MIT Technology Review is a media company founded at the Massachusetts Institute of Technology in 1899. Through a seemingly well resourced portfolio of web sites, print, newsletter, media and live events, MIT Technology Review says they aim to explain the newest technologies and their commercial, social, and political impacts. Their mission, they say, is to empower our audience with credible insights to understand what’s coming next in emerging technology, and why it matters.

I have been subscribed to their free newsletter on AI for the last three years. And it has extensively covered the rise of Generative AI from a technology angle but also covering the social impact including on education (although unfortunately much of their coverage is behind a paywall). Now they have launched a new series: “Hype Correction”. “Hype Correction”, they say, is a package of stories about AI “to reset expectations—a critical look at where we are, what AI makes possible, and where we go next.

With. my free subscription I had to choose which of the articles in the collection I would read. And I chose to download “Generative AI hype distracts us from AI’s more important breakthroughs” with the sub heading of ” It’s a seductive distraction from the advances in AI that are most likely to improve or even save your life” by Margaret Mitchell. Margaret Mitchell is a computer science researcher and chief ethics scientist at AI startup Hugging Face. Her PhD is on AI language generation and she has worked on algorithmic bias and fairness in machine learning.

One of the issues we have faced in the Erasmus+ AI pioneers project is that many teachers, trainers and policy maker believe AI is only Generative AI and Margaret Mitchell says there is a frenzy of misunderstandings about what AI actually is and what it can and cannot do.

She contrasts Predictive AI to AI designed for generative tasks. Predictive AI, she explains, involves tasks with a finite, known set of answers; the system just has to process information to say which answer is right.

She points out that “predictive AI has quietly been improving weather prediction and food safety, enabling higher-quality music production, helping to organize photos, and accurately predicting the fastest driving routes. We incorporate predictive AI into our everyday lives without evening thinking about it, a testament to its indispensable utility. Yet over the past 10 years, predictive AI has not only nailed bird detection down to the specific species; it has rapidly improved life-critical medical services like identifying problematic lesions and heart arrhythmia.” Because of this technology, she. says “seismologists can predict earthquakes and meteorologists can predict flooding more reliably than ever before. Accuracy has skyrocketed for consumer-facing tech that detects and classifies everything from what song you’re thinking of when you hum a tune to which objects to avoid while you’re driving—making self-driving cars a reality.”

But, Margaret Mitchell says there is “a tendency for people to hype AI without making it clear what kind of AI they’re talking about.” “The generative AI technology involved in chatbots, face-swaps, and synthetic video makes for stunning demos, driving clicks and sales as viewers run wild with ideas that superhuman AI will be capable of bringing us abundance or extinction.”

I think this has happened and continues to happen with educational technology. We have many useful applications of technology both to support facilitate administration and to support learning which have been developed using predictive AI or even no AI at all.

Margaret Mitchell believes: “The widespread appeal of AI is clearly linked to the intuitive nature of conversation-based interactions. But this method of engagement currently overuses generative methods where predictive ones would suffice, resulting in an awkward situation that’s confusing for users while imposing heavy costs in energy consumption, exploitation, and job displacement.”

She concludes: “The future of beneficial AI will not be defined by the flashiest demos but by the quiet, rigorous progress that makes technology trustworthy. And if we build on that foundation—pairing predictive strength with more mature data practices and intuitive natural-language interfaces—AI can finally start living up to the promise that many people perceive today.”

With the large Generative AI companies fighting to dominate the education market, we need to build partnerships between developers, teachers, trainers and education researchers who can lead that quiet, rigourous progress in the education domain.

About the Image

Two older women are visiting enjoying a visit at the museum of technology and are comparing an AI model to previous technological breakthroughs. This image was created as part of the research project co-led by Eleonora Lima (KCL) and We and AI, “Empowering older voices through sensory storytelling about AI”, funded by KCL Creative Practice Catalyst Fund. The image reflects the discussions and insights that emerged from the creative workshop held in July 2025 at the Colindale Library, London, amongst the older adults attending the event. Respondents showed humorous and historically contextual perspectives about generative AI which subverted the stereotypes of older people not being able to understand or meaningfully engage in discourse about AI use.