Arguments over what data should be allowed to be used for training Large Language Models rumble on. Ironically it is LinkedIn which hosts hundreds of discussion is AI which is the latest villain.
The platform updated its policies to clarify data collection practices, but this led to user backlash and increased scrutiny over privacy violations. The lack of transparency regarding data usage and the automatic enrollment of users in AI training has resulted in a significant loss of trust. Users have expressed feeling blindsided by LinkedIn's practices.
In response to user concerns, LinkedIn has committed to updating its user agreements and improving data practices. However, skepticism remains among users regarding the effectiveness of these measures. LinkedIn has provided users with the option to opt out of AI training features through account settings. However, this does not eliminate previously collected data, leaving users uneasy about data handling.
However, it is worth noting that accounts from Europe are not affected at present. It seems that LinkedIn would be breaking European laws if they were to try to do the same within the European Union.
More generally, the UK Open Data Institute says "there is very little transparency about the data used in AI systems - a fact that is causing growing concern as these systems are increasingly deployed with real-world consequences. Key transparency information about data sources, copyright, and inclusion of personal information and more is rarely included by systems flagged within the Partnership for AI’s AI Incidents Database.
While transparency cannot be considered a ‘silver bullet’ for addressing the ethical challenges associated with AI systems, or building trust, it is a prerequisite for informed decision-making and other forms of intervention like regulation."
Joahna Kuiper / Better Images of AI / Little data houses / CC-BY 4.0
In August I became hopeful that the hype around Generative AI was beginning to die down. Now I thought we might get a gap to do some serious research and thinking about the future role of AI in education. I was wrong! Come September and the outpourings on LinkedIn (though I can' really understand how such a boring social media site became the focus for these debates) grew daily. In part this may be because there has now been time for researchers to publish the results of projects actually using Gen AI, in part because the ethical issues continue to be of concern. But it may also be because of a flood of AI based applications for education are being launched almost every day. As Fengchun Miao, Chief, Unit for Technology and AI in Education at UNESCO, recently warned: "Big AI companies have been hiring chief education officers, publishing guidance for teachers, and etc. with an intention to promote hype and fictional claims on AI and to drag education and students into AI pitfalls."
He summarised five major AI pitfalls for education:
Fictional hype on AI’s potentials in addressing real-world challenges
Machine-centrism prevailing over human-centrism and machine agency undermining human agency
Sidelining AI’s harmful impact on environment and ecosystems
Covering up on the AI-driven wealth concentration and widened social inequality
Downgrading AI competencies to operational skills bound to commercial AI platforms
UNESCO has published five guiding principles in their AI competency framework for students: 2.1 Fostering critical thinking on the proportionality of AI for real-world challenges 2.2 Prioritizing competencies for human-centred interaction with AI 2.3 Steering the design and use of more climate-friendly AI 2.4 Promoting inclusivity in AI competency development 2.5 Facilitating transferable AI foundations for lifelong learning
And the Council of Europe are looking at how Vocational education and Training can promote democracy (more on this to come later). At the same time the discussion on AI Literacy is gaining momentum. But in reality it is hard to see how there is going to be real progress in the use of AI for learning, while it remains the preserve of the big tech companies with their totally technocratic approach to education.
For the last year, I have been saying how the education sector needs to itself be leading developments in AI applications for learning, in a multi discipline approach bringing together technicians and scientists with teachers and educational technologists. And of course we need a better understanding of pedagogic approaches to the use of AI for learning, something largely missing from the AI tech industry. A major barrier to this has been the cost of developing Large Language Models or of deploying applications based on LLMs from the big tech companies.
That having been said there are some encouraging signs. From a technical point of view, there is a move towards small (and more accessible) language models, bench-marked near to the cutting edge models. Perhaps more importantly there is a growing understanding than the models can be far more limited in their training and be trained on high quality data for a specific application. And many of these models are being released as Open Source Software, and also there are Open Source datasets being released to train new language models. And there are some signs that the education community is itself beginning to develop applications.
AI Tutor Pro is a free app developed by Contact North | Contact Nord in Canada. They say the app enables students to:
Do so in almost any language of their choice
Learn anything, anytime, anywhere on mobile devices or computers
Engage in dynamic, open-ended conversations through interactive dialogue
Check their knowledge and skills on any topic
Select introductory, intermediate and advanced levels, allowing them to grow their knowledge and skills on any topic.
And the English Department for Education has invited tenders to develop an App for Assessment, based on data that they will supply.
I find this encouraging. If you know of any applications developed with a major input from the education community, I'd like to know. Just use teh contact form on this website.
Some ten or so years ago myself and Jenny Hughes were commissioned by Lifelong Learning UK to produce a Literature review on Pedagogic approaches to using technology for learning. As Wikipedia explains, Lifelong Learning UK (LLUK) was one of the independent, Sector Skills Councils (SSCs) for UK employers in the lifelong learning sector. It was responsible for the professional development of all those working in community learning and development, further education, higher education, libraries, archives and information services, and work based learning across the UK.
As has arisen in the recent debate over the new Labour Government's establishment of SkillsUK, there is a long tradition in UK education of setting up new agencies and organizations, only to abandon them after a few years. Anyway just after our report was published LLUK were shut down. Even so, the report continued to be accessible from ResearchGate and other sites and has over the years gained a respectable number of downloads.
In the last couple of days I have been playing with the Google Labs experimental NotebookLM. I am normally rather under impressed by AI applications built for education but I can see a lot of use for this. It is backed by the Gemini Large Language Model but the Notebooks are based, they say only on data sources which you provide. And then there are different tools which can be used to support your research.
The Literature Review on Pedagogic approaches was a fairly dense piece of work and seemed to me a good example for playing with. I uploaded the 80 page report as a PDF and it gave me teh follwing summary.
This source is a literature review entitled "Pedagogic approaches to using technology for learning", which explores the evolving landscape of technology-enhanced learning. The authors, Graham Attwell and Jenny Hughes, argue that the proliferation of new technologies and internet tools is fundamentally changing the way we learn. They identify key themes driving this change, including the increasing role of digital literacies, the shifting expectations of learners, and the need for new pedagogic models. The review delves into specific pedagogic theories and their implications for using technology, highlighting frameworks like constructionism, activity theory, communities of practice, and Vygotsky's social constructivism.
The document examines the impact of technology on pedagogy in practice, exploring the rhetoric-reality gap and highlighting challenges such as the mismatch between students' digital skills outside school and their limited use within the formal learning environment. They discuss the emergence of personal learning environments (PLEs) as a potential solution to this challenge, and analyse the impact of emerging technologies like Web 3.0 and mobile devices on learning. The authors also examine the role of teacher training and professional development in adapting to this changing technological landscape, highlighting the critical need for training that focuses on pedagogic approaches rather than just technical skills. The review concludes by identifying gaps in existing research, underscoring the need for further investigation into the impact of technology on student performance, the development of evaluation tools specific to e-learning, and the exploration of culturally-specific influences on ICT integration.
Pretty smart although you wold expect an advanced Generative LLM to be able to do that. But you can also ask questions and it suggests examples - for instance "Describe the evolution of digital literacy understandings as outlined in the text." I just uploaded the one report but it could get much more interesting with multiple documents.
Anyway on to the killer feature which may turn out just to be a gimmick. You can ask it to produce a podacst in which two different 'people' discuss your work. And I love it. It has some of the baest explanations of Activity theory, constructionism and Vygotsky's theory of the Zone Proximal Development I have ever heard. Anyway do listen. Although ten years old, I think the pedagogic approaches outlined in chis paper stand the test of time - even more I think they are highly relevant for the debate over AI and the podcast makes the work far more approachable. But if you dco what the original report it is downloadable here.
I don't know quite how I have managed to miss this up to now. The AI Assessment Scale (AIAS) has been around for over a year. On the occasion of updating to the latest version - see illustration above, Leon Furze, a Consultant, author and PhD candidate and one of the authors, said in his blog:
The original AIAS and its subsequent formal version (published in JUTLP) represents a moment in time where educational institutions across the world were reaching for something to help with the immediate problems of AI, such as the perceived threat to academic integrity.
So we have reflected on all of the versions of the AIAS we have seen across the world in K-12 and higher education. We have sought out critique and engaged with diverse perspectives, from school teachers to students, university lecturers, to disability activists, experts in fields including assessment security, cognitive sciences, and pedagogy.
And over the past months, we have refined and invigorated the AI Assessment Scale to bring it up to speed with our current understandings of generative AI and learning.
Rick Payne and team / Better Images of AI / Ai is… Banner / CC-BY 4.0
As the debate rolls on about the use of AI in education,we seem stuck on previous paradigms abut how technology can be used to support the existing education system rather than thing about AI and learning. Bill Gates said last week "The dream that you could have a tutor who’s always available to you, and understands how to motivate you, what your level of knowledge is, this software should give us that. When you’re outside the classroom, that personal tutor is helping you out, encouraging you, and then your teacher, you know, talks to the personal tutor." This can be seen in the release of Apps designed to make the system run more efficiently and support teachers in producing lesson plans, reduce administration etc. And for learners a swath of tutor apps and agents to help navigate the way through to support skills and knowledge development.
But writing about the popular educational exercise of future forecasting in the European Journal of Education in 2022, Neil Selwyn outlined five broad areas of contention that merit closer attention in future discussion and decision-making. These include, he said:
(1) "taking care to focus on issues relating to 'actually existing' AI rather than the overselling of speculative AI technologies;
(2) clearly foregrounding the limitations of AI in terms of modelling social contexts, and simulating human intelligence, reckoning, autonomy and emotions;
(3) foregrounding the social harms associated with AI use;
(4) acknowledging the value-driven nature of claims around AI; and
(5) paying closer attention to the environmental and ecological sustainability of continued AI development and implementation."
In a recent presentation, Rethinking Education, rather than predicting the future of technology in education, Ilkka Tuomi reconsiders the purpose of AI in education which he says "changes knowledge production and use. This has implications for education, research, innovation, politics, and culture. Current educational institutions are answers to industrial-age historical needs."
EdTech he says, has conflated education and learning but they are not the same thing. He quotes Biesta(2015 who said "education is not designed so that children and young people might learn –people can learn anywhere and do not really need education for it –but so that they might learn particular things, for particular reasons, supported by particular (educational) relationships.” (Biesta, 2015)
He goes on to quote Arendt (2061) who said “Normally the child is first introduced to the world in school. Now school is by no means the world and must not pretend to be; it is rather the institution that we interpose between the private domain of home and the world in order to make the transition from the family to the world possible at all. Attendance there is required not by the family but by the state, that is by the public world, and so, in relation to the child, school in a sense represents the world, although it is not yet actually the world.”
Education 4.0 he says is supposedly about “Preparing children for the demands of the future. "Education becomes a skill-production machine." Yet "Skills are typically reflections of existing technology that is used in productive practice and "Skills change when technology changes." Tumomi notes "There are now 13 393 skills listed in the European Skills, Competences, and Occupations taxonomy."
Digital skills are special, he says "because the computer is a multi-purpose tool" and "AI skills are even more special, because they interact with human cognition."
Social and emotional “skills” rank-order people“. "'21st century skills' are strongly linked to human personality, which, by definition, is stable across the life-span and People can be sorted based on, e.g., “openness to experience,” “conscientiousness,” “agreeableness,” “verbal ability,” “complex problem-solving skills,” etc."
Their position is these list doesn’t change in education and "Instead, training and technology potentially increase existing differences.|"
Tuomi draws attention to the the three social functions of education:
"Enculturation: Becoming a member of the adult world, community of practice, or thought community
Development of human agency: Becoming a competent participant in social and material worlds with the capability to transform them
Reproduction of social structures: Maintaining social continuity; social stratification through qualification and social filtering'
AI in education supports Enculturation through:
"AI for knowledge transfer and mastery
Development of human agency
AI for augmentation of agency
Reproduction of social structures
AI for prediction and classification (drop-out / at-risk, high-stakes assessment)Incentives and motives in HE."
But while "Students used to be proud to be on their way into becoming respected experts and professionals in the society which For many families, this required sacrifice they are now facing LLMs that know everything." Why, he asks "should you waste your time in becoming an expert in a world, where the answers and explanations become near zero-cost commodities?" What happens to HE, he ask, "when AI erodes the epistemic function of education? The traditional focus of AI&ED in accelerating learning and achieving mastery of specific knowledge topics is not sustainable"
His proposal is that "The only sustainable advantage for primary and secondary education, will be a focus on the development of human agency. Agency is augmented by technology. Agency is culturally embedded and relies on social collaboration and coordination. Affect and emotion are important and the epistemic function will be increasingly seen from the point of view of cognitive development (not knowledge acquisition). Qualification has already lost importance as the network makes history visible. It still is important for social stratification (in many countries)."
He concludes by reiterating that "Education is a social institution. It should not be conflated with 'learning'. AI vendors typically reinterpret education as learning. Education becomes “personalized” and “individualized,” and the objective changes to fast acquisition of economically useful skills and knowledge. The vendors are looking for education under the lamp-post, but this lamp-post is something they themselves have set up. Very little to do with education."
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