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As educators and students grapple with what is allowed when using generative AI (GenAI) tools, I have compiled five tips to help you design or redesign academic integrity statements for your syllabus, assignments, exams, and course activities.
Via Peter Mellow
EdTech has been a hot button topic for some time now, and the focus on generative AI in 2023 has only amplified interest in EdTech. Proponents see a plethora of ways that AI can improve education outcomes. AI’s potential for transforming education is real, but so are the dangers—so we must move forward with care and intentionality. It is with an eye toward care and intentionality that members of W2050’s Senior Fellows committee on education and work met to discuss how AI could—and how it should—impact education in 2024 and beyond.
Via Canadian Vocational Association / Association canadienne de la formation professionnelle
The demographics of AI developers often differ from those of users. For instance, a considerable number of prominent AI companies and the datasets utilized for model training originate from Western nations, thereby reflecting Western perspectives.
Via Tom D'Amico (@TDOttawa)
Academic librarians are helping both students and instructors navigate the rapidly evolving field of artificial intelligence.
Via Peter Mellow, Bhushan Thapliyal
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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.
Via Canadian Vocational Association / Association canadienne de la formation professionnelle
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Coming soon: advances in VR and high-tech classrooms, plus even more AI. After a 2023 filled with “metaversities,” robots rolling across campuses and artificial intelligence tools spurring both anxiety and excitement, what could this coming year have in store f
Via Peter Mellow
Discover Artificial Intelligence tools for education. A comprehensive directory of AI tools that can help transform the classroom.
Via Gust MEES
ChatGPT is an Artificial Intelligence (AI) tool that has taken the world by storm, reaching 100 million users just two months after its launch. To meet the exceptionally high level of interest in this tool, UNESCO IESALC held a webinar to discuss what are its possibilities and challenges in higher...
Via Nathalie Ferret
University World News continues its exploration of generative AI in this new special report on ‘AI and Higher Education’. In commentaries and features, academics and our journalists around the world investigate issues and developments around AI that are impacting on universities. Generative AI tools are challenging and changing higher education systems and institutions — how they are run as well as ways of teaching and learning and conducting research.
Via Canadian Vocational Association / Association canadienne de la formation professionnelle
At a glance
During the pandemic (2019–22), the US labor market saw 8.6 million occupational shifts, 50 percent more than in the previous three-year period. Most involved people leaving food services, in-person sales, and office support for different occupations.
By 2030, activities that account for up to 30 percent of hours currently worked across the US economy could be automated—a trend accelerated by generative AI. However, we see generative AI enhancing the way STEM, creative, and business and legal professionals work rather than eliminating a significant number of jobs outright. Automation’s biggest effects are likely to hit other job categories. Office support, customer service, and food service employment could continue to decline.
Federal investment to address climate and infrastructure, as well as structural shifts, will also alter labor demand. The net-zero transition will shift employment away from oil, gas, and automotive manufacturing and into green industries for a modest net gain in employment. Infrastructure projects will increase demand in construction, which is already short almost 400,000 workers today. We also see increased demand for healthcare workers as the population ages, plus gains in transportation services due to e-commerce.
An additional 12 million occupational transitions may be needed by 2030. As people leave shrinking occupations, the economy could reweight toward higher-wage jobs. Workers in lower-wage jobs are up to 14 times more likely to need to change occupations than those in highest-wage positions, and most will need additional skills to do so successfully. Women are 1.5 times more likely to need to move into new occupations than men.
The United States will need workforce development on a far larger scale as well as more expansive hiring approaches from employers. Employers will need to hire for skills and competencies rather than credentials, recruit from overlooked populations (such as rural workers and people with disabilities), and deliver training that keeps pace with their evolving needs.
Via Canadian Vocational Association / Association canadienne de la formation professionnelle
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A new AI tracks college students who are missing classes, running out of money or just feeling lonely, and helps colleges reach out to intervene.
Via Peter Mellow, Tony Parkin
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we know your time is valuable and student needs are broad. With Generative AI for Educators, you’ll learn how to use generative AI tools to help you save time on everyday tasks, personalize instruction, enhance lessons and activities in creative ways, and more. Self paced course that should be completed in 1-2 hours.
Via Tom D'Amico (@TDOttawa) , michel verstrepen
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The real number of students using artificial intelligence is likely far higher, experts say, with detection tools only able to catch unsophisticated cheats.
Via Peter Mellow
Reflecting on AI and learning, a student offers four insights gleaned from firsthand interactions with ChatGPT.
Via Peter Mellow, Bhushan Thapliyal
"I recently gave a guest lecture in a Technology Leadership class for Doctoral Candidates, and in that conversation, we spent some time reflecting on the significant impact over the last two decades brought on by the exponential growth of technology. We watched a brief clip from the Today Show (1994), where the hosts were trying…"
Via Leona Ungerer
The emergence of generative artificial intelligence (AI), while creating new possibilities for learning and teaching, has exacerbated existing assessment challenges within higher education. However, there is considerable expertise, based on evidence, theory and practice, about how to design assessment for a digital world, which includes artificial intelligence. AI is not new, after all, even if the current iterations of generative AI are. This document, constructed through expert collaboration, draws on this body of knowledge and outlines directions for the future of assessment. It seeks to provide guidance for the sector on ways assessment practices can take advantage of the opportunities, and manage the risks, of AI, specifically generative AI.
Via Canadian Vocational Association / Association canadienne de la formation professionnelle
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Scientific writing is a difficult task that requires clarity, precision, and rigour. It also involves a large amount of research, analysis, and synthesis o
Via CECI Jean-François, michel verstrepen
Artificial intelligence (AI) is a daunting topic as there are so many issues with respect to its use in education. AI is also currently going through yet another period of extreme hype, currently being at the peak of inflated expectations, driven by developments in generative large language models such as ChatGPT and DALL-E. Anything written down in a textbook such as this is likely to be overtaken very quickly by new developments. Furthermore, at the time of writing, the lack of transparency and complexity of the underlying software that drives AI is a particular challenge. If you don’t know how something works, it is very difficult to control or manage it. For many AI developers, this is not a bug but a deliberate feature, but in education it is essential to understand the assumptions, biases and limitations of the software.
Via Canadian Vocational Association / Association canadienne de la formation professionnelle
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