Taccle AI

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

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Pedagogical Approaches and Google LM Notebooks

Graham Attwell 131 Views 0 Comments , , , , , ,

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.

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The AI Assessment Scale

Graham Attwell 90 Views 0 Comments , , , , , ,

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.

Jason Lodge at University of Queensland and TEQSA refers to these as the acute problems of AI, but we recognise the need for robust frameworks that also tackle the chronic problems brought on in some ways by how we approach ideas of assessment and academic integrity in education.

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.

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AI: education and learning are not the same thing

Graham Attwell 112 Views , , , , , , ,
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."

News PLEs 

AI in ED: Equity, XAI and learner agency

Graham Attwell 91 Views , , , , , , , ,
Alexa Steinbrück / Better Images of AI / Explainable AI / CC-BY 4.0

The AI in education theme continues to gather momentum, resulting in a non stop stream of journal; articles, reports, newsletters. blogs and videos. However, while not diminishing, there seem to be some subtle change in directions in the messages.

Firstly, despite many schools wary of Generative AI, there is a growing realisation that students are going to use it anyway and that the various apps claiming to check student work for AI simply don't work.

At the same time, there is an increasing focus on AI and pedagogy (perhaps linked to the increasing sophistication of Frontier Models from Gen AI but also the realisation that gimmicks like talking to an AI pretending to be someone famous from the past are just lame!). This increased focus on pedagogy is also leading to pressure to involve students. in the application of Gen AI for teaching and learning. And at recent students two ethical questions have emerged. The first is unequal access to AI applications and tools. Inside Higher Ed reports that recent research from the Public Policy Institute of California on disparate access to digital devices and the internet for K-12 students in the nation’s largest state public-school system. Put simply, they say. students who are already at an educational and digital disadvantage because of family income and first-generation constraints are becoming even more so every day as their peers embrace AI at high rates as a productivity tool—and they do not.

And while some tools will remain free, it appears that the most powerful and modern tools will increasingly come at a cost. The U.K. Jisc recently reported that access to a full suite of the most popular generative AI tools and education plug-ins currently available could cost about £1,000 (about $1,275) per year. For many students already accumulating student debt and managing the rising cost of living, paying more than $100 per month for competitive AI tools is simply not viable.

A second issue is motivation and agency for students in using AI tools. It may be that the rush to gamification, inspired by Apps like DuoLingo, is running thin. Perhaps a more subtle and sustained approach is needed to motivate learners. That may increase a focus on learner agency which in turn is being seen as linked to Explainable AI (or XAI for short). Research around Learning Analytics has pointed to the importance of students understanding the use purpose of LA but also being able to understand why the Analytics turns out as it does. And research into Personal Learning Environments has long shown the importance of learner agency in developing meta-cognition in learning. With the development of many applications for personalized learning programmes, it becomes important that learners are able to understand the reasons for their own individual learning pathways and if necessary challenge them.

While earlier debates about AI in Ed ethics, largely focused on technologies, the new debates are more focused on practices in teaching and learning using AI.

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Considerations for Curriculum and Assessment design

Graham Attwell 109 Views , , , , , , ,

Drawing on the findings of the GENIAL project, which focused on how generative AI tools are used in practice by students in real time, Dorottya Sallai, Jon Cardoso-Silva and Marcos Barreto analyse how students use these tools differently across qualitative and quantitative subjects and offer recommendations for how educators can integrate these findings into their teaching and assessment plans in a blog article, To improve their courses, Educators should respond to how students actually use AI, on the London School of Economics website.

They say their study suggests that students rely more on AI tools when they are struggling with the quantity of the reading materials or the complexity of the course content and tend to lean less on it when the pace of delivery and the content is more accessible to them. As one of the management students expressed it: “This week’s content was pretty straightforward, and I haven’t found myself using AI”

Based on these preliminary findings, they set out some practical policy recommendations for university educators in relation to course and assessment design. Given the high likelihood that students may use AI as shortcuts, they say educators must find strategies to ensure that students use these tools to enhance their learning rather than bypassing it.

They go on to propose practical Curriculum Design considerations and considerations for Assessment Design.

Curriculum Design Considerations
- Assume Student Use of GenAI: Plan with the expectation that students will use GenAI tools.
- Integrate Non-Marked Activities: Include activities that are not graded but provide feedback on AI use.
- Ensure Full Engagement: Prevent GenAI from diminishing students' engagement with the curriculum. Prepare students to progress beyond AI-generated solutions.
- Teach Critical Analysis: Emphasize the importance of finding primary sources and critically evaluating AI outputs.
- Avoid Underspecified Assignments: Do not attempt to outsmart AI by underspecifying tasks, as future models may overcome these tactics.
- Coding Course Guidance: Instruct students on problem identification and correction in coding. Highlight alternative solutions and teach high-level engineering concepts by analyzing and improving AI outputs.

Assessment Design Considerations
- Process Mapping: Visualize the learning journey with milestones and check-in points to evaluate students’ progress.
- Separate Learning from Final Product: Design continuous assessments throughout the term or incorporate documentation of the development process in end-of-course evaluations.
- Measure Individual Learning: Use in-class quizzes at various stages of the term to gauge and support individual student progress. Use these assessments to benchmark final grades against the students' learning journeys