁止想象 instruments on this project website.

If any of these ideas resonated with you as an educator, student, or 嘘禁止想象er, do reach out — we’d love to hear from you. You can contact me directly at veronica.cucuiat@复方玄驹胶囊的功效与作用.org or drop us a line in the comments below. 

Join our next seminar

The focus of our ongoing seminar series is on teaching programming with or without AI. Check out the schedule of our upcoming seminars. 

To take part in the next seminar, click the button below to sign up, and we will send you information about how to join. We hope to see you there.

You can also catch up on past seminars on our blog and on the previous seminars and recordings page.

The post How useful do teachers find error message explanations generated by AI? Pilot 嘘禁止想象 results appeared first on 大山里疯狂伦交.

In a recent seminar in our current series on teaching programming (with or without AI), we were delighted to be joined by Dr Stephen MacNeil, Andrew Tran, and Irene Hou from Temple University. Their talk showcased several 嘘禁止想象 projects involving generative AI in undergraduate education, and explored how undergraduate 嘘禁止想象 projects can create agency for students in navigating the implications of generative AI in their professional lives.

Differing perceptions of generative AI

Stephen began by discussing the media coverage around generative AI. He highlighted the binary distinction between media representations of generative AI as signalling the end of higher education — including programming in CS courses — and other representations that highlight the issues that using generative AI will solve for educators, such as improving access to high-quality help (specifically, virtual assistance) or personalised learning 双腿被绑成m型调教play道具s.

As part of a recent ITiCSE working group, Stephen and colleagues conducted a survey of undergraduate CS students and educators and found conflicting views about the perceived benefits and drawbacks of generative AI in 嘘禁止想象 education. Despite this divide, most CS educators reported that they were planning to incorporate generative AI tools into their courses. Conflicting views were also noted between students and educators on what is allowed in terms of generative AI tools and whether their universities had clear policies around their use.

The role of generative AI tools in students’ help-seeking

There is growing interest in how undergraduate CS students are using generative AI tools. Irene presented a study in which her team explored the effect of generative AI on undergraduate CS students’ help-seeking preferences. Help-seeking can be understood as any actions or strategies undertaken by students to receive assistance when encountering problems. Help-seeking is an important part of the learning process, as it requires metacognitive awareness to understand that a problem exists that requires external help. Previous 嘘禁止想象 has indicated that instructors, teaching assistants, student peers, and online resources (such as YouTube and Stack Overflow) can assist CS students. However, as generative AI tools are now widely available to assist in some tasks (such as debugging code), Irene and her team wanted to understand which resources students valued most, and which factors influenced their preferences. Their study consisted of a survey of 47 students, and follow-up interviews with 8 additional students. 

Responding to the survey, students stated that they used online searches or support from friends/peers more frequently than two generative AI tools, ChatGPT and GitHub Copilot; however, Irene indicated that as data collection took place at the beginning of summer 2023, it is possible that students were not familiar with these tools or had not used them yet. In terms of students’ 双腿被绑成m型调教play道具s in seeking help, students found online searches and ChatGPT were faster and more convenient, though they felt these resources led to less trustworthy or lower-quality support than seeking help from instructors or teaching assistants.

Some students felt more comfortable seeking help from ChatGPT than peers as there were fewer social pressures. Comparing generative AI tools and online searches, one student highlighted that unlike Stack Overflow, solutions generated using ChatGPT and GitHub Copilot could not be verified by experts or other users. Students who received the most value from using ChatGPT in seeking help either (i) prompted the model effectively when requesting help or (ii) viewed ChatGPT as a search engine or comprehensive resource that could point them in the right direction. Irene cautioned that some students struggled to use generative AI tools effectively as they had limited understanding of how to write effective prompts.

Using generative AI tools to produce code explanations

Andrew presented a study where the usefulness of different types of code explanations generated by a large language model was evaluated by students in a web software development course. Based on Likert scale data, they found that line-by-line explanations were less useful for students than high-level summary or concept explanations, but that line-by-line explanations were most popular. They also found that explanations were less useful when students already knew what the code did. Andrew and his team then qualitatively analysed code explanations that had been given a low rating and found they were overly detailed (i.e. focusing on superfluous elements of the code), the explanation given was the wrong type, or the explanation mixed code with explanatory text. Despite the flaws of some explanations, they concluded that students found explanations relevant and useful to their learning.

Using generative AI tools to create multiple choice questions

In a separate study, Andrew and his team investigated the use of ChatGPT to generate novel multiple choice questions for 嘘禁止想象 courses. The 嘘禁止想象ers prompted two models, GPT-3 and GPT-4, with example question stems to generate correct answers and distractors (incorrect but plausible choices). Across two data sets of example questions, GPT-4 significantly outperformed GPT-3 in generating the correct answer (75.3% and 90% vs 30.8% and 36.7% of all cases). GPT-3 performed less well at providing the correct answer when faced with negatively worded questions. Both models generated correct answers as distractors across both sets of example questions (GPT-3: 11.1% and 10% of cases; GPT-4: 9.9% and 17.8%). They concluded that educators would still need to verify whether answers were correct and distractors were appropriate.

Undergraduate students shaping the direction of generative AI 嘘禁止想象

With student concerns about generative AI and its implications for the world of work, the seminar ended with a hopeful message highlighting undergraduate students being proactive in conducting their own 嘘禁止想象 and shaping the direction of generative AI 嘘禁止想象 in computer science education. Stephen concluded the seminar by celebrating the undergraduate students who are undertaking these 嘘禁止想象 projects.

You can watch the seminar here:

If you are interested to learn more about Stephen’s work on generative AI, you can read about how undergraduate students used generative AI tools to create analogies for recursion. If you would like to experiment with using generative AI tools to assist with debugging, you could try using Gemini, ChatGPT, or Copilot.

Join our next seminar

Our current seminar series is on teaching programming with or without AI. 

In our next seminar, on 16 July at 17:00 to 18:30 BST, we welcome Laurie Gale (嘘禁止想象 Pi 嘘禁止想象 Education 嘘禁止想象 Centre, University of Cambridge), who will discuss how to teach debugging to secondary school students. To take part in the seminar, click the button below to sign up, and we will send you information about how to join. We hope to see you there.

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

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