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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.
Currently, Industry 4.0 (I4.0) represents a paradigm shift that is redefining manufacturing processes globally through the integration of advanced technologies in all aspects of production. However, this technological advancement poses significant challenges in the realm of higher education, particularly in properly preparing students to meet the demands of the modern labor market. This article, written by a team of experts from the Polytechnic University of Cartagena and other European institutions, explores the deep gap between the current competencies of students and those required by Industry 4.0, especially in the wood and furniture manufacturing sector. The study is part of the European project MAKING 4.0, which aims to assess and report on the deficiencies and needs in key competencies and skills related to the Key Enabling Technologies (KETs) of I4.0. Through a thorough analysis of surveys directed at relevant actors in the sector, a low level of competencies and qualifications in this industrial sector is revealed, both in educational and industrial contexts. This research not only highlights deficiencies in current training but also emphasizes the urgent need to adapt educational curricula to close these competency gaps and effectively prepare future professionals for the challenges of Industry 4.0.
Via LGA
Abstract Purpose This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations. Design/methodology/approach The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations. Findings The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work. Originality/value This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.
In this research, we discuss the results of a survey of 221 Canadian start-ups and 17 interviews with company leaders and experts to understand how ventures are using or exploring using generative AI and the challenges they’re facing in adopting this technology.
How can generative AI help close Canada’s productivity gap? What barriers are Canadian businesses facing in deploying generative AI, and how are they overcoming these barriers? What are some ways that ventures can take advantage of generative AI’s potential?
This paper examines a possible future for postsecondary education in the age of AI. The consensus view among economists is that AI is a general purpose technology (GPT), similar to the steam engine, electricity, and the internet. As a GPT, AI will be the main driver of innovation for the foreseeable future in most sectors of the economy, including education. As AI evolves, it holds the promise of fundamentally redefining the educational landscape, influencing not only current practices in institutional management and pedagogy but also shaping future trends in learning, evaluation, and accreditation. While traditional college-aged students have received significant attention in educational studies, this paper emphasizes the needs of adult learners as lifelong learners and explores how AI-driven innovations can enhance their educational experiences, offering personalized and flexible learning solutions. This paper also argues that a dramatic breakthrough is needed in the cost–value equation for education to support workforce development and lifelong learning.
Microcredentials hold promise for connecting people’s skillsets with labour market demand in a rapidly changing world of work. They have proliferated in recent years across economic sectors and education levels, reinforcing European and national efforts to understand and develop them better.
They can increase the provision of labour-market-relevant vocational education and training (VET), supporting national, regional and sectoral upskilling and reskilling strategies, offering learners targeted training for better employment prospects, and helping employers improve employee retention and productivity. They support the modularisation of qualifications and the validation of prior learning, enabling the inclusion of the most vulnerable and lifelong learning at all levels.
Implications for the future of education Developments in generative AI raise fundamental questions for the future of education.What will be the role of teachers with this technology in wide circulation? What will assessment look like now that AI utilities can perform very well on examinations that were, until very recently, widely considered un-hackable, such as tests to demonstrate mastery of specific subject areas, and exams to credential skilled professionals, including doctors, engineers, and lawyers? Generative AI and the future of education4As a university professor, I have long considered the teaching of writing to be one of the most effective ways to cultivate and demonstrate analytical and critical thinking skills. But generative AI invites me to question such assumptions, even as I continue to hold them.In a world where generative AI systems seem to be developing new capabilities by the month, what skills, outlooks and competencies should our education systems cultivate? What changes are needed in schools and beyond to help students navigate a future where human and machine intelligence seem to be ever more closely connected – one supporting the other and vice versa? It is possible that we will soon achieve artificial general intelligence – a milestone at which machines will surpass us not only in narrow areas such as playing chess, but also in much larger ones, such as recommending actions to mitigate the dangers of climate change. What then should education look like? What will be its purpose and role in a world where humans are not necessarily the ones opening new frontiers of understanding and knowledge?These are daunting questions. They are forcing us to seriously consider concerns that we have, arguably, avoided for too long. At their most basic level, these concerns relate to the sort of world we want to live in. Our education systems often take for granted what the world looks like – and will and should look like. Our formal learning systems are designed to help people develop the competencies needed to navigate and, we hope, thrive in this known world. AI is forcing us to ask questions about the ‘known-world’ that we usually take as a starting point for education. Many of our old assumptions and norms, especially those concerning knowledge and learning, appear unlikely to sustain the ‘weight’ of this new technology. We can no longer just ask ‘How do we prepare for an AI world?’ We must go deeper: ‘What should aworld with AI look like? What roles should this powerful technology play? On whose terms? Who decides?’ Education systems need to return agency to learners and remind young people that we remain at the helm of technology. There is no predetermined course.
After more than 50 years of research on drop-out from Vocational Education and Training (VET), databases provide vast amounts of drop-out studies from around the globe that entail hundreds of potential drop-out factors. However, many scholars tend to explore the topic without detailed and theoretically grounded foundation. Several diverging research foci exist within the research field of drop-out from VET: Studies differ with regard to the analysed phase of drop-out (development, decision-making, paths after dropping out), the applied sample perspective (learners, educators, further stakeholders), and the overall research perspective that is applied based on a specific theoretical approach. Given the range of drop-out factors, stakeholders, and possible theoretical approaches within studies, this article aims at organising the research on the multi-facetted phenomenon for future endeavours by presenting a framework model that comprises a three-step recommendation: First, the model illustrates different phases (Development-, Decision-, Adjustment-Phase) of drop-out research that should be differentiated. Second, it is argued that the chosen sample perspective is highly relevant for drop-out research and must be chosen carefully. Third, the overarching disciplinary research perspectives on drop-out from VET, which scholars may adopt within their research, entail particular relevant variables and focal points and should therefore be discussed. Four examples of research perspectives (economical, psychological, sociological, pedagogical) are briefly presented, structured on a micro-, meso- and macro-level, to exemplify different theoretical accesses to drop-out research. Throughout the article, the novel organisation framework is explained and exemplified by illustrating relevant aspects of each research area and exemplary literature. The presented framework will help organising the research field and enable scholars to delimit their work more precisely, leading to a more traceable structure of further research efforts and more significant contributions to the state of knowledge.
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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.
Abstract Purpose The present study investigates the impact of digital entrepreneurial education and training and its impact on the digital entrepreneurial intention (EI) through the mediating character of entrepreneurial competence. Design/methodology/approach A total of 391 survey responses were collected from employees using convenient and snowball sampling methods. Findings Digital entrepreneurial education and training showed a positive influence on entrepreneurial competence and EI, with entrepreneurial competence mediating the relationship between digital entrepreneurial education and training practices and EI. Research limitations/implications This study is intended to assist the development of digital entrepreneurs. The implications of this study are also useful for governments, entrepreneurs, venture capitalists, angel investors and various international development institutions. Originality/value The novelty of this study relates to exploring the relationship between digital entrepreneurial education and training, entrepreneurial competence and digital EI.
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.
In this research, we examine the results of interviews with nurse educators, nursing experts, and recent nurse graduates to determine how the pandemic-driven move to virtual study affected the delivery of experiential learning.
Did nursing students learn the necessary skills required for safe and effective practice? How did the move to online learning affect student engagement and academic dishonesty? Were there benefits specific to virtual learning that can be incorporated into future teaching efforts?
Elevator pitch
Although early human capital theory recognized the relevance of workers’ experience, its focus was on education and formal training. More recent studies show that much of the performance of newly hired workers is driven by learning by doing or learning from peers or supervisors in the workplace. Descriptive data show that workers learn a lot from the various tasks they perform on the job. Informal learning at work seems to be relevant for all age groups, although it is more meaningful for younger workers’ performance. Informal learning is far more important for workers’ human capital development than formal training courses.
Key findings
Pros
Informal learning is more important to workers’ performance than formal training.
Learning by doing is often an automatic byproduct of productive work.
Informal learning is highly important for workers with temporary contracts.
New hires have a steep performance increase in their first year of employment.
Knowledge spillovers between peers in the workplace contribute to firm productivity.
Cons
Skills acquired through informal learning are less observable by other employers when compared to skills acquired through formal training.
Informal learning involves costs when less proficient workers are less productive in their jobs.
Most firms do not have adequate human resource management strategies to optimize informal learning in the workplace.
The causal effects of informal learning on worker performance are still unclear.
The economic literature on informal learning is underdeveloped.
Author's main message
Steep performance increases among new hires and the large share of work time in which workers perform tasks that impart new skills indicate that informal learning is the main driver of human capital development in the workplace. Knowledge spillovers among co-workers are also an important part of informal learning. Rapidly changing skill demands and rising retirement ages make informal learning even more important for workers’ employability throughout their work life. However, policies still tend to emphasize education and formal training, and most firms dedicate insufficient efforts on optimizing the gains from informal learning at work.
Abstract:
Technology is disrupting labor markets. We analyze the demand and reward for skills at occupation and state level across two time periods using job postings. First, we use principal components analysis to derive nine skills groups: 'collaborative leader', 'interpersonal & organized', 'big data', 'cloud computing', 'programming', 'machine learning', 'research', 'math' and 'analytical'. Second, we comment on changes in the price and demand for skills over time. Third, we analyze non-linear returns to all skills groups and their interactions. We find that 'collaborative leader' skills become significant over time and that legacy data skills are replaced over time by innovative ones.
Based on a randomized controlled trial conducted on extremely poor youths in Nepal, we report the impact of a vocational training program that offered long-duration training combined with incentives for trainers tied to trainees’ success. Furthermore, to mimic the practices in the field, a component of the program allowed trainers to select trainees from eligible applicants. For the trainees that were randomly selected, after nine months of program completion, we found no significant effect of the training on the outcomes except for employment prospects. However, we observed some improved outcomes for the trainees selected by trainers. The findings are consistent with the observed pattern
of finding a better outcome when the program implementers non-randomly select the treatment groups. Our investigation thus points out that trainee selection can provide a better outcome of vocational training.
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