What are the implications of artificial intelligence for media educators, and for the media themselves?<\/em><\/p>\n\n\n\n Last weekend, a letter<\/a> by a group of British headteachers was published in the Times<\/em> newspaper, with the front page headline \u2018AI is clear and present danger to education\u2019. Calling recent developments in the area \u2018bewildering\u2019, it urged the government to set up an independent enquiry to establish which of them are \u2018beneficial\u2019 and which are \u2018damaging\u2019. The letter\u2019s lead author was Sir Anthony Seldon, a well-known private school head and a noted political biographer, whose popular book<\/a> on the subject is fairly even-handed \u2013 despite the dramatic headline.<\/p>\n\n\n\n Coincidentally, I was at an international conference on \u201821st<\/sup> Century Learning\u2019 at the time, hearing some much more enthusiastic accounts of the issue. I doubt that my general scepticism<\/a> about technology-in-education (familiar to readers of this blog) went down well with many of the delegates. While one or two of the other presentations were somewhat critical, the overall tone was almost overwhelmingly celebratory. As is commonly the case in such discussions, the breathless excitement often resembled a sales promotion, as presenters marvelled at the swathes of \u2018cool stuff\u2019 that is now coming onto the market.<\/p>\n\n\n\n The use of AI is already widespread in many arenas: image recognition software, search engines, file-sharing platforms and retailers\u2019 \u2018recommender\u2019 systems all depend upon it. However, what we\u2019re talking about here is something more specific: that is, generative<\/em> AI. This refers to software that (like the algorithms developed by Google, YouTube, Spotify or Amazon, for example) is capable of gathering, processing, analysing and synthesising user data. In this case, however, the outputs that ensue are not just a few more-or-less useful recommendations or suggestions, but extensive texts that users can subsequently refine or edit.<\/p>\n\n\n The debate has significantly gathered pace since the launch<\/a> last November of the latest (fourth) iteration of ChatGPT, created by the US company OpenAI. Like some other Silicon Valley start-ups, OpenAI began life in 2015 as a not-for-profit venture, but it has quickly become a hot commercial enterprise: it partners with Microsoft, and is currently (some might say conservatively) projecting<\/a> annual revenues of $1 billion by 2024. Within two months of its launch, ChatGPT-4 apparently became the fastest selling application<\/a> of all time, reaching 100 million users by the start of this year. However much some commentators may fantasise about \u2018banning\u2019 such products, they are very much here to stay.<\/p>\n\n\n\n ChatGPT has attracted most attention, but it is only one of many such applications. Notable rivals in the field of verbal text (or \u2018large-language-model\u2019) generation include Google\u2019s BARD and Microsoft\u2019s BING-AI. Others \u2013 such as OpenAI\u2019s own DALL-E, along with packages like Open Diffusion and Midjourney \u2013 are capable of producing high-quality images based on fairly simple verbal prompts. This issue hit the headlines most recently, when the creative photo category in the Sony World Photography Awards was won by an image<\/a> (pictured here) that turned out to be generated in DALL-E. (The winner, Boris Eldagsen, had entered precisely in order to draw attention to the issue, and turned down the prize.)<\/p>\n\n\n\n Contrary to OpenAI\u2019s rhetoric of \u2018deep learning\u2019, when it comes to education, the fear is that this technology is actually about the very opposite: cheating. In the UK, the debate about AI has been dominated by an overwhelming anxiety about assessment<\/em> \u2013 a phenomenon that reflects the absolutely determining role of high-stakes assessment in our current system. This takes on a particular force in higher education, where institutions are massively (and precariously) dependent on high-paying international students, and where language requirements have been progressively reduced.<\/p>\n\n\n\n Of course, plagiarism is by no means a new phenomenon \u2013 nor indeed are \u2018essay farms\u2019 that will sell sufficiently competent and seemingly original work. Like most academics, I could tell some amusing and depressing tales about this that go back several decades. Yet while packages like Turnitin are now routinely used to detect cut-and-paste plagiarism, the threat with ChatGPT is that plagiarism is now becoming impossible to detect. While some maintain that new services (like gptzero.me) can already perform this task, others are less convinced.<\/p>\n\n\n\n Judging from results I have seen, packages like ChatGPT are pretty good at mimicking the bland \u2018on the one hand this, on the other hand that\u2019 style of conventional essay writing. The essays that emerge are typically dutiful and mechanical: they are rarely personal or passionate or humorous or inspired, and of course they cannot include first-hand research. In the face of what seems like undetectable plagiarism, some institutions are already desperately turning back to sit-down written examinations; yet if the advent of AI encourages educators to develop more creative and challenging approaches to assessment than the predictable essay format, that may be no bad thing.<\/p>\n\n\n\n At my conference, I was asked how I thought the advent of AI would affect the media themselves; and how media educators might respond. Of course, the technology is changing so quickly that it may be too early to say very much about this. As with earlier technologies, we need to be wary both of excitable hype and of doom-saying: AI is unlikely to transport us to a creative utopia any more than it will take us all to hell. This was easy to say; yet as I listened to the hucksters enthusiastically promoting all that \u2018cool stuff\u2019, I felt the need to go further. What follows are some early thoughts on these two questions \u2013 from the position of one who is evidently far from an expert.<\/p>\n\n\n In some respects, the implications for media themselves might seem easier to identify. The use of AI represents a form of automation that seems bound to lead to job losses<\/a>, or at least to a significant restructuring of working practices. Of course, this is an ongoing process in the media industries in any case; and it may be that the kinds of work that will be replaced will be fairly low-level. Applications like ChatGPT are good at generating generic or descriptive content, and the kind of bland \u2018space filling\u2019 reports that journalists routinely have to produce: they are much less good at being innovative or creative, and they don\u2019t do original research. Yet if routine entry-level jobs disappear, where will creative careers begin?<\/p>\n\n\n\n This kind of software initially \u2018trains\u2019 itself by scraping or gathering existing online data, and it does so largely without the consent or knowledge of those who originally created it. It does not require permission; nor indeed is it currently possible to prevent this from happening. Inevitably, this has massive implications for copyright; and beyond that, for workers\u2019 livelihoods. Once again, this is not a new issue; but the potential for appropriating (or stealing) existing content from multiple sources and then reworking it is vastly increased when it is possible to do so invisibly<\/em>.<\/p>\n\n\n\n Meanwhile, packages like ChatGPT \u2013 like Google<\/a> in its early days \u2013 do not outwardly appear to depend upon advertising. They appear to talk to you, to be your friend, to help you, all for free. Yet, as the saying goes, if the service seems to be free, then you<\/em> are the product. The profitability of such packages depends upon the recording and gathering \u2013 and eventually selling \u2013 of personal data. The more \u2018interactive\u2019 they seem, the greater the potential for surveillance.<\/p>\n\n\n\n A further issue that has generated concern here has carried over from debates about so-called \u2018fake news\u2019. Generative AI packages currently have no meaningful way of checking the veracity of the original data on which they depend, or indeed of assessing the reliability of sources. This renders them vulnerable to all sorts of \u2018algorithmic bias\u2019 that further privilege particular social groups, and raise the potential for dissemination of disinformation \u2013 not least in political campaigns. Self-evidently, the outcomes will only be as good as the existing data on which the application depends.<\/p>\n\n\n Critics gleefully report on the basic factual mistakes that can be made by applications like ChatGPT (which some refer to as \u2018hallucinations\u2019) \u2013 although, as I\u2019ve argued before<\/a>, factual errors are generally easier to spot (and correct) than more complex and subtle forms of misinformation and bias. We might well ask whether misinformation is likely to be a much greater hazard than it is with existing forms of journalism. Likewise, visual AI applications are capable of producing convincing \u2018deepfakes\u2019; although the potential for this has also been available<\/a> for several years.<\/p>\n\n\n\n Somewhat less obvious to most critics \u2013 but arguably much more concerning \u2013 are the environmental implications of such technologies. Some reports<\/a> have suggested that a single ChatGPT dialogue with 20-50 questions consumes half a litre of clean drinking water; and with 100 million global subscribers already, the environmental impact is potentially vast. It has been estimated that \u2018training\u2019 the earlier iteration of ChatGPT (version 3) may have required as much as 700,000 litres to generate and cool the vast data centres that the industry euphemistically refers to as the \u2018cloud\u2019.<\/p>\n\n\n\n To some extent, all these problems might be seen merely as extrapolations of existing trends, rather than entirely new developments; all that AI does is to automate (and thus accelerate) processes that have already been under way for some time. Nevertheless, they do undoubtedly reinforce the need for regulation. When Sam Altman, the CEO of OpenAI, recently appeared before Congress, he appeared to accept the need for government regulation, and even called for a moratorium on new developments until this could be achieved.<\/p>\n\n\n\n While it might seem extraordinary for technology companies to be asking for their own products to be regulated in this way, Altman\u2019s performance recalled that of Mark Zuckerberg making similar requests to Congress in the wake of the Cambridge Analytica scandal \u2013 as the New Yorker<\/em> correspondent pointed out<\/a>. With this new market still in its infancy, companies like OpenAI are trying very hard to manoeuvre themselves into the key position of being early brand leaders, and to steal a march on the opposition. They may be keen to manicure their public image, but they surely recognise the likelihood that any meaningful regulation will be lost in the miasma of bureaucracy.<\/p>\n\n\n So to the second question: how might media educators respond to these developments? On one level, the advent of AI provides us with yet more new curriculum content. The crucial thing here, in my view, is to build on the critical frameworks and classroom approaches that we have developed over time. Just as with social media<\/a>, it\u2019s both possible and productive to extend media education \u2018key concepts\u2019 to this new medium. My observations above suggest a series of questions to do with the political economy<\/em> of AI that might well be explored by students: basic questions about business, work and regulation are all familiar from the approaches we have developed with students relating to \u2018older\u2019 media. Likewise, well-established questions about representation, reliability and trust, and about the wider social implications of AI, will be familiar, and can usefully be explored and debated by students.<\/p>\n\n\n\n However, there are other questions about how we might actively use<\/em> AI in our teaching. Here I begin with a quotation adapted from the semiologist Umberto Eco\u2019s arguments about television, from almost fifty years ago: if you want to use media (or technology) to teach somebody, you first have to teach them how to understand media. Media education is not primarily about teaching with<\/em> or through<\/em> media, but teaching about<\/em> media: it is not to be confused with educational media, or educational technology. This takes us well beyond the use of technology as an instrumental tool for learning \u2013 an approach that seems to be taken for granted in debates about AI, just as it has been in relation to earlier educational technologies. Arguably, the more effective and seamless the technology becomes, the harder (and yet more necessary) it is to take a critical distance from it: we need to slow it down, to de-familiarise it, and to consider how it might be otherwise.<\/p>\n\n\n\n There are numerous resources that explain the basic principles of how AI works, at least on a technical level. At my conference, I was impressed by some very straightforward materials<\/a> produced by BSD Education, which are quite usable at primary school level; and from there, it would not be difficult to move on to more critical questions about algorithmic bias. (There are numerous others, notably Day of AI<\/a>, which comes out of MIT; and a large collection here<\/a>.)<\/p>\n\n\n![\"images\"](\"https:\/\/ddbuckingham.files.wordpress.com\/2023\/05\/images.jpg?w=335&h=335&fit=335%2C335&resize=225%2C225\")
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