Vocational education and training - VET

When it comes to artificial intelligence, what are we actually creating? Even those closest to its development are struggling to describe exactly where things are headed, says Microsoft AI CEO Mustafa Suleyman, one of the primary architects of the AI models many of us use today. He offers an honest and compelling new vision for the future of AI, proposing an unignorable metaphor — a new digital species — to focus attention on this extraordinary moment. (Followed by a Q&A with head of TED Chris Anderson)

Generative AI (GAI) has quickly gained a significant foothold in academia, and is now used widely for teaching, learning, and research purposes.[1] While national trends in student and faculty adoption are unclear, surveys conducted by individual institutions have found that approximately 50 percent to 65 percent of both students and faculty have used ChatGPT or one of its commercial competitors.[2] If current trends continue, in the near future GAI use will be ubiquitous, fully integrated into the core mission of colleges and universities.

Understanding the value of individual products in a now-crowded marketplace is a major challenge for end users and for university CIOs, IT departments, and others involved in decision-making.

While the full effects of this transformation are, at best, clear only in outline, 2023 saw both well-established vendors and start-ups racing to bring GAI applications optimized for use in educational contexts to market. As we write this brief, new products are appearing so rapidly that just keeping up with them is difficult, and understanding the value of individual products in a now-crowded marketplace is a major challenge for end users and for university CIOs, IT departments, and others involved in decision making about which products will be supported and/or licensed for campus users.

Since last fall, Ithaka S+R has been partnering with 19 colleges and universities from the US and Canada to assess GAI’s impact on higher education and make evidence-based, proactive decisions about how to manage the far-ranging effects of GAI.[3] As part of this project, Ithaka S+R has been cataloging GAI applications geared towards teaching, learning, and research in the higher education context. Today, we are excited to make our Product Tracking tool (https://sr.ithaka.org/our-work/generative-ai-product-tracker/) publicly available.

The Product Tracker includes a basic description of each tool, as well as information about the pricing model, key features, and other relevant details such as the large language model (LLM) or datasets behind the tool or background on the vendor. As it would be impossible to track every GAI application that might conceivably be used in higher education contexts, we have limited the Tracker to a) products marketed specifically towards faculty or student use, and b) products that appear to be in active use in teaching, learning, or research activities. At present, the Product Tracker contains data on over 100 GAI tools and applications. While we make no claims to including every relevant or potentially relevant product, the Tracker includes data on the most visible individual products on the market and is comprehensive enough to provide a landscape perspective on the market itself. The Tracker is also a living document which we update regularly to include new products or add new data about existing products. We will continue to do so for at least the remainder of 2024 if the tool retains value to the community.

This issue brief is designed to enrich the descriptive data captured in the Product Tracker. In the brief’s first section, we provide a typology of existing products and value propositions. In the second, we offer observations about what the product landscape suggests about the future of teaching, learning, and research practices, and speculations on the near-term future of the academic GAI market.

The use of AI in the workplace is exploding and your employees’ ability to discern fact from AI hallucination is increasingly imperative. Without critical thinking skills, we face a danger where falsehoods will negatively impact workplaces and consumer interactions.

This engaging session will focus on ways to increase critical thinking for employees through effective training. Learning needs to occur in the flow of work and adapt quickly to the rapidly shifting landscape. Discover new ways to frame critical thinking and how this skill is already vastly different than before the use of ChapGPT and other generative AI tools.

Reduce the threat to your organization of employees utilizing problematic work products written by generative AI by giving them new methods to question information and verify sources. See examples of how to enliven curiosity, boost learning, and strengthen accuracy and productivity across all functions of your organization. You can reap additional benefits as we create citizens who can accurately assess the validity of information on all kinds of topics from health care, to climate change, to government representation.

The release of such generative artificial intelligence (AI) tools as ChatGPT in 2022 was a major advancement in the field of AI. Two burning questions for kindergarten through grade 12 (K–12) educators are to what extent new generative AI tools will change teaching and whether they will improve learning.

The answers to these questions are not yet clear and likely will not be for some time. But to learn firsthand from educators the ways in which AI is beginning to affect teaching and learning in K–12 public schools, the authors surveyed and interviewed educators across the United States. Specifically, the authors surveyed a nationally representative sample of 1,020 teachers using RAND's American Teacher Panel in fall 2023. They also surveyed a nationally representative sample of 231 districts in fall 2023 and interviewed 11 leaders from these districts in December 2023 and January 2024. The districts the authors surveyed and the leaders they interviewed are members of the American School District Panel (ASDP). The ASDP is a research partnership between RAND and the Center on Reinventing Public Education.

The authors combine the perspectives of K–12 teachers and district leaders in this report to construct the most comprehensive picture to date of how educators are engaging with AI products and tools for teaching. Teachers reported how they actually use AI tools in their practices, and district leaders reported whether and how they are providing policies, guidance, and training on the use of AI tools.

Key Findings
As of fall 2023, 18 percent of K–12 teachers reported using AI for teaching and another 15 percent have tried AI at least once.
Middle and high school teachers and those who taught English language arts or social studies were more likely to be AI users.
Among those teachers who use AI for teaching, most were using virtual learning platforms, adaptive learning systems, and chatbots on a weekly basis.
The most common ways that teachers used AI tools were to adapt instructional content to fit the level of their students and to generate materials.
By the end of the 2023–2024 school year, 60 percent of districts plan to have trained teachers about AI use. Urban districts were the least likely to deliver such training.
In interviews, leaders described focusing more on increasing teachers' AI use and less on crafting student use policy, primarily because they saw the potential for AI to make teachers' jobs easier.

t is increasingly common to interact with products that seem “intelligent”, although the label “artificial intelligence” may have been replaced by other euphemisms. Since November 2022, with the emergence of the ChatGPT tool, there has been an exponential increase in the use of artificial intelligence in all areas. Although ChatGPT is just one of many generative artificial intelligence technologies, its impact on teaching and learning processes has been significant. This article reflects on the advantages, disadvantages, potentials, limits, and challenges of generative artificial intelligence technologies in education to avoid the biases inherent in extremist positions. To this end, a systematic review has been carried out of both the tools and the scientific production that has emerged in the six months since the appearance of ChatGPT. Generative artificial intelligence is extremely powerful and improving at an accelerated pace, but it is based on large language models with a probabilistic basis, which means that they have no capacity for reasoning or comprehension and are therefore susceptible to containing errors that need to be contrasted. On the other hand, many of the problems associated with these technologies in educational contexts already existed before their appearance, but now, due to their power, we cannot ignore them, and we must assume what our speed of response will be to analyse and incorporate these tools into our teaching practice.

Artificial intelligence (AI) can significantly contribute to the implementation of the United Nations Sustainable Development Goals (SDGs) by offering innovative solutions and enhancing the efficiency of processes aimed at achieving these goals. There is a perceived need for studies which may look at these connections. Against this background, this paper reports on a study that investigated the connections between artificial intelligence and the implementation of the UN Sustainable Development Goals (SDGs) at higher education institutions. The paper deployed a multi-methods approach. The first one was a bibliometric analysis of publications in the topic. The second method used was an assessment of a set of case studies, that illustrate how artificial intelligence is being deployed among a sample of universities in support of efforts to implement the SDGs and a survey aimed at identifying current and future trends. The data gathered allow some trends to be identified. For instance, that there is a wide range of applications of AI to sustainability in High Education Institutions (HEI), to be chosen in terms of campus operations and greening, outreach and community engagement, research, teaching and learning, and university management. Also, the paper has identified successful examples of the deployment of AI in various sustainability contexts, illustrating what are the success factors for them. Moreover, the survey identified the fact that the use of AI is quite widely spread, and is likely to increase in coming years, due to a greater demand. Finally, AI also poses several challenges, such as authenticity and ethics in assessment (case studies), ‘lack of access to software/materials’, and ‘lack of information technology training for myself/my colleagues’ (survey). Overall, AI offers a powerful toolset to accelerate and enhance the implementation of the UN SDGs. By analysing vast datasets, predicting outcomes, optimising processes, and providing new insights, AI has the potential to address complex sustainability challenges across various sectors.

AI can bring significant benefits to the workplace. In the OECD AI surveys of employers and workers, four in five workers say that AI improved their performance at work and three in five say that it increased their enjoyment of work. But the benefits of AI depend on addressing the associated risks. Taking the effect of AI into account, occupations at highest risk of automation account for about 27% of employment in OECD countries. Workers also express concerns around increased work intensity, the collection and use of data, and increasing inequality. To support the adoption of trustworthy AI in the workplace, this policy paper identifies the main risks that need to be addressed when using AI in the workplace. It identifies the main policy gaps and offers possible policy avenues specific to labour markets.