Taccle AI

Improving skills and competences of VET teachers and trainers in the age of Artificial Intelligence

21stCenturySkills

News 

What is the purpose of Vocational Education and Training?

Graham Attwell 125 Views , , , , , , , ,
Photo by Jeswin Thomas on Unsplash

Is Artificial Intelligence challenging us to rethink the purpose of Vocational Education and Training? Perhaps that is going too far, but there are signs of questions being asked. For the last twenty five years or so there has been a tendency in most European countries for a narrowing of the aims of VET, driven by an agenda of employability. Workers have become responsible for their own employability under the slogan of Lifelong Learning. Learning to learn has become a core skill for students and apprentices, not to broaden their education but rather to be prepared to update their skills and knowledge to safeguard their employability.

It wasn’t always so. The American philosopher, psychologist, and educational reformer John Dewey believed “the purpose of education should not revolve around the acquisition of a pre-determined set of skills, but rather the realization of one's full potential and the ability to use those skills for the greater good.” The overriding theme of Dewey's work was his profound belief in democracy, be it in politics, education, or communication and journalism and he considered participation, not representation, the essence of democracy.

Faced with the challenge of generative AI, not only to the agency and motivation of learners, but to how knowledge is developed and shared within society, there is a growing understanding that a broader approach to curriculum and learning in Vocational Education and Training is necessary. This includes a more advanced definition of digital literacy to develop a critique of the outputs from Large Language Models. AI literacy is defined as the knowledge and skills necessary to understand, critically evaluate, and effectively use AI technologies (Long & Magerko, 2020) including understanding the capabilities and limitations of AI systems, recognising potential biases and ethical implications of AI-generated content  and developing critical thinking skills to evaluate AI-produced information .

UNESCO says their citizenship education, including the competence frameworks for teachers and for students, builds on peace and human rights principles, cultivating essential skills and values for responsible global citizens. It fosters criticality, creativity, and innovation, promoting a shared sense of humanity and commitment to peace, human rights, and sustainable development. Fenchung Miao from UNESCO has said the AI competency framework for students proposed the term of "AI society citizenship" and provided interpretation in multiple sections. Section 1.3 of the Framework, AI Society Citizenship says:

Students are expected to be able to build critical views on the impact of AI on human societies and expand their human centred values to promoting the design and use of AI for inclusive and sustainable development. They should be able to solidify their civic values and the sense of social responsibility as a citizen in an AI society. Students are also expected to be able to reinforce their open minded attitude and lifelong curiosity about learning and using AI to support self actualisation in the AI era.

The Council of Europe says Vocational Education and Training is an integral part of the entire educational system and shares its broader aim of preparing learners not only for employment, but also for life as active citizens in democratic societies. Social dialogue and corporate social responsibility are seen as tools for democratising AI in work.

Renewing the democratic and civic mission of education underlines the importance of integrating Competences for Democratic Culture (CDC) in VET to promote quality citizenship education. This initiative aims to support VET systems in preparing learners not only for employment but also for active participation as citizens in culturally diverse democratic societies. By embedding CDC in learning processes in VET, the Council of Europe aims to ensure that VET learners acquire the necessary knowledge, skills, values and attitudes to participate fully in democratic life.

The Council of Europe Reference Framework for Democratic Culture and the Unesco AI Competence Framework can provide a focus for a wider understanding of AI competences in VET and provide a challenge for how they can be implemented in practice. 

Such an understanding can shape an educational landscape that leverages AI while safeguarding human agency, motivation, and ethics. As generative AI advances, continuous dialogue and investigation among all educational stakeholders are essential to ensure these technologies enhance learning outcomes and equip students for an AI-driven future.

References

Dewey, J. (1916) Democracy and Education: an introduction to the philosophy of education, New York: Macmillan. https://archive.org/stream/democracyandedu00dewegoog#page/n6/mode/2up. Retrieved 4 May 2024 

Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

UNESCO (2024) AI Competency Framework for Students, https://unesdoc.unesco.org/ark:/48223/pf0000391105

News 

Teacher’s Digital Literacy

Graham Attwell 230 Views , , , , , ,
Nacho Kamenov & Humans in the Loop / Better Images of AI / A trainer instructing a data annotator on how to label images / CC-BY 4.0

This definition of AI literacy for teachers was posted on Linked in by Fenchung Miao, Chief, Unit for Technology and AI in Education at UNESCO.

  1. Cultivate a critical view that AI is human led and the corporate and individual decision of AI creators have profound impact on human autonomy an rights, becoming aware of the importance of human agency when evaluating and using AI tools.
  2. Develop a basic understanding on typical ethical issues related to AI and acquire basic knowledge on ethical principles for human / AI interactions, including protection of human rights and human agency, promotion of linguistic and cultural diversity and advocating for inclusion and environmental sustainability.
  3. Acquire basic conceptual knowledge on AI, including the definition of AI, basic knowledge on how an AI model is trained and associated knowledge on data and algorithm, main categories and examples of AI technologies, as well as basic skills on examining appropriateness of specific AI tools for education and operational skills of validated AI tools.
  4. Identify and leverage the pedagogical benefits of AI tools to support subject specific lesson planning, teaching and assessments.
  5. Explore the useoif AI tools to enhance their professional learning and reflective practices, supporting assessment of learning needs and personal learning pathways in the rapidly evolving educational landscape.
News 

Who promotes education technology as a precondition for education transformation?

Graham Attwell 640 Views , , , , , , , , ,

UNESCO has published the 2023 edition of the The Global Education Monitoring Report (GEM) entitled "technology in education: a tool on whose terms?" The report is an independent annual publication. The funded by a group of governments,multilateral agencies and private foundations and facilitated and supported by UNESCO. It comes in at a weighty 418 pages with much of interest (I have yo admit I have only read the 32 page summary.

Unusually for a report of this type, it received considerable media attention, at least in the UK. But this was focused on the section of the use of smartphones in school which concluded that "Mere proximity to a mobile device was found to distract students and to have a negative impact on learning in 14 countries, yet less than one in four have banned smartphone use in schools."

The report says that "good, impartial evidence on the impact of education technology is in short supply" adding that here is little robust evidence on digital technology’s added value in education. Technology evolves faster than it is possible to evaluate it: Education technology products change every 36 months, on average. Most evidence comes from the richest countries. In the United Kingdom, 7% of education technology companies had conducted randomized controlled trials, and 12% had used third-party certification. A survey of teachers and administrators in 17 US states showed that only 11% requested peer-reviewed evidence prior to adoption. A lot of the evidence comes from those trying to sell it. Pearson funded its own studies, contesting independent analysis that showed its products had no impact.

The 2018 PISA found that 65% of 15-year-old students in OECD countries were in schools whose
principals agreed that teachers had the technical and pedagogical skills to integrate digital devices in instruction and 54% in schools where an effective online learning support platform was available; these shares are believed to have increased during the COVID-19 pandemic. Despite this the report found that teachers often feel unprepared and lack confidence teaching with technology. Only half of countries have standards for developing teacher ICT skills. While 5% of ransomware attacks target education, few teacher training programmes cover cybersecurity.

The report found that online content has grown without enough regulation of quality control or diversity. Online content is produced by dominant groups, affecting access to it. Nearly 90% of content in higher education repositories with open education resource collections was created in Europe and Northern America; 92% of content in the OER Commons global library is in English. Massive open online courses (MOOCs) mainly benefit educated learners and those from richer countries.

Higher education is adopting digital technology the fastest and being transformed by it the most. There were over 220 million students attending MOOCs in 2021. But digital platforms challenge universities’ role and pose regulatory and ethical challenges, for instance related to exclusive subscription deals and to student and personnel data.

While such technology has tremendous potential, many tools have not been designed for application to education. Not enough attention has been given to how they are applied in education and even less to how they should be applied in different education contexts.

To understand the discourse around education technology, it is necessary to look behind the language being used to promote it, and the interests it serves. Who frames the problems technology should address? What are the consequences of such framing for education? Who promotes education technology as a precondition for education transformation? How credible are such claims? What criteria and standards need to be set to evaluate digital technology's current and potential future contribution to education so as to separate hype from substance? Can evaluation go beyond short-term assessments of impact on learning and capture potential far-reaching consequences of the generalized use of digital technology in education?

Exaggerated claims about technology go hand in hand with exaggerated estimates of its global market size. In 2022, business intelligence providers’ estimates ranged from USD 123 billion to USD 300 billion. These accounts are almost always projected forward, predicting optimistic expansion, yet they fail to give historic trends and verify whether past projections proved true. Such reporting routinely characterizes education technology as essential and technology companies as enablers and disruptors.
If optimistic projections are not fulfilled, responsibility is implicitly placed on governments as a way of maintaining indirect pressure on them to increase procurement. Education is criticized as being slow to change, stuck in the past and a laggard when it comes to innovation. Such coverage plays on users’ fascination with novelty but also their fear of being left behind.

News 

What will happen to jobs with the rise and rise of Generative AI

Graham Attwell 517 Views , , , , , , , , ,
Photo by Xavier von Erlach on Unsplash

OK where to start? First what is Generative AI? It is the posh term for things like ChatGPT from OpenAI or Bard from Google. And these Generative AIs based on Large Language Models are fast being integrated into all kinds of applications starting out with the chatbot integrated into Microsoft Bing browser and Dalll-E just one of applications generating images from text or chat descriptions.

Predicting what will happen with jobs is a tricky business. Jobs have been threatened by successive waves of technology. In general the overall effect on employment appears to have been less than was predicted. Of course there was a vast shift in employment with the advent of mechanization in agriculture but that took place around the end of the 19th century at least in some countries. And its pretty easy to find jobs that have disappeared in recent times - for instance employment in video shops. But in general it appears that disruption has been less than predicted in various surveys and reports. Technology has been used to increase productivity - for example in shops using self checkouts and automated stock management - or has been used to complement working processes and tasks rather than substitute for workers and the generation of new jobs to work with the technology

But what is going to happen this time round with all sorts of predictions and speculation - not helped by no-one quite knowing what Generative AI is capable of and even harder what it will be able to do in the very near future. Bill Gates (the founder of Microsoft) has said the development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. There is too much press and media speculation to even sum up the general reaction to the release of these new AI models and applications although Stephen Downes is making a valiant attempt in his OLDaily newsletter. Personally I enjoyed UK restaurant critic, Jay Raynor's account in the Guardian newspaper of when he asked ChatGPT to write a restaurant review in his own inimitable style. Of course, along with concerns over the impact on employment and jobs, there is much concern over the ethical implications of the new AI models although it is worth noting Ilkka Tuomi writing on LinkedIn (his posts are well worth following) has noted that the EU has been an early mover in policy and regulation. Ilkka also, while noting that education (and teaching) is more than just knowledge transformation, says "dialogue and learning by teaching are very powerful pedagogical approaches and generative AI can be used in many different ways in learning and education:. He concludes by saying: "This really could have a transformative impact."

Anyway back to the more general impact on jobs which is an issue for the new EU AI Pioneers project which focuses on the impact on Vocational Education and Training and Adult Education. Last weekend saw the release of a report by Goldman Sachs predicating that as many as 300 million jobs could be affected by generative AI and the labor market could face significant disruption. However they suggest that :most jobs and industries are only partially exposed to automation and are thus more likely to be complemented rather than substituted by AI". In the US they estimate 7% of jobs could be replaced by AI, with 63% being complemented by AI and 30% being unaffected by it. Perhaps one of the reasons for so much concern is that this wave of automation seems to be most likely to impact on skilled work with, say Goldman Sachs, office and administrative support positions at the greatest risk of task replacement (46%(, followed by legal positions (44%) and architecture and engineering jobs (37%).

What I found most interesting from the full report (rather than the press summaries) is the methodology. The report includes a quite detailed description. It says:

Generative AI’s ability to 1) generate new content that is indistinguishable from human-created output and 2) break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects.

The report is based on "data from the O*NET database on the task content of over 900 occupations in the US (and later extend to over 2000 occupations in the European ESCO database) to estimate the
share of total work exposed to labor-saving automation by AI by occupation and industry." They assume that AI is capable of completing tasks up to a difficulty of 4 on the 7-point O*NET “level” scale and
"then take an importance- and complexity-weighted average of essential work tasks for each occupation and estimate the share of each occupation’s total workload that AI has the potential to replace." They "further assume that occupations for which a significant share of workers’ time is spent outdoors or performing physical labor cannot be automated by AI."

What are the implications for Vocational Education and Training and Adult Education? It seems clear that very significant number of workers are going to need some form of training or Professional Development - at a general level for working with AI and at a more specific level for undertaking new work tasks with AI. There is little to suggest present education and training systems in Europe can meet these needs, even if we expect a ramping up of online provision. The EU's position seems to be to push the development of Microcredentials which according the the EU Cedefop agency "are seen to be fit for purposes such as addressing the needs of the labour market, lifelong learning, upskilling and reskilling, recognising prior learning, and widening access to a greater variety of learners. Yet in their recent report, they say that

"Microcredentials tend to be a flexible, demand-driven response to the need for skills in the labour market, but they can lack the same trust and recognition enjoyed by full qualifications. In terms of whether and how they might be accommodated within qualification systems, they can pose important questions about how to guarantee their value and currency without undermining both their own flexibility and the stability and dependability of established qualifications."

The need for new skills for AI pose a question for how curricula can be adapted and updated faster than has been done traditionally. And they pose major questions for institutions to adapting course provsion to to new skill needs at a local and regional level as well as national. Of course there are major challenges for the skills and competences of teachers and trainers, who, the AI and VET project found, were generally receptive to embracing AI for teaching and learning as well as new curricula content, but felt the need for more support and professional training to update their own skills and knowledge (and this was before the launch of Generative AI models.

All in all, there is a lot to think about here.

News 

Artificial Intelligence degrees

Graham Attwell 747 Views , , ,
convocation, mortar board, graduation

mamir_k94 (CC0), Pixabay

The UK operates a central university admissions service, called UCAS. Today they have released their analysis of institutional and subject admissions for 2020. In an article in the online Higher Education newspaper, WONKHE, Sander Kristel, Chief Operations Officer at UCAS, points out some of  the more striking features of the data.

He reports that Artificial Intelligence degrees have grown by more than 400 per cent in the past decade – from just 65 acceptances in 2011 to 355 acceptances in 2020.

As he says:

This growth will be music to the ears of employers according to research from the Industrial Strategy Council, which highlighted the adoption of automation as the biggest driver of a shift in skills and estimated that 39 per cent of the activities that people are paid to do in the UK today could be automated by 2030, with current technology creating demand in technology-related occupations such as software development.

Less welcome news, however, is that although the ratio of UK male acceptances to UK female acceptances across all Science, Technology, Engineering and Maths subjects has shrunk from 1.34 in to 1.06 over the last decade, there has been little progress made in closing the gap for computer science (6.2 in 2011, relative to 5.7 in 2020), perhaps related to the significant amount of growth in this subject overall.