Taccle AI

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

Assessment

News 

AI and Ed: pitfalls but encouraging signs

Graham Attwell 148 Views , , , , , , , , , ,
Joahna Kuiper / Better Images of AI / Little data houses / CC-BY 4.0

In August I became hopeful that the hype around Generative AI was beginning to die down. Now I thought we might get a gap to do some serious research and thinking about the future role of AI in education. I was wrong! Come September and the outpourings on LinkedIn (though I can' really understand how such a boring social media site became the focus for these debates) grew daily. In part this may be because there has now been time for researchers to publish the results of projects actually using Gen AI, in part because the ethical issues continue to be of concern. But it may also be because of a flood of AI based applications for education are being launched almost every day. As Fengchun Miao, Chief, Unit for Technology and AI in Education at UNESCO, recently warned: "Big AI companies have been hiring chief education officers, publishing guidance for teachers, and etc. with an intention to promote hype and fictional claims on AI and to drag education and students into AI pitfalls."

He summarised five major AI pitfalls for education:

  1. Fictional hype on AI’s potentials in addressing real-world challenges
  2. Machine-centrism prevailing over human-centrism and machine agency undermining human agency
  3. Sidelining AI’s harmful impact on environment and ecosystems
  4. Covering up on the AI-driven wealth concentration and widened social inequality
  5. Downgrading AI competencies to operational skills bound to commercial AI platforms

UNESCO has published five guiding principles in their AI competency framework for students:
2.1 Fostering critical thinking on the proportionality of AI for real-world challenges
2.2 Prioritizing competencies for human-centred interaction with AI
2.3 Steering the design and use of more climate-friendly AI
2.4 Promoting inclusivity in AI competency development
2.5 Facilitating transferable AI foundations for lifelong learning

And the Council of Europe are looking at how Vocational education and Training can promote democracy (more on this to come later). At the same time the discussion on AI Literacy is gaining momentum. But in reality it is hard to see how there is going to be real progress in the use of AI for learning, while it remains the preserve of the big tech companies with their totally technocratic approach to education.

For the last year, I have been saying how the education sector needs to itself be leading developments in AI applications for learning, in a multi discipline approach bringing together technicians and scientists with teachers and educational technologists. And of course we need a better understanding of pedagogic approaches to the use of AI for learning, something largely missing from the AI tech industry. A major barrier to this has been the cost of developing Large Language Models or of deploying applications based on LLMs from the big tech companies.

That having been said there are some encouraging signs. From a technical point of view, there is a move towards small (and more accessible) language models, bench-marked near to the cutting edge models. Perhaps more importantly there is a growing understanding than the models can be far more limited in their training and be trained on high quality data for a specific application. And many of these models are being released as Open Source Software, and also there are Open Source datasets being released to train new language models. And there are some signs that the education community is itself beginning to develop applications.

AI Tutor Pro is a free app developed by Contact North | Contact Nord in Canada. They say the app enables students to:

  • Do so in almost any language of their choice
  • Learn anything, anytime, anywhere on mobile devices or computers
  • Engage in dynamic, open-ended conversations through interactive dialogue
  • Check their knowledge and skills on any topic 
  • Select introductory, intermediate and advanced levels, allowing them to grow their knowledge and skills on any topic.

And the English Department for Education has invited tenders to develop an App for Assessment, based on data that they will supply.

I find this encouraging. If you know of any applications developed with a major input from the education community, I'd like to know. Just use teh contact form on this website.

News 

The AI Assessment Scale

Graham Attwell 153 Views , , , , , ,

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.

News 

Considerations for Curriculum and Assessment design

Graham Attwell 161 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

News 

Homework Apocolypse?

Graham Attwell 179 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."

News 

AI: What do teachers want?

Graham Attwell 230 Views , , , , , , , ,
Yutong Liu & Kingston School of Art / Better Images of AI / Talking to AI / CC-BY 4.0

A quick post in follow up to my article yesterday on the proposals by the UK Department for Education to commission tech companies to develop an AI app for teachers to save them time. The Algorithm - a newsletter from MIT Technology Review picked up on this today, saying "this year, more and more educational technology companies are pitching schools on a different use of AI. Rather than scrambling to tamp down the use of it in the classroom, these companies are coaching teachers how to use AI tools to cut down on time they spend on tasks like grading, providing feedback to students, or planning lessons. They’re positioning AI as a teacher’s ultimate time saver."

The article goes on to ask how willing teachers are to turn over some of their responsibilities to an AI model? The answer, they say, really depends on the task, according to Leon Furze, an educator and PhD candidate at Deakin University who studies the impact of generative AI on writing instruction and education.

“We know from plenty of research that teacher workload actually comes from data collection and analysis, reporting, and communications,” he says. “Those are all areas where AI can help.”

Then there are a host of not-so-menial tasks that teachers are more skeptical AI can excel at. They often come down to two core teaching responsibilities: lesson planning and grading. A host of companies offer large language models that they say can generate lesson plans that conform to different curriculum standards. Some teachers, including in some California districts, have also used AI models to grade and provide feedback for essays. For these applications of AI, Furze says, many of the teachers he works with are less confident in its reliability. 

Companies promising time savings for planning and grading “is a huge red flag, because those are core parts of the profession,” he says. “Lesson planning is—or should be—thoughtful, creative, even fun.” Automated feedback for creative skills like writing is controversial too. “Students want feedback from humans, and assessment is a way for teachers to get to know students. Some feedback can be automated, but not all.”