he two highest-ranked spots went to skills that didn’t appear at all on WEF’s previous list: 1) analytical thinking and innovation, and 2) active learning and learning strategies. Another skill cluster that didn’t make the previous list debuted at No. 5 — resilience, stress tolerance, and flexibility.
“The pace of technology adoption is expected to remain unabated and may accelerate in some areas,” including the use of robots and artificial intelligence, the report said. Most businesses — 84 percent — plan to accelerate the digitalization of work processes and the use of digital tools, such as video conferencing,
Elon Musk’s brain-computer startup is getting ready to blow your mind
Musk reckons his brain-computer interface could one day help humans merge with AI, record their memories, or download their consciousness. Could he be right?
The idea is to solve these problems with an implantable digital device that can interpret, and possibly alter, the electrical signals made by neurons in the brain.
the latest iteration of the company’s hardware: a small, circular device that attaches to the surface of the brain, gathering data from the cortex and passing it on to external computing systems for analysis.
Several different types of working brain-computer interfaces already exist, gathering data on electrical signals from the user’s brain and translating them into data that can be interpreted by machines.
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If we put computers in our brains, strange things might happen to our minds
Using a brain-computer interface can fundamentally change our grey matter, a view of ourselves and even how fast our brains can change the world.
Last year, Australia’s Chief Scientist Alan Finkel suggested that we in Australia should become “human custodians”. This would mean being leaders in technological development, ethics, and human rights.
A recent report from the Australian Council of Learned Academies (ACOLA) brought together experts from scientific and technical fields as well as the humanities, arts and social sciences to examine key issues arising from artificial intelligence.
A similar vision drives Stanford University’s Institute for Human-Centered Artificial Intelligence. The institute brings together researchers from the humanities, education, law, medicine, business and STEM to study and develop “human-centred” AI technologies.
Meanwhile, across the Atlantic, the Future of Humanity Institute at the University of Oxford similarly investigates “big-picture questions” to ensure “a long and flourishing future for humanity”.
The IT sector is also wrestling with the ethical issues raised by rapid technological advancement. Microsoft’s Brad Smith and Harry Shum wrote in their 2018 book The Future Computed that one of their “most important conclusions” was that the humanities and social sciences have a crucial role to play in confronting the challenges raised by AI
Without training in ethics, human rights and social justice, the people who develop the technologies that will shape our future could make poor decisions.
Got a new open access article out on the ways AI is embedding in education research. Well-funded precision education experts and learning engineers aim to collect psychodata, brain data and biodata as evidence of the embodied substrates of learning. https://t.co/CbdHReXUiz
This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.
Research across the sciences, humanities and social sciences is increasingly conducted through digital knowledge machines that are reconfiguring the ways knowledge is generated, circulated and used (Meyer and Schroeder, 2015).
Knowledge infrastructures, such as those of statistical institutes or research-intensive universities, have undergone significant digital transformation with the arrival of data-intensive technologies, with knowledge production now enacted in myriad settings, from academic laboratories and research institutes to commercial research and development studios, think tanks and consultancies. Datafied knowledge infrastructures have become hubs of command and control over the creation, analysis and exchange of data (Bigo et al., 2019).
The combination of AI and learning science into an AILSci research assemblage consists of particular forms of scientific expertise embodied by knowledge actors – individuals and organizations – identified by categories including science of learning, AIED, precision education and learning engineering.
Precision education overtly uses psychological, neurological and genomic data to tailor or personalize learning around the unique needs of the individual (Williamson, 2019). Precision education approaches include cognitive tracking, behavioural monitoring, brain imaging and DNA analysis.
Expert power is therefore claimed by those who can perform big data analyses, especially those able to translate and narrate the data for various audiences. Likewise, expert power in education is now claimed by those who can enact data-intensive science of learning, precision education and learning engineering research and development, and translate AILSci findings into knowledge for application in policy and practitioner settings.
the thinking of a thinking infrastructure is not merely a conscious human cognitive process, but relationally performed across humans and socio-material strata, wherein interconnected technical devices and other forms ‘organize thinking and thought and direct action’.
As an infrastructure for AILSci analyses, these technologies at least partly structure how experts think: they generate new understandings and knowledge about processes of education and learning that are only thinkable and knowable due to the computational machinery of the research enterprise.
Big data-based molecular genetics studies are part of a bioinformatics-led transformation of biomedical sciences based on analysing exceptional volumes of data (Parry and Greenhough, 2018), which has transformed the biological sciences to focus on structured and computable data rather than embodied evidence itself.
Isin and Ruppert (2019) have recently conceptualized an emergent form of power that they characterize as sensory power. Building on Foucault, they note how sovereign power gradually metamorphosed into disciplinary power and biopolitical forms of statistical regulation over bodies and populations. Sensory power marks a shift to practices of data-intensive sensing, and to the quantified tracking, recording and representing of living pulses, movements and sentiments through devices such as wearable fitness monitors, online natural-language processing and behaviour-tracking apps. Davies (2019: 515–20) designates these as ‘techno-somatic real-time sensing’ technologies that capture the ‘rhythms’ and ‘metronomic vitality’ of human bodies, and bring about ‘new cyborg-type assemblages of bodies, codes, screens and machines’ in a ‘constant cybernetic loop of action, feedback and adaptation’.
Techno-somatic modes of neural sensing, using neurotechnologies for brain imaging and neural analysis, are the next frontier in AILSci. Real-time brainwave sensing is being developed and trialled in multiple expert settings.
International Data Corporation says it expects the number of AI jobs globally to grow 16% this year.
a new report released Wednesday, IBM found the majority (85%) of AI professionals think the industry has become more diverse over recent years
3,200 people surveyed across North America, Europe and India, 86% said they are now confident in AI systems’ ability to make decisions without bias.
A plurality of men (46%) said they became interested in a tech career in high school or earlier, while a majority of women (53%) only considered it a possible path during their undergraduate degree or grad school.
Algorithmic test proctoring’s settings have discriminatory consequences across multiple identities and serious privacy implications.
While racist technology calibrated for white skin isn’t new (everything from photography to soap dispensers do this), we see it deployed through face detection and facial recognition used by algorithmic proctoring systems.
While some test proctoring companies develop their own facial recognition software, most purchase software developed by other companies, but these technologies generally function similarly and have shown a consistent inability to identify people with darker skin or even tell the difference between Chinese people. Facial recognition literally encodes the invisibility of Black people and the racist stereotype that all Asian people look the same.
As Os Keyes has demonstrated, facial recognition has a terrible history with gender. This means that a software asking students to verify their identity is compromising for students who identify as trans, non-binary, or express their gender in ways counter to cis/heteronormativity.
These features and settings create a system of asymmetric surveillance and lack of accountability, things which have always created a risk for abuse and sexual harassment. Technologies like these have a long history of being abused, largely by heterosexual men at the expense of women’s bodies, privacy, and dignity.
my note: I am repeating this for years
Sean Michael Morris and Jesse Stommel’s ongoing critique of Turnitin, a plagiarism detection software, outlines exactly how this logic operates in ed-tech and higher education: 1) don’t trust students, 2) surveil them, 3) ignore the complexity of writing and citation, and 4) monetize the data.
Technological Solutionism
Cheating is not a technological problem, but a social and pedagogical problem.
Our habit of believing that technology will solve pedagogical problems is endemic to narratives produced by the ed-tech community and, as Audrey Watters writes, is tied to the Silicon Valley culture that often funds it. Scholars have been dismantling the narrative of technological solutionism and neutrality for some time now. In her book “Algorithms of Oppression,” Safiya Umoja Noble demonstrates how the algorithms that are responsible for Google Search amplify and “reinforce oppressive social relationships and enact new modes of racial profiling.”
Anna Lauren Hoffmann, who coined the term “data violence” to describe the impact harmful technological systems have on people and how these systems retain the appearance of objectivity despite the disproportionate harm they inflict on marginalized communities.
This system of measuring bodies and behaviors, associating certain bodies and behaviors with desirability and others with inferiority, engages in what Lennard J. Davis calls the Eugenic Gaze.
Higher education is deeply complicit in the eugenics movement. Nazism borrowed many of its ideas about racial purity from the American school of eugenics, and universities were instrumental in supporting eugenics research by publishing copious literature on it, establishing endowed professorships, institutes, and scholarly societies that spearheaded eugenic research and propaganda.
Information literacies (media literacy, Research Literacy, digital literacy, visual literacy, financial literacy, health literacy, cyber wellness, infographics, information behavior, trans-literacy, post-literacy)
Information Literacy and academic libraries
Information Literacy and adult education
Information Literacy and blended learning
Information Literacy and distance learning
Information Literacy and mobile devices
Information Literacy and Gamification
Information Literacy and public libraries
Information Literacy in Primary and Secondary Schools
Information Literacy and the Knowledge Economy
Information Literacy and Lifelong Learning
Information Literacy and the Information Society
Information Literacy and the Multimedia Society
Information Literacy and the Digital Society
Information Literacy in the modern world (e.g trends, emerging technologies and innovation, growth of digital resources, digital reference tools, reference services).
The future of Information Literacy
Workplace Information Literacy
Librarians as support to the lifelong learning process
Digital literacy, Digital Citizenship
Digital pedagogy and Information Literacy
Information Literacy Needs in the Electronic Resource Environment
Integrating Information Literacy into the curriculum
Putting Information Literacy theory into practice
Information Literacy training and instruction
Instructional design and performance for Information Literacy (e.g. teaching practice, session design, lesson plans)
Information Literacy and online learning (e.g. self-paced IL modules, online courses, Library Guides)
Information Literacy and Virtual Learning Environments
Supporting users need through library 2.0 and beyond
Digital empowerment and reference work
Information Literacy across the disciplines
Information Literacy and digital preservation
Innovative IL approaches
Student engagement with Information Literacy
Action Literacy
Information Literacy, Copyright and Intellectual Property
Information Literacy and Academic Writing
Media and Information Literacy – theoretical approaches (standards, assessment, collaboration, etc.)
The Digital Competence Framework 2.0
Information Literacy theory (models, standards, indicators, Moscow Declaration etc.)
Information Literacy and Artificial intelligence
Information Literacy and information behavior
Information Literacy and reference services: cyber reference services, virtual reference services, mobile reference services
Information Literacy cultural and contextual approaches
Information Literacy and Threshold concepts
Information Literacy evaluation and assessment
Information Literacy in different cultures and countries including national studies
Information Literacy project management
Measuring in Information Literacy instruction assessment
New aspects of education/strategic planning, policy, and advocacy for Information Literacy in a digital age
Information Literacy and the Digital Divide
Policy and Planning for Information Literacy
Branding, promotion and marketing for Information Literacy
Cross –sectorial; and interdisciplinary collaboration and partnerships for Information Literacy
Leadership and Governance for Information Literacy
Strategic planning for IL
Strategies in e-learning to promote self-directed and sustainable learning in the area of Information Literacy skills.
Neck and neck for the top spot in the LMS academic vendor race are Blackboard—the early entry and once-dominant player—and coming-up quickly from behind, the relatively new contender, Canvas, each serving about 6.5 million students . The LMS market today is valued at $9.2 billion.
Digital Authoring Systems
Faced with increasingly complex communication technologies—voice, video, multimedia, animation—university faculty, expert in their own disciplines, find themselves technically perplexed, largely unprepared to build digital courses.
instructional designers, long employed by industry, joined online academic teams, working closely with faculty to upload and integrate interactive and engaging content.
nstructional designers, as part of their skillset, turned to digital authoring systems, software introduced to stimulate engagement, encouraging virtual students to interface actively with digital materials, often by tapping at a keyboard or touching the screen as in a video game. Most authoring software also integrates assessment tools, testing learning outcomes.
With authoring software, instructional designers can steer online students through a mixtape of digital content—videos, graphs, weblinks, PDFs, drag-and-drop activities, PowerPoint slides, quizzes, survey tools and so on. Some of the systems also offer video editing, recording and screen downloading options
Adaptive Learning
As with a pinwheel set in motion, insights from many disciplines—artificial intelligence, cognitive science, linguistics, educational psychology and data analytics—have come together to form a relatively new field known as learning science, propelling advances in a new personalized practice—adaptive learning.
MOOCs
Of the top providers, Coursera, the Wall Street-financed company that grew out of the Stanford breakthrough, is the champion with 37 million learners, followed by edX, an MIT-Harvard joint venture, with 18 million. Launched in 2013, XuetangX, the Chinese platform in third place, claims 18 million.
Former Yale President Rick Levin, who served as Coursera’s CEO for a few years, speaking by phone last week, was optimistic about the role MOOCs will play in the digital economy. “The biggest surprise,” Levin argued, “is how strongly MOOCs have been accepted in the corporate world to up-skill employees, especially as the workforce is being transformed by job displacement. It’s the right time for MOOCs to play a major role.”
In virtual education, pedagogy, not technology, drives the metamorphosis from absence to presence, illusion into reality. Skilled online instruction that introduces peer-to-peer learning, virtual teamwork and other pedagogical innovations stimulate active learning. Online learning is not just another edtech product, but an innovative teaching practice. It’s a mistake to think of digital education merely as a device you switch on and off like a garage door.
