AI Competency Framework for teachers

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.

AI: What do teachers want?

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.” 

TeacherMatic

The AI pioneers project which is researching an developing approaches to the use of AI in vocational and adult education in Europe is presently working on a Toolkit including analysis of a considerable number of AI tools for education. Indeed a problem is that so many new tools and applications are being released it is hard for organisations to know what they should be trying out.

In the UK, JISC has been piloting and evaluating a number of different applications and tools in vocational colleges. Their latest report is about TeacherMatic which appears to be adapted in many UK Further Education Colleges. TeacherMatic is a generative AI-powered platform tailored for educators. It provides an extensive toolkit featuring more than 50 innovative tools designed to simplify the creation of educational content. These tools help in generating various teaching aids, such as lesson plans, quizzes, schemes of work and multiple-choice questions, without users needing to have expertise in prompt engineering. Instead, educators can issue straightforward instructions to produce or adapt existing resources, including presentations, Word documents, and PDFs. The main goal of TeacherMatic, the developers say, is to enhance teaching efficiency and lighten educators’ workloads. To allow teachers to dedicate more time to student interaction and less to repetitive tasks.

For the pilot, each participating institution received 50 licenses for 12 months, enabling around 400 participants to actively engage with and evaluate the TeacherMatic platform.

The summary of the evaluation of the pilot is as follows.

The pilot indicates that TeacherMatic can save users time and create good quality resources. Participants commended the platform for its ease of use, efficient content generation, and benefits to workload. Feedback also highlighted areas for improvement and new feature suggestions which the TeacherMatic team were very quick to take on board and where possible implement.

Participants found TeacherMatic to be user-friendly, particularly praising its easy-to-use interface and simple content generation process. The platform was noted for its instructional icons, videos, and features such as Bloom’s taxonomy, which assists in creating educational content efficiently. However, suggestions for enhancements include the ability to integrate multiple generators into a single generator. It also remains essential for users to evaluate the generated content, ensuring it is suitable and accessible to the intended audience.

TeacherMatic was well-received across institutions, for its capabilities, especially beneficial for new teaching staff and those adapting to changing course specifications. Feedback showed that TeacherMatic is particularly valuable for those previously unfamiliar with generative AI. Pricing was generally seen as reasonable, aligning with most participants’ expectations.

TeacherMatic has been well-received, with a majority of participants recognising its benefits and expressing a willingness to continue using and recommending the tool.

AI in Education – the question of hype and reality

Max Gruber / Better Images of AI / Banana / Plant / Flask / CC-BY 4.0

I have spent a lot of time over the past two weeks working on the first year evaluation report for the AI pioneers Web site. Evaluation seems to come in and out of fashion on European Commission funded projects. And now its in an upswing, partly due to the move to funding projects based on the products and results, rather than number of working days claimed.

For the AI Pioneers project, I have adopted a Participant oriented approach to the evaluation. This puts the needs of project participants as its starting point. Participants are not seen as simply the direct target group of of the project but will includes other stakeholders and potential beneficiaries. Participant-orientated evaluation looks for patterns in the data as the evaluation progresses and data is gathered in a variety of ways, using a range of techniques and culled from many different sources. Understandings grow from observation and bottom up investigation rather than rational deductive processes. The evaluator’s key role is to represent multiple realities and values rather than singular perspectives.

Hopefully we will be able to publish the Evaluation report in the early new year. But here are a few take aways, mainly gleaned from interview I undertook with each of the project partners.

The partners have a high level of commitment to the project. However the work they are able to undertake, depends to a large extent on their role in their organisations and the organisations role in the wider are of education. Pretty much all of the project partners, and I certainly concur with this sentiment, feel overwhelmed by the sheer volume of reports and discourse around AI in education and the speed of development especially around generative AI, makes it difficult to stay up to date. All the partners are using AI to some extent. Having said that there is a tendency to thing of Generative AI as being AI as a whole, and to forget about the many uses of AI which are not based on Large Language Models.

Despite the hype (as John Naughton in his Memex 1.1 newsletter pointed out this week AI is at the peak of the Gartner hype cycle, see illustration) finding actual examples of the use of AI in education and training and Adult Education is not easy.

A survey undertaken by the AI pioneers project found few vcoati0nal education and train9ing organisations in Europe had a policy on AI. There is considerable differences between different sectors - Marketing, Graphic design, computing, robotics and healthcare appear to be ahead but in many subjects and many institutions there is little actual movement in incorporating AI, either as a subject or for teaching and learning. And indeed, where there are initial projects taking place, this is often driven by enthusiastic individuals, with or without the knowledge of their managers.

This finding chimes with reports from other perspect6ives. Donald H Taylor and Egle Vinauskaite have produced a report looking at how AI is being used in workplace Learning and Development today, and concludes that it is in its infancy. "Of course, some extraordinary things are being done with AI within L&D," they say. "But our research suggests that where AI is currently being used by L&D, it is largely for the routine tasks of content creation and increased efficiency."

If there is one message L&D practitioners should take away from this report, it is that there is no need to panic – you are not falling far behind your peers, for the simple reason that very few are making major strides with AI. There is every need, however, to act, if only in a small way, to familiarize yourself with what AI has to offer.

Donald H Taylor and Egle Vinauskaite. Focus on AI in L&D, https://donaldhtaylor.co.uk/research_base/focus-on-ai-in-ld/

Indeed, for all the talk of the digital transformation in education and training, it my be that education, and certainly higher education, is remarkably resistant to the much vaunted hype of disruption and that even though AI will have a major impact it may be slower than predicted.

What does it mean to live in a world with AI?

This is the third in our series of expert interviews about AI and education.

Linda Castaneda is Associate Professor Educational Technology at the Universidad de Murcia in Spain where she teaches preservice teachers and other professionals related to learning and undertakes continuing professional development activities for teachers and university professors.

She has recently led a research project for the European Joint Research Centre around the European Frameworks and tools for technology for teachers and trainers:

  • DigCompOrg) - supporting the development of digital capacities within educational organisations;
  • DigCompEdu – the European framework for supporting teachers and trainers in the use of technology for teaching and learning;
  • SELFIE and SELFIE Work Based Learning tools for the self-assessment of readiness of both organisation and individuals for using technology for teaching and learning. m

The study involved the analysis of the use of the Frameworks in Spain to support educational digital transformation, seen as one of the European Member States with a deeper and more extensive use of the frameworks and tools, to learn from its practical experience. The aim was to extract lessons on how to adapt, apply and use the frameworks and tools to digitally transform the Spanish educational systems and to increase the educators´ digital competences together with its educational organisations´ digital capacities.

This report highlights the importance of DigCompEdu as a framework that goes beyond the instrumental view of the digital transformation of education, helping institutions to anticipate, design and structure it. SELFIE is seen as a fundamental tool for school awareness and digital planning. Furthermore, the results consolidate the evidence of diverse approaches to digital transformation, especially considering the context of Spain, where the competence of education is at the regional level.

Linda is also involved in two projects devoted to Foster DIGCOMPEDU in University Teachers, nationally and internationally.

Graham Attwell interviewed Linda Castaneda in September 2003.

Competence Frameworks

“The major problem is how to engage participants in the process of educational digital transformation. Teachers' Training is not meaningful. Students are not motivated. Teachers and trainers complain about how useful the programme is. A reason may be that the Framework of Competences – DigCompEdu is being taken as a curriculum. But before it is useful and can be applied, its contents must be localized, the jargon needs to be translated into something close to the day-to-day experience of teachers – it needs to be based on their practice and to be important for them. At the moment many teachers are not appreciating how useful the Framework could for their area of education.

We need to translate the global framework into something which they can take ownership of. We must realise that different territories, as well as different areas of knowledge – e.g.Engineers and lawyers– have little in common. Translation is needed to make it meaningful with them.

Institutions and governments are backing the use of the Frameworks because they consider a way to be connected with a global –European– vision about the future of education, and also because they are supported by European Union money. It is all about politics and impact.

Even though frameworks are a clear approach on what to do and the perspective where to go, they come from the Anglo Saxon epistemic tradition about learning and education, which focuses on course and time-based learning. Education –with a wider approach– should include informal learning from outside the institution.

Additionally, DigCompEdu is mainly based on Blooms taxonomy, but it largely ignores the issue of metacognition and agency – the power to enact self-directed learning. It is not in the framework but is in discussion of the framework.

Digital transitions

Digital transition in education is too often focused thinking on budgets and governance, not on approaches to teaching and learning. We try to implement as many devices as possible but without challenging poor pedagogical approaches. We saw the problems in that approach in the Covid emergency. Everything about digital transformation is about teaching using a digital device or online. The most advanced technology most teachers –specially at the university– know is Turnitin. This needs a professional approach, teachers are professionals of education.

The Challenge of AI in education

The challenge of AI is what it means to day-to-day work, to the human everyday life. What does it mean to live in a world with AI.

Now we have a very restrictive curriculum. There is a growing debate over how to reshape courses. We need to rethink the purpose of education for each case, and the role of teachers in that new definition of education– e.g. what is the point of teachers especially in technical education –, it is crucial redefine human roles. We need to reconfigure the role of people and think in broader terms. For instance, why do we have a shortage of teachers, can we really replace them with AI?.

This, as everything regarding transformation of education and learning, requires a strategic approach. It is not the responsibility of some, but a systemic issue that ask everyone to have a critical perspective and to redefine the elements of the institutional structures.