Taccle AI

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

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AI: education and learning are not the same thing

Graham Attwell 181 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."

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AI in ED: Equity, XAI and learner agency

Graham Attwell 131 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 149 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

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Homework Apocolypse?

Graham Attwell 159 Views , , , , , , ,
Catherine Breslin & Tania Duarte / Better Images of AI / AI silicon clouds collage / CC-BY 4.0

November marks two years since the release of Open AI's GPT large language model chatbot. Since then AI, or more specifically Generative AI has dominated the discourse over the future of education. And of course it has spawned hundreds of project resulting in an increasing torrent of research results. Yet on one critical issue - does the use of AI improve learning - there appears little consensus. This is probably because we have no good ways of measuring learning. Instead we use performance in tests and exams as a proxy for learning. And its probably true to say that the debates over AI are turning the heat on the use of such a proxy, just as it is on the essay as the dominant form of assessment in schools and universities.

Last week in his newsletter, One Useful thing, Ethan Mollick talked about the use of AI, cheating and learning in an article entitled 'What comes after the Homework Apocalypse'. It is probably fair to say Ethan is a big fan of AI in education.

To be clear, AI is not the root cause of cheating. Cheating happens because schoolwork is hard and high stakes. And schoolwork is hard and high stakes because learning is not always fun and forms of extrinsic motivation, like grades, are often required to get people to learn. People are exquisitely good at figuring out ways to avoid things they don’t like to do, and, as a major new analysis shows, most people don’t like mental effort. So, they delegate some of that effort to the AI. In general, I am in favor of delegating tasks to AI (the subject of my new class on MasterClass), but education is different - the effort is the point.

He postulated that fall in grades achieved by students in the USA between 2008 and 2017 had been caused by the increasing use of the Internet for homework. Students were simply copying homework answers. And in an experiment in ma high school in Turkey with students using GPT4 grades for homework went up but final exam grades fell. But giving students GPT with a basic tutor prompt for ChatGPT, instead of having them use ChatGPT on their own, boosted homework scores without lowering final exam grades. 

Ethan says this shows "we need to center teachers in the process of using AI, rather than just leaving AI to students (or to those who dream of replacing teachers entirely). We know that almost three-quarters of teachers are already using AI for work, but we have just started to learn the most effective ways for teachers to use AI."

He remains convinced to the value of Generative AI in education. The question now, he says "is not whether AI will change education, but how we will shape that change to create a more effective, equitable, and engaging learning environment for all."

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AI Competency Framework for teachers

Graham Attwell 547 Views , , , , , , ,

At last week's Digital Learning Week 2024, UNESCO formally launched two AI Competence Frameworks, one for teachers and the other for students. These frameworks aim to guide countries in supporting students and teachers to understand the potential as well as risks of AI in order to engage with it in a safe, ethical and responsible manner in education and beyond.

Above is a copy of Tim Evans popular poster  summarizing the AI Competency Framework for Teachers. He says "I've taken the extensive, lengthy report and attempted to gather my take on the 10 key points, and areas of focus." Tim has also made a copy of the poster available on Canva.