Taccle AI

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

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How are jobs sensitive to AI doing: an update

Graham Attwell 35 Views 0 Comments , , , , , ,
Nacho Kamenov & Humans in the Loop / Better Images of AI / Data annotators discussing the correct labeling of a dataset / CC-BY 4.0

‭Jeisson Cardenas-Rubio and Gianni Anelli-Lopez from the University of Warwick Institute for Employment Research have posted an interesting blog on the LMi for All website. They have been using data from scraped job adverts to assess the impact of generative AI on employment in the UK. In their first report, some six months ago and looking at data until mid 2022, they looked at the impact on 15 jobs identified by Eloundou, (2023) as vulnerable to Chat GPT. In their initial research they found that "although the fear of AI-induced job displacement is understandable, the current evidence from the UK suggests that AI tools such as Chat-GPT are not yet leading to job losses. The initial findings from the United Kingdom (UK) indicate that, following the launch of Chat-GPT, there have been no sizable changes in the labour market trends, particularly for jobs deemed susceptible to these type of AI tools."

The follow up research suggests some change. In the period to December 2023, the data

begins to reveal a subtle yet persistent decline in the share of online job advertisements (OJAs) for jobs considered to be sensitive to the diffusion of GPTs (i.e. jobs where tasks can be automated or augmented by the widespread adoption and integration of GPTs).

They question whether the modest negative trajectory will continue, stabilise at its current levels, or whether there will be a resurgence as the market adapts and finds new equilibrium with AI technologies?

In conclusion they say:

Incorporating the insights of Acemoglu, Autor, and Johnson (2023), we recognise that selecting a path where technology complements human skills is possible but demands shifts in technological innovation, corporate norms, and behaviours. The goal is to use generative AI to develop new tasks and enhance capabilities across various professions, including teaching, nursing, and technical trades. This approach can help reduce inequality, increase productivity, and elevate wages by enhancing the skill level and expertise of workers.

References

Acemoglu, D. et al. (2023) Can we Have Pro-Worker AI? Choosing a path of machines in service of minds. Centre for Economic Policy Research. Available at: https://cepr.org/system/files/publication-files/191183-policy_insight_123_can_we_have_pro_worker_ai_choosing_a_path_of_machines_in_service_of_minds.pdf

Eloundou, T. et al. (2023) ‘GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models’. Available at: http://arxiv.org/abs/2303.10130.

Appendix

This is the fifteen occupations identified by Eloundou as sensitive to AI. The numbers refer to the UK Standard Occupational Classification: it would be interesting to hear from any similar work undertaken in other European countries.

  1. Survey Researchers – Business and related research professionals (UK SOC code 2434)
  2. Animal Scientists – Biological scientists (2112)
  3. Climate Change Policy Analysts – Social and humanities scientists (2115)
  4. Blockchain Engineers – Programmers and software development professional (2134)
  5. Web and Digital Interface Designers – Web design professionals (2141)
  6. Financial Quantitative Analysts – Finance and investment analysts and advisers (2422)
  7. Tax Preparers – Taxation experts (2423)
  8. Mathematicians – Actuaries, economists and statisticians (2433)
  9. News Analysts, Reporters, and Journalists – Newspaper and periodical journalists and reporters (2492)
  10. Public Relations Specialist – Public relations professionals (2493)
  11. Proofreaders and Copy Markers – Authors, writers and translators (3412)
  12. Accountants and Auditors – Book-keepers, payroll managers and wages clerks (4122)
  13. Correspondence Clerks – Records clerks and assistants (4131)
  14. Clinical Data Managers – Database administrators and web content technicians (3133)
  15. Court Reporters and Simultaneous Captioners – Typists and related keyboard occupations (4217).

(Eloundou T. et al. (2023) ‘GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models’. Available at: http://arxiv.org/abs/2303.10130.

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The Digital Native Myth: A Story of Evolution

Graham Attwell 58 Views 0 Comments , , , , ,

Remember when people started talking about "digital natives" back in 2001? It was a catchy term for kids growing up surrounded by tech and the internet. The specific terms "digital native" and "digital immigrant" were popularized by education consultant Marc Prensky in his 2001 article entitled Digital Natives, Digital Immigrants, in which he relates the contemporary decline in American education to educators' failure to understand the needs of modern students. His article posited that "the arrival and rapid dissemination of digital technology in the last decade of the 20th century" had changed the way students think and process information, making it difficult for them to excel academically using the outdated teaching methods of the day. Prensky's article was not scientific and there was no research or evidence to back up his idea. But despite this, the idea caught on fast, influencing how we approached education and technology.

Researchers dug deeper and found no real evidence that an entire generation was thinking differently. You'd think that would be the end of it, right? Surprisingly, the digital native idea is still kicking around in the media and education circles. Yet, the digital natives narrative persists in popular media and the education discourse. A new study set out to investigate the reasons for the persistence of the digital native myth. It analyzed the metadata from 1886 articles related to the term between 2001 and 2022 using bibliometric methods and structural topic modeling. The results show that the concept of “digital native” is still both warmly embraced and fiercely criticized by scholars mostly from western and high income countries, and the volume of research on the topic is growing. However, interestingly the results suggest that what appears as the persistence of the idea is actually evolution and complete reinvention: The way the “digital native” concept is operationalized has shifted over time through a series of (metaphorical) mutations. The concept of digital native is one (albeit a highly successful) mutation of the generational gap discourse dating back to the early 1900s. While the initial digital native literature relied on Prensky's unvalidated claims and waned upon facing empirical challenges, subsequent versions have sought more nuanced interpretations.

The study uncovered 1,886 articles about digital natives, published between 2001 and 2022 with some interesting patterns. The authors say that what we mean by "digital native" has shifted over time. The idea is part of a bigger story and is just one chapter in a long history of talking about generational gaps. Its not going to be long before the idea mutates for those growing up in the age of AI!

Want to find out more? Listen to the podcast above or if you prefer your learning in written form download the paper below.

Mertala, P., López-Pernas, S., Vartiainen, H., Saqr, M., & Tedre, M. (2024). Digital natives in the scientific literature: A topic modelling approach. Computers in Human Behavior, 152, 108076.

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AI Procurement: key questions

Graham Attwell 105 Views 0 Comments , , , , , ,
Alexa Steinbrück / Better Images of AI / Explainable AI / CC-BY 4.0

In the AI pioneers project we are frequently asked by teachers. and. trainers in Vocational Education and Training and Adult Education what they should be looking for if they intend licensing or buying AI based applications. The UK Jisc has developed and published an AI Maturity model. "As institutions move to the ’embedded’ stage," they say "we expect appropriate processes to be in place for the entire lifecycle of AI products, including procurement."

They continue to explain that: "This detailed scrutiny aims to facilitate a better understanding and mitigation of potential risks associated with AI deployment. Additionally, it is crucial to ensure that the use of AI in educational and research settings does not infringe on IP rights and that the data used in AI models is appropriately managed to maintain research integrity and protect proprietary information."

The model includes comprehensive due diligence processes for areas such as supplier information, financial stability, insurance coverage, modern slavery compliance, information security, and data protection. By thoroughly vetting these aspects, JISC says, we aim to ensure that any solutions are not only innovative and effective but also ethical and compliant with all relevant regulations and standards. The questions are intended to be dynamic and will be reviewed to reflect advances in technology or legislation.

1Outline which AI features of your system use third party AI models, and which use your own proprietary or in-house AI models.  Please provide details of any third-party technologies used, including the name of provider and an outline of the features used.  Note that for major suppliers in the LLM supply chain, such as OpenAI, Google DeepMind, Anthropic, etc., due diligence should be conducted separately. There’s no need to request information about them from all third-party providers built on these large language models.
2Where you are either creating your own model or fine tuning a third-party model, how is performance defined and measured? Include details of initial training and monitoring over time.  (UK AI Principle: Safety, security and robustness)
3What data do your AI models require for initial training or fine tuning? If you are using third party models, you should only describe data that is unique to your application.  (UK AI Principle: Safety, security and robustness)
4a/4bIs data from user interactions with the system utilized to enhance model performance, and if so, please elaborate on the mechanisms involved? Furthermore, could you provide clarification on whether institutional data is integrated into external models?  (UK AI Principle: Safety, security and robustness)
5What features does your solution have to make it clear when the user is interacting with an AI tool or AI features?  (UK Principle: Safety, security and robustness)
6Could you please provide comprehensive information about the safety features and protections integrated into your solution to ensure safe and accessible use by all users, including those with accessibility needs and special education requirements?(UK Principle: Safety, security and robustness) 
7Can you specify any special considerations or features tailored for users under the legal majority age?UK Principle: Safety, security and robustness) 
8What explainability features does your AI system provide for in its decisions or recommendations?(UK Principle: Safety, security and robustness) 
9What steps are taken to minimize bias within models your either create or fine tune?(UK Principle: Fairness robustness) 
10Does your company have a public statement on Trustworthy AI or Responsible AI? Please link to it here.(UK Principle: Accountability and governance) 
11/ 11a/ 11b/ 11c Does your solution promote research, organizational or educational use by: A)    Not restricting the use of parts of your solution within AI tools and services B)    Not preventing institutions from making licensed solutions fully accessible to all authorized users in any legal manner; C)    Not introducing new liability on institutions, or require an institution to indemnify you especially in relation to the actions of authorized users (Gartner, Inc, ICOLC statement and legal advice obtained by Jisc)
12Does your solution adequately protect against institutional intellectual property (IP) infringement including scenarios where third parties are given access to and may harvest institutional IP?(Gartner, Inc and ICOLC statement)
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Teacher’s Digital Literacy

Graham Attwell 94 Views 0 Comments , , , , , ,
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.
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AI Pioneers Action Lab at the EDEN Digital Learning Europe Conference in Graz

Graham Attwell 123 Views 0 Comments , , , ,

Last week at the EDEN Digital Learning Europe Conference in Graz. The AI Pioneers project organised an Action Lab. I wasn't quite sure what the idea of an Action Lab was but we agreed that it seemed to imply some involvement and participation from those attending. Neither did we know exactly how many would attend given that as in other large conferences their were a considerable number of parallel sessions.

So - in the end we decided we would deliver a very short presentation as an introduction to the AI pioneers project and then split into groups to discuss three key questions:

  • The skills and competences required for using Generative AI for teaching and learning
  • The impact of Generative AI on education
  • Guidelines for future projects on using AI for learning in education

In the end nearly 30 people turned up - to a room which was better suited to 15! And we only had one hour for the Action Lab so it was quick fire stuff. Originally we had through about using a Miro board for collecting inputs but there was not enough time for that. Instead we went back to basics and collected the replies on post-it notes displayed on posters on the wall. We captured posters through mobile phone pictures.

The session was lively and a lot of fun (at least I thought so and others I talked to afterwards agreed. Of course a usual problem with a set up like this is how to record and analyse the discussion afterwards. No problems - since this is about AI I gave to to Claude 3.5 Sonnet with the following prompt:

Those stickers are responses in the question about the impact of AI in education from a workshop about AI in education. Can you extract the text from the stickers. Can you analyse and conclude ?

Here's what it came back with.

Skills and competences required for using Generative AI for teaching and learning:

  1. What do you want to achieve? WEIRD prompts
  2. GenAI can help build skills
  3. Critical analysis of outputs + ethics
  4. Domain understanding
  5. Basic computer skills
  6. TPAK (Technological Pedagogical Content Knowledge)
  7. Critical thinking and ethical skills
  8. Design documents and use appropriate AI skills, take notes to AI tools
  9. Critical reflection of output from AI - evaluative judgment
  10. Agility, willingness to explore and change
  11. Prompt design
  12. Level up pedagogical skills
  13. Play with AI, Explore, Experiment, Engage
  14. Learn how to prompt correctly

The impact of Generative AI on education:

  1. Consider how learning is assessed and how we define success
  2. Collaborative learning initiatives
  3. Leveling up of some skills or knowledge / language
  4. Reduction in the value/need for qualifications for jobs
  5. Possible increase of behaviorism-based learning
  6. Effective tool SaaS may no longer exist
  7. Opportunities to extend through AI
  8. Personalization of learning
  9. Amplifier for re-thinking Education - the way we teach
  10. Dropping old skills, developing new skills
  11. Disruption in trust
  12. Impact on our cognitive development
  13. Role in historical narratives + trust + misinformation
  14. Impact on staff workload/training on job
  15. Risks to assessment - cheating
  16. Danger of partial understanding AI as a crutch
  17. NO IMPACT!

Guidelines for future projects on using AI for learning in education:

  1. Research informed practice
  2. Co-design with students/teachers + partners
  3. Authentic Assessment
  4. Ethical Proofing
  5. AI taxonomies
  6. digital poverty
  7. Focus on innovation equity
  8. Preserve the values of education and its expertise
  9. Custom small LLMs for customized content exploration
  10. Include nudges to help people be better/happier/healthier
  11. AI as Co-Teacher + Blended Learning + Flippers
  12. AI Tutor for AI National Network of Hub AI Play fps, Research Financial, Pedagogy for all
  13. Accountability Buddy
  14. Project & support building custom kits / APIs for use cases

Analysis and Conclusion:

The sticky notes reveal a comprehensive exploration of AI's role in education, covering guidelines for implementation, necessary skills, and potential impacts. Here's an analysis of the key themes:

  1. Ethical Considerations: There's a strong emphasis on ethical implementation, including "Ethical Proofing" and preserving educational values. This suggests a cautious approach to AI adoption.
  2. Skill Development: Both educators and students need to develop new skills, such as prompt design, critical analysis of AI outputs, and understanding AI's capabilities and limitations.
  3. Personalization and Collaboration: AI is seen as a tool for personalizing learning experiences and fostering collaborative initiatives.
  4. Pedagogical Shifts: The notes indicate that AI may lead to changes in teaching methods, assessment practices, and the overall educational paradigm.
  5. Equity and Accessibility: There's concern about digital poverty and ensuring equitable access to AI-enhanced education.
  6. Potential Risks: Some notes highlight risks such as over-reliance on AI, impacts on trust, and potential for cheating.
  7. Workforce Implications: AI's impact extends beyond the classroom, potentially affecting job qualifications and workforce skills.

Conclusion: The overall sentiment towards AI in education is cautiously optimistic. While there's excitement about AI's potential to enhance learning experiences and personalize education, there's also a clear recognition of the challenges and risks involved. The focus is on responsible implementation, emphasizing the need for ethical guidelines, skill development, and maintaining human-centric values in education. The diverse perspectives represented in these notes suggest that the integration of AI in education will require careful planning, continuous assessment, and adaptability to ensure it benefits all stakeholders while mitigating potential drawbacks.