Taccle AI

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

Wales Wide Web

News 

Edtech: Disruption or incremental change?

Graham Attwell 133 Views , , , , , ,
Yutong Liu & Kingston School of Art / Better Images of AI / Talking to AI / CC-BY 4.0

Technology evangelists and the big tech providers have long dreamed of disrupting education. That despite all the changes brought about by technology most education remains organised by institutions, many funded by the public sector is a source of frustration to the. One of the most prominent of teh change advocates is Salman Khan. Founder of the Salmon Academy, which boomed during the COVID 19 lock downs and advocate of video lessons and flipped learning, Salmon Khan was in London last week to publicize his new book, Brave New Words: how AI will revolutionise education (and why that’s a good thing), and while there he was interviewed for an article in the Times Education Supplement.

As Dan Meyer says in his newsletter Mathworlds, his remarks indicate "the edtech industry is starting to realize that the possibility of revolutionary impact with generative AI is small and the possibility of any impact will require them to operate as partners with institutions that many of them have disregarded."

Meyer points out that despite all the rhetoric "in reality Khan Academy has not transformed teaching like Khan hoped it might. In the US, as in the UK, students still typically sit at desks while a teacher delivers a lecture-style presentation, and then they complete tasks based on what they have learned." And Khan seems to agree. "“If you walk into a random classroom, for the most part it seems pretty similar to what we used to see,” he says. “If you asked me 10 years ago, I would have hoped… I mean, I’ve given TED Talks saying you shouldn’t need to give lectures any more, and everyone should be able to go at their own pace.”

And this raises the question of why the Khan Academy Hasn't ushered in a new era of education?

Well, the platform is designed to help students who are “trying to get through the [public school] system”, Khan says. “Either we support them in moments where they have a gap or we are used more systematically by their teacher, by their school, to improve the learning that goes on.”

And the public school system is far bigger than Khan Academy, he says.

The academy “needs to be pretty well integrated with the formal systems for it to have the maximum impact. That’s the journey that will keep us busy for decades to come”, its founder says.

It seems increasingly unlikely that AI alone is going to revolutionise education this t9ime round.

News 

GenAI and Assessment

Graham Attwell 163 Views , , , , , , ,

As a recent publication from the Universitat Oberta de Catalunya points out, Artificial Intelligence remains an opportunity (or an excuse) to transform assessment, curriculum, teaching, personalization and teaching competencies. This is especially so in relation to assessment with widespread concern in the academic world about the near impossibility of detecting whether or not a student has used generative AI in an assignment.

The Universitat Oberta de Catalunya article explores the potential of continuous assessment aimed at self-regulation of learning. It suggests changing the assessment approach, moving from criteria focused on the assessment of the result to criteria focused on the process of development of the activity by the students.

    Furthermore it advocates designing continuous assessment activities as part of the same learning sequence, with relationships of dependency and complementarity, instead of discrete tests and focusing the activities on the development of competencies and the assessment of progress and reflection on the learning process of each student.

    Leon Furze is a prolific contributor to LinkedIn and describes his work as "Guiding educators through the practical and ethical implications of GenAI. Consultant & Author | PhD Candidate."

    Witting from the perspective of education in Australia he says:

    When it comes to GenAI, much of the conversation in education has been focused on academic achievement, perceived threats to academic integrity, and the risk that this technology poses to written assessments. I think that vocational education actually offers some fantastic alternative forms of assessment which are less vulnerable to generative artificial intelligence. If you’re not familiar with vocational education, assessments are often incredibly rigorous, sometimes to the point where the paperwork on evaluation and assessment is significantly longer than the assessment itself.

    Vocational training, by nature, is practical and geared around skills which are needed for the particular job role or discipline being studied. Mainstream education, by contrast, is focused predominately on subjects and content.

    Furze provides examples of different types of assessment in vocational educati9n and training:

    • Observation checklists
    • Role plays
    • Scenarios
    • Workplace activities
    • Reports from employers

    He has prublished a free 60 page ebook - Rethinking Assessment for GenAI which he says covers everything from ways to update assessments, to the reasons I advise against AI detection tools. 

    News 

    Explainable AI

    Graham Attwell 169 Views , , , ,

    Alexa Steinbrück / Better Images of AI / Explainable AI / CC-BY 4.0

    The European Digital Education Hub has put out a call for members for a working group (they call them "squads") on Explainable AI. They say:

    As AI systems become increasingly influential in shaping teaching, educational outcomes and assessment, the demand for transparency and accountability in these systems has grown. Explainable AI aims to bridge the gap between complex AI algorithms on the one side and educators, learners, and administrators on the other by providing clear insights into how AI systems arrive at their conclusions.

    This transparency is crucial as it fosters trust among users, who can see and understand the rationale behind AI-driven decisions, recommendations and actions. It also empowers educators to make informed decisions about integrating AI tools into their teaching strategies. Additionally, it ensures that AI systems uphold ethical standards, mitigating potential biases and promoting fairness in educational assessments.

    I like the idea and have put in my application (membership is unpaid). But I got to thinking that we should pick up this in the AI Pioneers project. My idea is to try to write one 'explanation' about AI a week and also to publish it as a TIC TOC video. And of course I would love to involve AI Pioneer members in the whole process. As an aside we will be unveiling more interactive tools and activities for members of the network in the new few weeks.

    But here for starters is to decide what topics related to AI and education need explaining. Here is a few I jotted down on a Google doc one evening (after, I have to admit, a couple of cooling glasses of white wine). What do you think and what have I missed out? Add your ideas on the Google doc here.

    News 

    AI and Assessment

    Graham Attwell 271 Views , , , , , ,
    Image by Mohamed Hassan from Pixabay

    Maybe the panic over the impact of AI on assessment in education has died down a little, but it has been useful in that it has focused attention on the puropse of assessment and the pedagogic approaches to assessment. Simon Brookes, Executive Dean, Faculty of Creative & Cultural Industries, at the University of Portsmouth in the UK has started a new blog series on Rethinking Assessment in the Age of AI. His latest post features insights from the University of Melbourne's Centre for the Study of Higher Education. Their recent guide, "Rethinking Assessment in Response to AI" (pdf) offers a thoughtful approach to redesigning assessments that maintain academic integrity without sacrificing pedagogical value, he says.

    The guide includes seven critical strategies for improving assessment design and integrity:

    1. Shift from product to process: Focus on evaluating students' thinking processes and problem-solving approaches rather than just the final output. This could involve asking students to maintain learning journals, document their research process, or explain their reasoning in solving problems.

    2. Incorporate evaluative judgement tasks: Ask students to review or evaluate work against set criteria, encouraging higher-order thinking skills. This might include peer review exercises, critiquing published works, or assessing case studies against industry standards.

    3. Design nested or staged assessments: Create assignments that build on each other throughout the semester, allowing for feedback and adaptation. For example, a research project could be broken down into proposal, literature review, draft, and final submission stages, each informing the next.

    4. Diversify assessment formats: Use various modalities, such as videos, blogs, podcasts, and animations, which are less susceptible to AI generation. This not only makes cheating more difficult but also allows students to develop a broader range of communication skills.

    5. Create authentic, context-specific assignments: Design tasks that mirror real-world scenarios or are highly specific to the subject matter. This could involve analysing local case studies, solving problems specific to your discipline, or applying theories to current events.

    6. Include more in-class and group assignments: Incorporate collaborative learning and reduce opportunities for individual cheating. This might involve group presentations, debates, or problem-solving sessions during class.

    7. Use oral interviews: Test understanding through verbal responses to unpredictable prompts, making it difficult to use AI. This could range from viva voce examinations to informal discussions about a student's work process.

    News 

    How are jobs sensitive to AI doing: an update

    Graham Attwell 217 Views , , , , , ,
    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.