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Generative AI Practicals - King's Academy 

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Generative AI Practicals - King's Academy 

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Videos from KCL on practical uses of AI in HE

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There are loads of things we (in HE and education more broadly) need to think about and do when it comes to generative AI, both cognitively and practically.…

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Despite the buzz surrounding it, the prominence of prompt engineering may be fleeting. A more enduring and adaptable skill will keep enabling us to harness the potential of generative AI? It is called problem formulation - the ability to identify, analyze, and delineate problems.

Prompt engineering has taken the generative AI world by storm. The job, which entails optimizing textual input to effectively communicate with large language models, has been hailed by World Economic Forum as the number one "job of the future" while Open AI CEO Sam Altman characterized it as an "amazingly high-leveraged skill." Social media brims with a new wave of influencers showcasing "magic prompts" and pledging amazing outcomes.

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Despite the buzz surrounding it, the prominence of prompt engineering may be fleeting. A more enduring and adaptable skill will keep enabling us to harness the potential of generative AI? It is called problem formulation - the ability to identify, analyze, and delineate problems.

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In 2022, Pratyusha Ria Kalluri, a graduate student in artificial intelligence (AI) at Stanford University in California, found something alarming in image-generating AI programs. When she prompted a popular tool for 'a photo of an American man and his house', it generated an image of a pale-skinned person in front of a large, colonial-style home. When she asked for 'a photo of an African man and his fancy house', it produced an image of a dark-skinned person in front of a simple mud house - despite the word 'fancy'.

After some digging, Kalluri and her colleagues found that images generated by the popular tools Stable Diffusion, released by the firm Stability AI, and DALL·E, from OpenAI, overwhelmingly resorted to common stereotypes, such as associating the word 'Africa' with poverty, or 'poor' with dark skin tones. The tools they studied even amplified some biases. For example, in images generated from prompts asking for photos of people with certain jobs, the tools portrayed almost all housekeepers as people of colour and all flight attendants as women, and in proportions that are much greater than the demographic reality (see 'Amplified stereotypes'). Other researchers have found similar biases across the board: text-to-image generative AI models often produce images that include biased and stereotypical traits related to gender, skin colour, occupations, nationalities and more.

Perhaps this is unsurprising, given that society is full of such stereotypes. Studies have shown that images used by media outlets, global health organizations and Internet databases such as Wikipediaoften have biased representations of gender and race. AI models are being trained on online pictures that are not only biased but that also sometimes contain illegal or problematic imagery, such as photographs of child abuse or non-consensual nudity. They shape what the AI creates: in some cases, the images created by image generators are even less diverse than the results of a Google image search, says Kalluri. "I think lots of people should find that very striking and concerning."

This problem matters, researchers say, because the increasing use of AI to generate images will further exacerbate stereotypes. Although some users are generating AI images for fun, others are using them to populate websites or medical pamphlets. Critics say that this issue should be tackled now, before AI becomes entrenched. Plenty of reports, including the 2022 Recommendation on the Ethics of Artificial Intelligence from the United Nations cultural organization UNESCO, highlight bias as a leading concern.

Some researchers are focused on teaching people how to use these tools better, or on working out ways to improve curation of the training data. But the field is rife with difficulty, including uncertainty about what the 'right' outcome should be. The most important step, researchers say, is to open up AI systems so that people can see what's going on under the hood, where the biases arise and how best to squash them. "We need to push for open sourcing. If a lot of the data sets are not open source, we don't even know what problems exist," says Abeba Birhane, a cognitive scientist at the Mozilla Foundation in Dublin.

Make me a picture

Image generators first appeared in 2015, when researchers built alignDRAW, an AI model that could generate blurry images based on text input. It was trained on a data set containing around 83,000 images with captions. Today, a swathe of image generators of varying abilities are trained on data sets containing billions of images. Most tools are proprietary, and the details of which images are fed into these systems are often kept under wraps, along with exactly how they work.

In general, these generators learn to connect attributes such as colour, shape or style to various descriptors. When a user enters a prompt, the generator builds new visual depictions on the basis of attributes that are close to those words. The results can be both surprisingly realistic and, often, strangely flawed (hands sometimes have six fingers, for example).

The captions on these training images - written by humans or automatically generated, either when they are first uploaded to the Internet or when data sets are put together - are crucial to this process. But this information is often incomplete, selective and thus biased itself. A yellow banana, for example, would probably be labelled simply as 'a banana', but a description for a pink banana would be likely to include the colour. "The same thing happens with skin colour. White skin is considered the default so it isn't typically mentioned," says Kathleen Fraser, an AI research scientist at the National Research Council in Ottawa, Canada. "So the AI models learn, incorrectly in this case, that when we use the phrase 'skin colour' in our prompts, we want dark skin colours," says Fraser.

The difficulty with these AI systems is that they can't just leave out ambiguous or problematic details in their generated images. "If you ask for a doctor, they can't leave out the skin tone," says Kalluri. And if a user asks for a picture of a kind person, the AI system has to visualize that somehow. "How they fill in the blanks leaves a lot of room for bias to creep in," she says. This is a problem that is unique to image generation - by contrast, an AI text generator could create a language-based description of a doctor without ever mentioning gender or race, for instance; and for a language translator, the input text would be sufficient.

Do it yourself

One commonly proposed approach to generating diverse images is to write better prompts. For instance, a 2022 study found that adding the phrase "if all individuals can be [X], irrespective of gender" to a prompt helps to reduce gender bias in the images produced.

But this doesn't always work as intended. A 2023 study by Fraser and her colleagues found that such intervention sometimes exacerbated biases. Adding the phrase "if all individuals can be felons irrespective of skin colour", for example, shifted the results from mostly dark-skinned people to all dark-skinned people. Even explicit counter-prompts can have unintended effects: adding the word 'white' to a prompt for 'a poor person', for example, sometimes resulted in images in which commonly associated features of whiteness, such as blue eyes, were added to dark-skinned faces.

Another common fix is for users to direct results by feeding in a handful of images that are more similar to what they're looking for. The generative AI program Midjourney, for instance, allows users to add image URLs in the prompt. "But it really feels like every time institutions do this they are really playing whack-a-mole," says Kalluri. "They are responding to one very specific kind of image that people want to have produced and not really confronting the underlying problem."

These solutions also unfairly put the onus on the users, says Kalluri, especially those who are under-represented in the data sets. Furthermore, plenty of users might not be thinking about bias, and are unlikely to pay to run multiple queries to get more-diverse imagery. "If you don't see any diversity in the generated images, there's no financial incentive to run it again," says Fraser.

Some companies say they add something to their algorithms to help counteract bias without user intervention: OpenAI, for example, says that DALL·E2 uses a "new technique" to create more diversity from prompts that do not specify race or gender. But it's unclear how such systems work and they, too, could have unintended impacts. In early February, Google released an image generator that had been tuned to avoid some typical image-generator pitfalls. A media frenzy ensued when user prompts requesting a picture of a '1943 German soldier' created images of Black and Asian Nazis - a diverse but historically inaccurate result. Google acknowledged the mistake and temporarily stopped its generator creating images of people.

Data clean-up

Alongside such efforts lie attempts to improve curation of training data sets, which is time-consuming and expensive for those containing billions of images. That means companies resort to automated filtering mechanisms to remove unwanted data.

However, automated filtering based on keywords doesn't catch everything. Researchers including Birhane have found, for example, that benign keywords such as 'daughter' and 'nun' have been used to tag sexually explicit images in some cases, and that images of schoolgirls are sometimes tagged with terms searched for by sexual predators. And filtering, too, can have unintended effects. For example, automated attempts to clean large, text-based data sets have removed a disproportionate amount of content created by and for individuals from minority groups. And OpenAI discovered that its broad filters for sexual and violent imagery in DALL·E2 had the unintended effect of creating a bias against the generation of images of women, because women were disproportionately represented in those images.

The best curation "requires human involvement", says Birhane. But that's slow and expensive, and looking at many such images takes a deep emotional toll, as she well knows. "Sometimes it just gets too much."

Independent evaluations of the curation process are impeded by the fact that these data sets are often proprietary. To help overcome this problem, LAION, a non-profit organization in Hamburg, Germany, has created publicly available machine-learning models and data sets that link to images and their captions, in an attempt to replicate what goes on behind the closed doors of AI companies. "What they are doing by putting together the LAION data sets is giving us a glimpse into what data sets inside big corporations and companies like OpenAI look like," says Birhane. Although intended for research use, these data sets have been used to train models such as Stable Diffusion.

Researchers have learnt from interrogating LAION data that bigger isn't always better. AI researchers often assume that the bigger the training data set, the more likely that biases will disappear, says Birhane. "People often claim that scale cancels out noise," she says. "In fact, the good and the bad don't balance out." In a 2023 study, Birhane and her team compared the data set LAION-400M, which has 400 million image links, with LAION-2B-en, which has 2 billion, and found that hate content in the captions increased by around 12% in the larger data set, probably because more low-quality data had slipped through.

An investigation by another group found that the LAION-5B data set contained child sexual abuse material. Following this, LAION took down the data sets. A spokesperson for LAION told Nature that it is working with the UK charity Internet Watch Foundation and the Canadian Centre for Child Protection in Winnipeg to identify and remove links to illegal materials before it republishes the data sets.

Open or shut

If LAION is bearing the brunt of some bad press, that's perhaps because it's one of the few open data sources. "We still don't know a lot about the data sets that are created within these corporate companies," says Will Orr, who studies cultural practices of data production at the University of Southern California in Los Angeles. "They say that it's to do with this being proprietary knowledge, but it's also a way to distance themselves from accountability."

In response to Nature's questions about which measures are in place to remove harmful or biased content from DALL·E's training data set, OpenAI pointed to publicly available reports that outline its work to reduce gender and racial bias, without providing exact details on how that's accomplished. Stability AI and Midjourney did not respond to Nature 's e-mails.

Orr interviewed some data set creators from technology companies, universities and non-profit organizations, including LAION, to understand their motivations and the constraints. "Some of these creators had feelings that they were not able to present all the limitations of the data sets," he says, because that might be perceived as critical weaknesses that undermine the value of their work.

Specialists feel that the field still lacks standardized practices for annotating their work, which would help to make it more open to scrutiny and investigation. "The machine-learning community has not historically had a culture of adequate documentation or logging," says Deborah Raji, a Mozilla Foundation fellow and computer scientist at the University of California, Berkeley. In 2018, AI ethics researcher Timnit Gebru - a strong proponent of responsible AI and co-founder of the community group Black in AI - and her team released a datasheet to standardize the documentation process for machine-learning data sets. The datasheet has more than 50 questions to guide documentation about the content, collection process, filtering, intended uses and more.

The datasheet "was a really critical intervention", says Raji. Although many academics are increasingly adopting such documentation practices, there's no incentive for companies to be open about their data sets. Only regulations can mandate this, says Birhane.

One example is the European Union's AI Act, which was endorsed by the European Parliament on 13 March. Once it becomes law, it will require that developers of high-risk AI systems provide technical documentation, including datasheets describing the training data and techniques, as well as details about the expected output quality and potential discriminatory impacts, among other information. But which models will come under the high-risk classification remains unclear. If passed, the act will be the first comprehensive regulation for AI technology and will shape how other countries think about AI laws.

Specialists such as Birhane, Fraser and others think that explicit and well-informed regulations will push companies to be more cognizant of how they build and release AI tools. "A lot of the policy focus for image-generation work has been oriented around minimizing misinformation, misrepresentation and fraud through the use of these images, and there has been very little, if any, focus on bias, functionality or performance," says Raji.

Even with a focus on bias, however, there's still the question of what the ideal output of AI should be, researchers say - a social question with no simple answer. "There is not necessarily agreement on what the so-called right answer should look like," says Fraser. Do we want our AI systems to reflect reality, even if the reality is unfair? Or should it represent characteristics such as gender and race in an even-handed, 50:50 way? "Someone has to decide what that distribution should be," she says.
doi: https://doi.org/10.1038/d41586-024-00674-9

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AI image generators often give racist and sexist results: can they be fixed?

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Researchers are tracing sources of racial and gender bias in images generated by artificial intelligence, and making efforts to fix them. Researchers are tracing sources of racial and gender bias in images generated by artificial intelligence, and making efforts to fix them.

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Student Perspectives on  generative AI @UCL -  how are students seeing it and using it?

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Updating Marking Rubrics to acknowledge use of Generative AI - Thread 

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Updating Marking Rubrics to acknowledge use of Generative AI - Thread 

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Michelle Kassorla, Ph.D. on LinkedIn: #rubric #writing #ai #gai #gai | 94 comments

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OMGDNESS!! I think I just did something amazing. I was making a #rubric for a #writing assignment, and this popped out: CRITERIA: Effectiveness in using #AI… | 94 comments on LinkedIn

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Academic integrity and academics' literacy: Student Perspectives on Generative AI @UCL

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Russell Group statement: Principles on the use of generative AI in education

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Russell Group statement: Principles on the use of generative AI in education

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A new set of principles has been created to help universities ensure students and staff are 'AI literate' so they can capitalise on the opportunities technological breakthroughs provide for teaching and learning.

The statement, published today (4 July) and backed by the 24 Vice Chancellors of the Russell Group, will shape institution and course-level work to support the ethical and responsible use of generative AI, new technology and software like ChatGPT.

Developed in partnership with AI and educational experts, the new principles recognise the risks and opportunities of generative AI and commit Russell Group universities to helping staff and students become leaders in an increasingly AI-enabled world.

The five principles set out in today's joint statement are:

Universities will support students and staff to become AI-literate.

Staff should be equipped to support students to use generative AI tools effectively and appropriately in their learning experience.

Universities will adapt teaching and assessment to incorporate the ethical use of generative AI and support equal access.

Universities will ensure academic rigour and integrity is upheld.

Universities will work collaboratively to share best practice as the technology and its application in education evolves.

The statement of principles comes shortly after the UK Government launched a consultation on the use of generative AI in education in England.

Commenting, Chief Executive of the Russell Group Dr Tim Bradshaw said:

"AI breakthroughs are already changing the way we work and its crucial students get the new skills they need to build a fulfilling career. University staff also need support as they look at how AI can be used to enhance their teaching and help bring subjects to life.

"This is a rapidly developing field, and the risks and opportunities of these technologies are changing constantly. It's in everyone's interests that AI choices in education are taken on the basis of clearly understood values.

"The transformative opportunity provided by AI is huge and our universities are determined to grasp it. This statement of principles underlines our commitment to doing so in a way that benefits students and staff and protects the integrity of the high-quality education Russell Group universities provide."

Read the principles in full below

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New principles on use of AI in education

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A new set of principles has been created to help universities ensure students and staff are 'AI literate' so they can capitalise on the opportunities technological breakthroughs provide for teaching and learning.

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Writing is still a primary way we assess learning at UCL. However, writing is also a way of developing understanding and expertise in a subject area: we write to learn, not just to demonstrate learning.

Use of generative AI for parts of the writing process has recently been deemed acceptable in some university policies, including at UCL, but how do students feel about using these tools? And how can students' experiences help staff to talk to students about using AI tools appropriately for the writing process?

Aims of the project

As part of a Changemakers Co-Creators AI project in summer 2023, we recruited three UCL students from different discipline areas (BSc Bioscience, BSc Linguistics, MSc International Planning) to explore what was gained and what was lost in the reading-into-writing process when using AI tools as reading and writing assistants.

We wanted to investigate:

how useful AI tools were

the impact of AI tools on the learning process

students' feelings about AI tools throughout

These findings and recommendations can support educators in discussing the appropriate use of AI in student written assignments, in the context of their discipline and learning objectives, alongside the toolkit on using generative AI in teaching and learning.

Our process

Students first described their usual reading-into-writing process (i.e. without use of AI tools) and reflected on what they find challenging and what they find enjoyable. We broke down the writing process into stages to give students a common vocabulary to describe their process.

The writing process in three stages

Reading, pre-writing and planning: reading; generating ideas, understanding the ideas of others, collecting information (note-taking, freewriting, brainstorming); organising and focusing ideas (mind mapping, clustering, listing, outline)

Drafting and review: writing versions of a text focusing mainly on the development, organisation and elaboration of ideas

Editing and proofreading: focusing attention on the surface-level features of the text

This not a linear process, and as we write we might go back and forth between these different stages.

(These stages are taken from a diagram in Curry, M.J. & Hewings, A. (2003). Approaches to teaching writing. In: Coffin, C., Curry, M.J., Goodman, S., Hewings, A., Lillis, T.M. & Swann, J. (eds.) Teaching Academic Writing: A Toolkit for Higher Education. Routledge, pp. 19-44. See original diagram on p.34)

Students selected an assignment set by their programme, which they had not completed previously and for which the deadline had already passed. They reflected on the intended learning outcomes of the task, why they thought it had been set, and what they might hope to learn through completing the assignment.

Students chose from a range of free and paid-for AI tools (ChatPDF, Humata, SciSpace, ChatGPT, Quillbot, Jenni.ai, Writesonic, GrammarlyGo, Claude.ai) to assist in writing the assignment. They had three weeks to complete their assignments, recording their thoughts and reflections on the process in a daily diary.

On completing the assignments, the students were interviewed to find out how their non-AI and AI-assisted writing processes differed, what they felt they had gained and lost in using these tools and how they now felt about using AI tools. Some common themes and key recommendations for staff and students emerged from these discussions.

Findings

Students' feelings about AI tools

Annoying, ambivalent, disappointed

When asked to sum up their overall feelings towards using AI tools for the reading-into-writing process, the students chose annoying, ambivalent, and disappointed.

Fundamentally, they thought that these tools negatively affected their university learning process:

"AI won't give you that depth that we require for a first. It won't give you that knowledge that you require in your later degrees and professional life. The student might ask why am I at university? Do I really want to learn? (Student 1)

[AI tools] don't understand the purpose of the assignment... which is not to write a perfect essay, but to learn and think independently. (Student 2)

How do I use these tools without outsourcing the entire project to them? How do I use them effectively, while maintaining my own voice? Am I getting dumber whilst using them? (Student 3)

The students felt that reliance on AI tools led to superficial learning and missed learning opportunities. They reflected that tutors had set the various assignments in order to develop skills and deepen understanding, so bypassing these processes missed the point.

Time spent and distrust in the output

The students also found the process of selecting and familiarising themselves with the various tools time consuming, as was developing the skill of appropriate prompt writing. Although students recognized that the tools could save time, particularly in the early stages of the reading-into-writing process, these benefits were in part negated by a lack of trust in the tools' output.

Texts often had to be read in full to check AI output for accuracy as the tools could not 'understand' the texts in relation to the task set with sufficient depth and nuance. The students also felt that these tools could not represent a writer's argument or view accurately enough to be relied upon.

Prompting a new perspective

One student had a more positive view of AI tools in terms of the broader applications for university learning, including outside of assessment. The student felt cautiously optimistic about the possible role of AI in tailoring learning for individual students, improving engagement and accessibility for neurodiverse groups by disrupting traditional modes of instruction.

"There's potential to use it elsewhere, in terms of how I learn or to suggest different ways in which I can approach this task. I would be more keen on using it for those things than something which is strictly academic. (Student 3)

AI for the reading-into-writing stages

Reading, pre-writing and planning

Students recognized that the tools could save time in the reading, pre-writing and planning stages. They found the tools useful for:

summarizing papers

questioning a text

locating information within a text

overcoming writer's block

suggesting a possible outline as a starting point.

However, the negatives at this early stage outweighed the positives:

"By cutting down the meaningful time that you directly engage with your readings, it's impossible to get to the level of understanding that you would get to without using these systems... with traditional reading you'll be led to unexpected places and that's one of the main joys of learning... (Student 1)

Reading the original articles in full can serve as a model for my own writing, but these AI tools discourage this practice... I lost the ability to understand the article as a whole. (Student 2)

I would want to engage with that literature and come up with my own conclusions or observations. (Student 3)

The students recognized that:

Reading widely and deeply for an assignment was necessary for acquiring sufficient subject knowledge, as well as exposing them to useful models of texts and raising their awareness of genre.

Using these tools became a substitute for their own reading process, shifting their role from that of active reader to editor and fact-checker.

There is a paradoxical need for a base level of subject knowledge in order to use the tools (create relevant and appropriate prompts; evaluate the AI-generated output) to complete their assignments, knowledge which could only be gained through their own reading.

Using these tools means they missed an opportunity to develop key skills of criticality and autonomy.

Drafting and reviewing

Students identified some benefits at the drafting stage. AI-generated text provided a grammatically accurate and fluent text which could be used as a model:

"The AI tools also produced 'perfect writing in pieces' i.e., no grammar mistakes and were useful at summarising and paraphrasing. After using AI, I understood what the shortages of AI were, but I also understood which parts of the reading into writing process that I had shortages in myself. AI writing tools also helped me become aware of my grammar mistakes. (Student 2)

Prompting AI tools to express an argument in different ways could serve as inspiration for variation in writing style. AI tools also made helpful suggestions for summary and paraphrase. However, as generative AI becomes ever more sophisticated in the generation of suggested text and embedded in the apps we use every day, a lack of clarity about where the boundary lies between appropriate and inappropriate use will continue to raise authorship concerns for both students and educators.

These potential benefits aside, students discovered various issues with AI-generated text:

The output could be superficial, misleading or irrelevant with missing or inaccurate references.

Text often followed a generic and non-committal pattern of argumentation, lacking nuance and 'stance' and the students thought the rhetorical style tended to be bland.

AI tools fell short when producing coherent longer texts. As a result,the output required extensive and time-consuming post-editing for flow, signposting, and transition.

Editing and proofreading

As noted in the previous sections, the use of generative AI for students' assignments resulted in extensive post-editing variously for content, argument, referencing, cohesion and coherence.

The use of AI tools created an altered, perhaps greater, demand on the role of editor in the reading-into-writing process in comparison to 'traditional' writing:

"Within paragraphs, their output does not always follow a clear and logical order, which will lead to even more work if you want to edit it into a coherent whole. (Student 1)

The answers Humata writes can be used as notes for my essay ... However, [they] need to be edited, but how to decide which to keep and which to delete is challenging because I must read the whole reference myself. (Student 2)

All students found AI tools useful for 'polishing' writing (e.g. improving grammar and spelling) and did not note any major disadvantages for this stage of the writing process, suggesting that this use is less problematic. However, issues of authorship and student learning are also relevant at the editing and proofreading stage, as with human editors and proofreaders.

Recommendations

In discussion with our students, we developed the recommendations below to facilitate discussions around appropriate use of AI tools in the reading-into-writing process.

Considerations of varying student access to these tools, as well as the ethical and data privacy issues relating to use of generative AI, may take high priority as part of an initial discussion.

How to talk to students about using AI tools appropriately for the writing process

Prompt students to identify the intended learning outcomes of assignments by reviewing assignment guidelines and assessment criteria.

Discuss with them how these outcomes relate to the different stages of the writing process.

Discuss with students whether outsourcing aspects of the process to AI tools might help or hinder their learning, in relation to the learning outcomes.

Create an environment in which students can discuss their honest feelings about using generative AI. Share the student reflections in this case study with your students to raise their awareness.

Whatever uses of AI tools you decide are (in)appropriate, be specific about what that means for the different stages of the reading-into-writing process. For example, is it ok to use these tools for brainstorming and polishing text, but not for summarizing reading?

Create time and space for students - and yourself! - to experiment with AI tools for different stages of the reading-into-writing process. This will help students gain confidence about the capabilities and limitations of using AI tools as reading and writing assistants.

Return to top

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Using AI tools for the reading-into-writing process

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Peter Puxon, Jessica Brook and Ayanna Prevatt-Goldstein from the UCL Academic Communication Centre worked with three students to reflect on writing an assignment with the assistance of AI tools.

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Artificial Intelligence, or AI, refers broadly to any technology or system that can reason and/or adapt, sometimes on its own, to achieve certain goals. For example, predictive text on a smartphone guesses what you will write next, or a robot that can analyse what's around it and then make decisions to act within its environment autonomously.

AI-based systems can be based purely in the virtual world with things like voice assistants (e.g. Siri or Google Home), image analysis tools, search engines, and technology that can recognise speech and faces.

AI can also be embedded in hardware, or objects in the physical world around us such as advanced robots, self-driving cars, drones, or devices connected to the Internet of Things.

Within AI, generative AI refers specifically to a type of AI where applications are trained to learn from data to improve at performing certain tasks such that it can create content like text, code, images, video and audio based on the vast amounts of data that the AI has been trained on. Generative AI (exemplified here but nor endorsed by King's) includes:

Text generation

Microsoft Bing Chat, ChatGPT, Google Bard, Claude. These are AI systems that can generate human-like text by predicting probable next words after being given a prompt. Writing prompts accurately and in order to fine tune outputs is referred to as 'prompt engineering'.

These tools are NOT databases of knowledge. They 'predict' combinations of plausible words; they should never be used instead of, but may be used alongside, conventional approaches to sourcing information.

Please note that Microsoft Bing Chat is available to all King's students with your KCL Microsoft log in credentials. Make sure you are logged into your KCL account and switch 'safe search' to 'moderate' to use it.

Other media and code generation

DALL-E, Midjourney and Stable Diffusion are examples of AI systems that create images from text prompts by analysing vast datasets of image-text pairs and producing novel images.

Descript and Murf.ai are examples of AI tools that can generate human-like audio narration and conversation from text.

GitHub Copilot and TabNine are examples of AI coding assistants that suggest completions for code based on analysing large codebases. These are but a few of the thousands of tools available.

Please note that there are ongoing controversies and challenges across the globe about use of text and images generated by AI, with many arguing that these breach copyright rules.

No, King's does not ban the use of any type of AI. It is increasingly part of the wider world and is changing the nature of many aspects of life including the jobs you are in or will progress into. At King's we are a signatory to the Russell Group principles:

Universities will support students and staff to become AI-literate.

Staff should be equipped to support students to use generative AI tools effectively and appropriately in their learning experience.

Universities will adapt teaching and assessment to incorporate the ethical use of generative AI and support equal access.

Universities will ensure academic rigour and integrity is upheld.

Universities will work collaboratively to share best practice as the technology and its application in education evolves.

We do, of course, expect students to critically engage with ideas and content produced from or with generative AI, to own outputs and to adhere to academic integrity policies to ensure appropriate and ethical use of AI.

Above all, any work submitted must represent a genuine demonstration of your own work, skills and subject knowledge, adhere to the guidelines of the assessment task, and respects the university's value of academic integrity and honesty.

Many of the tools we are already familiar with have built in AI elements and this will increase quite rapidly over the forthcoming months and years.

The key distinction of generative AI from e.g. a search engine output is its ability to create completely novel content at scale, as opposed to just accessing existing data. However, where outputs are fully AI-generated, they lack human understanding, critical engagement and precision and often exhibit a bland style of expression that, whilst technically accurate and impressive at first glance, is often flawed. The tools may generate biased, incorrect, or misleading information, and it is good practice to check information via other sources.

Generative AI can be extremely useful if used appropriately, but it's important to be aware of its limitations compared to human creativity and cognition. Used properly and strategically, generative AI can augment our creativity and productivity. The key is developing skills to harness it effectively as part of your own learning process, while maintaining full academic integrity through proper attribution and transparency.

Used properly, generative AI tools can:

Help generate ideas and frameworks for study and assessments

Upscale and organise rough typed notes

Provide explanations of concepts you are struggling to understand through dialogic exchanges

Paraphrase and summarise sources as a study aid

Provide feedback on your ideas and work, and help you improve it

Re-format text (e.g. from full prose to tables)

Suggest code completions or assist with debugging for programming work

Translate texts between languages for multilingual research or for communicating with researchers across the globe

Summarise meeting, video or podcast transcripts

'Clean' and punctuate automated transcripts (e.g. those produced in YouTube)

Help you create effective presentations

Help challenge some of the issues in study experienced by neurodivergent students.

It is absolutely essential that you are clear about any particular requirements or restrictions at programme level as these can vary, even across modules. For most of our students you will not be required or even recommended to use these tools if you do not want to. Maintaining academic integrity requires human direction over how generative AI is leveraged in the learning process. It is unlikely that copying and pasting from a generative AI tool will likely ever be appropriate practice in a summative assessment.

Please also note you should exercise caution when using any tools that require additional log ins or account generation as these will likely collect personal data.

You should not input personal data, sensitive information or content created by others into any AI platform.

Outputs from generative AI can seem convincing and plausible but due to the way these systems work, could be factually incorrect, contain 'sources' that do not actually exist and/ or reflect biases and prejudices derived from the data the models were trained on.

We understand that common tools (such as grammar and spell checks) are already embedded into the software you use for assignments and, unless measurement of spelling, for example, is an aspect of an assessment we would not anticipate any issues. The same principle should be used when considering whether AI is appropriate to use within your assessment.

What constitutes appropriate use?

You should be given clear guidance on what level of generative AI is appropriate in any given assessment. Things are moving quickly so if you are uncertain, please do ask. We have suggested four broad levels that your Programme and Module leaders may adjust to the specifics of an assessment:

1. Minimal

Includes routine and established use of tools such as auto transcription, spell checkers, grammar check.

2. Limited/selective

Use for clearly delineated tasks as appropriate/allowed/recommended. May include:

Ideation/brainstorming

Concept checking/explanation

Suggesting structures

Suggesting titles

Summarising own or other's key ideas

Illustrative image or other media generation

3. Open

No specific restrictions but with requirement to track key stages/tools utilised. Possible uses may include:

Socratic chat bot/tutor

Summarisation, simplification, synthesis, translation

Generate illustrative media content

Support production of multimedia artefacts

4. Embedded

AI use is a feature of the assessment itself. Here the use of generative AI is a focal aspect of the assessment. This may include:

Using named tools for a specific outcome

Output comparison and critique

Develop or error check code

Once again, we state that in no circumstance is it appropriate to use generated content verbatim without clear indication and acknowledgement, or to effectively outsource the writing task as much as it is inappropriate to get someone else to write something and claim it as your own.

We suggest three golden rules:

Golden Rule 1: Learn with your chats and interactions with AI, but never copy-paste. Close the AI chat before working on your assignment and avoid using AI tools that write directly into it.

Golden rule 2: Ask if you are uncertain about what is allowed in any given assessment.

Golden rule 3: Ensure you take time before submission to acknowledge use of generative AI.

If you follow these three rules you are essentially ensuring that your assessed outputs are unlikely to breach academic integrity guidance in terms of use of generative AI.

King's College London, unlike some other universities, does not require students to reference generative AI as an authoritative source in the reference list for much the same reason you would not be expected to cite a search engine, a student essay website or be over-dependent on synoptic, secondary source material.

However, as we learn more about the capabilities and limitations of these tools and as we work together to evolve our own critical AI literacies, we do expect you to be explicit in acknowledging your use of generative AI tools such as Microsoft Bing Chat (available via your KCL account), Google Bard, ChatGPT or any other media generated through other generative AI tools.

King's requires students to acknowledge any use of generative AI tools in coursework by including a declaration statement along with your references. Please note that so long as acknowledged use falls within the scope of appropriate use as defined in the assessment brief/guidance then this will not have any direct impact on the grades awarded. Unless alternative wording is suggested in an assessment brief then you should append one of the following:

1. I declare that no part of this submission has been generated by AI software. These are my own words.

Note. Using software for English grammar and spell checking is consistent with Statement 1.

[or]

2. I declare that parts of this submission has contributions from AI software and that it aligns with acceptable use as specified as part of the assignment brief/ guidance and is consistent with good academic practice. The content can still be considered as my own words. I understand that as long as my use falls within the scope of appropriate use as defined in the assessment brief/guidance then this declaration will not have any direct impact on the grades awarded.

I acknowledge use of software to [include as appropriate]:

(i) Generate ideas or structure suggestions, for assistance with understanding core concepts, or other substantial foundational and preparatory activity.

[insert AI tool(s) and links and/or how used]

(ii) Write, rewrite, rephrase and/or paraphrase part of this essay.

[insert AI tool(s) and links]

(iii) Generate some other aspect of the submitted assessment.

[insert AI tool(s) and links] / Include brief details]

Inappropriate use without attribution is considered academic misconduct. For further details please see the Academic Misconduct Policy and Academic Misconduct Procedure.

Potential penalties can range from formal warnings, resubmitting the coursework, suspension or expulsion.

If inappropriate use is suspected, a student may be called to an academic integrity meeting with academics to discuss authorship concerns and resolve any issues. The purpose of the AIM is to discuss the student's assessment. The following topics would be explored in the AIM:

* How the student prepared the assessment task

* Feedback on the academic integrity concerns identified

* Any other circumstances impacting the student's submission which they may wish to raise.

The Staff members discuss the concerns with the student, and the student is given an opportunity to input and discuss their perspectives and reflect on their work process.

In line with the Russell Group statement that universities will support you to become AI-literate, King's sees this as a key academic skill that you will need to develop to succeed in your studies and career.

The existing providers of academic, digital, and data literacy skills support (including King's Foundations, CTEL, Libraries and Collections, Centre for Doctoral Studies) will work closely with King's Academy to be able to provide a coherent and comprehensive level of support and skills development provision. Look out for a FutureLearn course that has contributions from staff and students from across King's too.

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Digital Realities: Embracing Translanguaging as the Future of EAP

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Digital Realities: Embracing Translanguaging as the Future of EAP

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https://www.youtube.com/watch?v=-600GB8w-WQ

This webinar will examine how the field of EAP can adapt to disruptive technologies by rejecting deficit approaches which enforce English dominance, and instead embrace students' digital translanguaging abilities that have too long been ignored. Kelly Webb-Davies - Bangor University International College.

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Kelly Webb-Davies: Translanguaging in EAP

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Interesting qualitative research:

The results are surprising and to me positive – students could clearly articulate what the tools lack, and what they would miss out on if they ONLY used AI tools:

Puxton, P., Brook, J. and Prevatt-Goldstein, A. (2023) Using AI tools for the reading-into-writing process. Available at: https://www.ucl.ac.uk/teaching-learning/case-studies/2023/oct/using-ai-tools-reading-writing-process (Accessed: 29 February 2024).

The results are surprising and to me positive – students could clearly articulate what the tools lack, and what they would miss out on if they ONLY used AI tools:

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Assessment in the age of artificial intelligence 

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Assessment in the age of artificial intelligence 

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Inside Higher Ed article: ChatGPT can helps students and faculty with ADHD

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Inside Higher Ed article: ChatGPT can helps students and faculty with ADHD

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Using generative AI to improve digital accessibility efficiency

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Using generative AI to improve digital accessibility efficiency

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Digitally Enhanced Education Series: Using Generative AI to Improve Digital Accessibility Efficiency explores how tools like Copilot, Whisper, and Claude can streamline accessibility workflows. Practical tips: generating readable transcripts, transcribing podcasts, and creating accessible flowcharts.

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Using generative AI to improve digital accessibility efficiency, featuring Copilot, Whisper & Claude

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AI in 2025 - What's beyond the Honeymoon Phase? 

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AI in 2025 - What's beyond the Honeymoon Phase? 

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Part of the Macmillan Teaching Festival 2025. It examines the shift from AI as a novelty to more practical integration, highlighting both the opportunities and challenges educators face as AI becomes a normal part of teaching and learning environments.

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AI in 2025 - What's Beyond the Honeymoon Phase? by Joanna Szoke

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TELSIG: AI errors as Learning Opportunities

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TELSIG: AI errors as Learning Opportunities

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AI Errors as Learning Opportunities – TELSIG: Anne-Marie Langford from the University of Northampton demonstrates how generative AI can be used to create academic writing examples with errors for students to critique. Anne-Marie argues that this approach encourages active learning, critical thinking, and ethical awareness by helping students identify and correct AI-generated errors, fostering deeper engagement with academic writing practices.

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TELSIG: Integrating an AI Criticality Assessment into EAP 

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TELSIG: Integrating an AI Criticality Assessment into EAP 

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"Integrating an AI Criticality Assessment into EAP – how EAP modules can incorporate AI literacy by teaching students to critically assess AI-generated content. It emphasizes developing students' analytical skills to evaluate the reliability, bias, and academic appropriateness of AI outputs.

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TELSIG Webinar: Integrating AI Criticism into student assessment