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Artificial intelligence gaining ground as college teaching tool

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Artificial intelligence gaining ground as college teaching tool

Dive Brief:

  • Use of artificial intelligence has become more common in the college classroom, and advocates say it can help with everything from improved writing skills to understanding DNA theory, according to the Chronicle of Higher Education.
  • Critics charge, however, that AI may be used to gloss over structural problems in higher education and result in formulaic teaching and potential threats to privacy.
  • In a broad look at the use of AI at colleges and universities, the Chronicle of Higher Education notes that it can perhaps fine-tune assignments, analyze student writing to see if it is on track and recommend prompts and organize lesson plans, adjusting them according to student understanding. AI systems allow some instructors more time for individual interaction with students giving them more information about their performance as a group and individually. Advanced systems might use machine learning to gather data and, for instance, design a better textbook or provide recommendations to medical students about the right procedure in certain circumstances.

Dive Insight:

Proponents say that professors freed from routine tasks by AI technology can further challenge students to get a deeper understanding of the material and assess them more accurately.  For instance, adaptive courseware allows a professor to use a dashboard to assess how students are doing with homework and quizzes, check their mastery of the work and then perhaps offer a weekly lesson to fill gaps. 

Beyond that, researchers are developing technology that can provide study tips based on student behaviour and work, or even guide teachers to provide teachers with feedback. Grades, researchers say, don't change much when AI is used.

Nevertheless, a 2018 Gallup-Northeastern University survey shows that of 3,297 U.S. citizens interviewed, only 22% with a bachelor’s degree said their education left them “well” or “very well prepared” to use AI in their jobs.

Critics say the technology is simply a money-saving tool that will eliminate a professor's personal touch in delivering information, assessing students and other important interactions. They also say there are privacy concerns because AI often uses large banks of data about students.

Adaptive courseware is most popular in STEM courses where it is easier to deliver content and assess understanding. Students learning about DNA in a smaller class than normal for introductory science at Arizona State University read some text, watch a video and take an assessment overnight to measure their understanding, thus providing more detail before a discussion with the professor. 

AI advocates also say it can generate more writing in a class and more peer review of writing, which has been shown to improve student skills. One program requires each student to post two questions relevant to the course, answer two others, then review one from a classmate. It also monitors efforts to keep students on track.

AI is increasingly used on other parts of the campus to automate routine systems, and experts say it is likely to grow in use in the admission process, according to a new book.

posted Jan 8 by Sokna

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+2 votes

With CBSE introducing artificial intelligence as an elective paper, students and teachers must be very excited to know how can AI help the students’ performance grow. It has been decided that the subject would be introduced in classes 8, 9 and 10 as a skill subject.

Artificial Intelligence in Schools: How AI-powered adaptive learning technology can help students

What is artificial intelligence?

Artificial intelligence is the ability of a machine to think, learn and perform tasks normally requiring human intelligence, such as visual perception, speech recognition and decision-making skills. Capabilities demonstrated by machines, including computers, from playing chess to operating cars and beyond, fall within the domain of artificial intelligence.

How AI-powered Adaptive Learning Technology brings Personalised Learning to Kids

The rapid spread of education among the masses in the industrial era made the ‘one-size-fits-all’ method of learning the most convenient one for training subsequent generations of the workforce due to lack of resources. This method of education did not cater to the interests of most students, and learning became less engaging and meaningful for them. Additionally, learning was very superficial, with learners having only a basic understanding of concepts and this led to poor retention. This problem was recognised by the 18th-century social revolutionary, Jean-Jacques Rousseau who made the following recommendations:

“Teach your scholar to observe the phenomena of nature; you will soon rouse his curiosity, but if you would have it grow, do not be in too great a hurry to satisfy this curiosity. Put the problems before him and let him solve them himself. Let him know nothing because you have told him, but because he has learnt it for himself…”

Rousseau talks about self-paced and self-styled learning methods, which came to be termed as personalised learning in the 1960s. Personalised learning includes tailoring educational content according to the learners’ strengths, needs and interests, applying competency-based progression to set the individual pace for comfortable learning, and customising instructional modes to maximise learning intake. Before technological advancement, personalised learning was possible only through one-to-one private tutorials, which could be afforded only by affluent families. The rest of the learners had to submit to ‘factory schooling’, which failed to spark interest or provide in-depth learning, thus demotivating them. However, the turn of the millennium saw technology grow by leaps and bounds and its eager adoption in upgrading several learning strategies, including personalised learning.

Adaptive tests

Personalised learning has now come within the reach of everyone through adaptive learning technology. Powered by artificial intelligence, it analyses a vast pool of data to tailor the content as per an individual’s interest and knowledge level. This is initiated with the help of adaptive tests, which accurately quantifies the knowledge of different topics of individual learners. These tests include a large pool of questions usually drawn from data collected over the years, whose difficulty level is determined on the basis of the number of students who have answered the questions correctly. Learners are first posed with a mid-level question and based on their response, the difficulty level of the next question and the subsequent ones are modified— if they answer a question of mid-level difficulty correctly, then they will be presented with a question with a higher level of difficulty, but if answered incorrectly, the system will pose a simpler question.

 

Optimal learning paths

Adaptive assessments help instructors to precisely determine where the individual learner stands at the beginning of the academic course and to measure gaps through the course of learning. These tests give detailed analytical reports of the knowledge state and learning pattern of the learner, according to which an optimal learning path is established. This constitutes the second part of adaptive learning— adaptive content. We at Next Education have designed an adaptive learning platform as part of our Next Learning Platform, which will present the optimal learning resources from our vast pool of content (simulations, real-life videos and hands-on learning tools) based on what the learner responds best to. This inspires inquiry-based learning, which ensures that students follow the best-suited learning path and attain learning goals in an optimal time.

Making personalised learning available for all

Implementing personalised learning was practically impossible before adaptive learning technology came into the scene. The two major factors affecting this were lack of qualified teachers and financial resources. Personalised instruction needs an ideal student--teacher ratio of six to eight students per teacher. Besides , most teachers lack adequate training for effective teaching. Thus, only a select few elite schools with exceptionally good teachers were able to facilitate a personalised learning environment to students.

 

herefore, in the absence of such learning opportunities in most schools, parents chose to enrol students in coaching classes or appoint private tutors. The scenario in group coaching classes was not that encouraging either. Appointing skilled private tutors might be ideal, but it is certainly be a costly affair. A private teacher would charge approximately ₹ 2,000 to ₹ 5,000 per student for one subject for a month. On the other hand, adaptive learning would cost only around ₹ 200–500 for a student per month, thereby helping students avail quality education at affordable prices. Additionally, increasing internet and digital system usage in recent years has improved access to digital education, bringing the advantages of adaptive learning to remote areas of the country.

Thus, AI-powered adaptive learning has brought personalised learning within the reach of all 21st-century learners, solving the three-fold problem of quality, cost and access that continues to plague education, especially in developing countries such as India. The continuous evolution of education technology with the help of artificial intelligence promises more wonders such as advanced language teaching tools and smart assistants in classrooms, which will change the face of education completely.

+1 vote

According to a Northeastern University/Gallup poll, most Americans are optimistic about artificial intelligence’s (AI) impact on their futures while, at the same time, expecting the net effect of AI to be an overall reduction in jobs. If we manage AI effectively, I believe it can be a net benefit to both society and the economy.

Is AI (Artificial Intelligence) a game-changer for higher Education?

The question is: How will higher education manage AI?

Unfortunately, higher education does not have a reputation for managing change effectively. Our experience is much more one of coming late to the party—and not of our own accord. We cannot and should not do this with AI.

First, much of the expertise to develop AI is coming from university laboratories, with AI hot spots in university centres such as Boston, San Francisco, Chicago, and the Research Triangle of North Carolina. If we can develop AI for businesses at home and abroad, why can’t we do the same for ourselves?

Second, many creative applications of AI have already been developed to solve problems within the university. Certainly, enrollment-management processes, as well as today’s learning management systems, look nothing like those of 20 years ago. These changes are clear applications of AI. At the end of the day, however, the application of AI within the university is quite limited.

Where are the higher-ed AI opportunities?

To find opportunities for AI growth within the university, we need to distinguish between activities that are uniquely human as opposed to those that can be computerized. Individuals excel at defining problems, distinguishing between “good” and “bad,” at idiosyncratic tasks such as detecting false positives, and in developing novel combinations not anticipated by previous experience. Computers excel at tasks that involve well-understood rules and procedures.

Furthermore, human decision making is enhanced when it occurs in groups. Social facilitation, cooperation, division of labour and the collecting of different perspectives, knowledge, and experience all combine to enhance decision making by groups.

Of course, neither individuals nor groups are without their problems. Individuals can be slow and inefficient in their decision making, to say nothing of the limits a single individual’s knowledge and experience. Likewise, groups can be guilty of premature closure, becoming too risky or too conservative because of preconceived expectations and groupthink. Much of the work of organizational psychology has focused on how to manage individual and group decision making so as to keep the good and minimize the bad.

Thus, if AI is seen not as a way to replace the individual but as a way to make individuals and groups more effective, both the impact of AI and its acceptance will be greatly improved. Today, augmented reality has greater potential for changing how we do things in higher education. Interesting examples of this concept can be found in the business world, where AI is used to facilitate human fraud detectors for banks and human translators and editors in publishing.

How can this distinction yield applications within higher education?

While MOOCs have not yielded the disruptions that many expected, they have had a significant impact on the way we deliver course materials. Lectures on most introductory topics are readily available on the web and the push for flipped classrooms is ubiquitous. These applications facilitate what individual instructors do.

Where AI can make its mark

The real impact on learning can come through learning management systems (LMSs). We have known for quite a while that we can use technology to manage classroom participation. There is much research, including my own, that shows that anonymous input systems, when added to regular or online classrooms, increase the participation of individuals who would normally shy away from raising their hands or volunteering comments.

Applications are being developed to use AI to track student questions asked in a class and direct them to answers and to other students with the same questions. The Minerva Project is so convinced of the power of such technology that class discussions occur only online—despite students living together in the same building.

Furthermore, the massive amount of data generated by LMSs has the potential to increase the effectiveness of learning. Researchers at a school where I previously worked used data on students’ online participation to identify within the first two weeks of a class which students were likely to perform poorly. They were then able to change these students’ participation patterns and thus their outcomes.

Getting faculty buy-in
The question, however, is whether such applications will be embraced by the faculty members who fear that change will result in their demise. And at their core, many faculty members believe that learning is a uniquely individual process. Until professors see AI as a means of enhancing their effectiveness, resistance will continue.

Disruptors are on the horizon. The entrance of Arizona State and Purdue into the online marketplace is significant. MBA programs are ripe for disruption; most business school deans expect the part-time MBA market to shift to online delivery in the next five years. These online platforms will accelerate the shift to AI-managed learning.

The future for AI within the university is bright. Applications will proliferate and finally disrupt the teaching paradigm. The danger is for institutions that come late to the party and not of their own accord.

in EdTech
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AI integration offers new potential to improve student outcomes and security.

Over the past several years, artificial intelligence transitioned from the movie screen to reality, and soon it will be everywhere.

The ubiquity of AI across industries leads to two key points for K–12 schools

First, K–12 schools should use current AI solutions to help with everything from classroom performance to network safety and monitoring. Second, students need to start learning how to design, manipulate and work alongside AI machines in order to build the foundation they need as they prepare to enter the workplace.

Organizing a successful AI integration and education plan will take collaboration and proper planning on the part of school decision-makers.

“AI may hold the potential to personalize instruction and learning. Yet its use in educational settings will require educators and school leaders to develop an understanding of how it can be implemented safely and smartly,” Keith Krueger, CEO of the Consortium for School Networking said in a recent statement

AI Can Act as A Multifaceted Digital Assistant

Artificial intelligence offers a helping hand to K–12 educators, staff and administrators, easing the burden of daily tasks and extending constrained resources.

Personalized learning, for example, is being rapidly adopted by educators as a key pedagogy. However, one of the most difficult parts of personalized learning is finding the time to give each student the attention he or she needs. 

AI-enabled teaching assistants and mobile applications help educators meet that challenge, using input from students to adjust course materials through educational apps, leaving teachers time to conduct the interpersonal aspects of their curricula.

AI-enabled content monitors also improve student safety. Putnam County School District adopted GoGuardian’s new content monitoring software, which uses machine learning to add context to flagged internet searches.

For example, the automated filtering solution would be able to identify a student searching about self-harm, prompting appropriate mental health intervention. 

K–12 Teachers Need to Bolster AI Curriculum

recent Gartner report predicts one in five workers will have some form of artificial intelligence as a coworker, and Forrester predicts by 2021, automation technology will account for the work of nearly 4.3 million humans worldwide. 

That means most of today’s K–12 students will enter the workforce by the time AI is well established. In order to compete, K–12 schools will need to create curricula around artificial intelligence. 

At the Montour School District in Pennsylvania, students use tablets and smart assistants like Amazon Alexa and Google Home to explore the mechanisms of machine learning.

“It’s the people that program artificial intelligence and technology that will change the future,” Justin Aglio, Montour’s director of academic achievement and district innovation, tells The Tribune-Review.

Experts realize planning AI-related curricula can be challenging, so organizations are forming to help drive K–12 education around artificial intelligence. 

The Association for the Advancement of Artificial Intelligence and the Computer Science Teachers Association recently formed the AI for K–12 Working Group, which recently drafted a report, “Envisioning AI for K–12: What should every child know about AI?” The report outlines five important ideas around the AI concepts that K–12 students should know, from how it functions to the ethics of using it. (Access the full report here.)

“It is just as important now to think about what AI education should look like in K–12, not only to ensure a more informed populace that understands the technologies they interact with every day but also to inspire the next generation of AI researchers and software developers,” the report’s authors write. “For many in this generation, AI will be an often overlooked, magical force that powers their lives much as electricity, the internal combustion engine, and networking technology power ours.”

+1 vote

Artificial intelligence (AI) and emerging technologies (ET) are poised to transform modern society in profound ways. As with electricity in the last century, AI is an enabling technology that will animate everyday products and communications, endowing everything from cars to cameras with the ability to interact with the world around them, and with each other. These developments are just the beginning, and as AI/ET matures, it will have sweeping impacts on our work, security, politics, and very lives.

These technologies are already impacting the world around us, as Darrell West and I wrote in our April 2018 piece “How artificial intelligence is transforming the world,” and I highly recommend that anyone just discovering the topic of AI policy read it thoroughly. There, Darrell and I describe several important implications related to AI/ET, but chief among them is that these technology developments are on the cusp of ushering in a true revolution in human affairs at an increasingly fast pace.

As AI continues to influence and shape existing industries and allows new ones to take root, its macro-level impact, particularly in the realm of economics, will become more and more apparent. Control over the research and development of AI will become increasingly vital, and the winners of this upcoming AI-defined era in human history will be the countries and companies that can create the most powerful algorithms, assemble the most talent, collect the most data, and marshal the most computing power. This is the next great technology race of our generation and the stakes are high, particularly for the United States. If American society is to embrace the full range of social and political changes that these technologies will introduce, then it is the education and training we provide our youth and workers that will fuel the engines of future AI, and therefore geopolitical success.

It is the education and training we provide our youth and workers that will fuel the engines of future AI, and therefore geopolitical success.

I’ve studied and written extensively about the effects of AI/ET on the evolving character of war toward a concept I’ve called hyperwar—or, a new era of warfare in which, through AI, the speed of decision-making is faster than anything that has come before. At a superficial level, this topic often devolves into a discussion of “killer robots,” or at the very least, the impending use of AI in lethal autonomous weaponry. While those discussions are relevant and inextricably linked, they represent a narrow understanding of the greater issues at hand. The concern over AI’s potential or theoretical military applications must not distract us from how far-reaching the impact of AI will be in nearly all other policy domains. Health care, education, agriculture, energy, finance, and yes, national security, will all be reshaped in some way by AI—with education being the pivot point around which the future of the United States revolves. This is not solely a matter of social redress, but, in fact, a larger national issue.

A future in which the United States is second in the race for AI technology would create a situation of national technological and digital/cyber inferiority, which could in turn result in national strategic subservience.

The way we use education to prepare our next generation of leaders will directly determine whether the U.S. retains its leadership in critical fields of relevance in the emerging digital environment. Without a sufficiently educated population and workforce, the U.S. likely will slip behind other states for whom AI/ET is not only meant for improved social organization, but for strategic superiority, and ultimately digital and physical conquest. A future in which the United States is second in the race for AI technology would create a situation of national technological and digital/cyber inferiority, which could, in turn, result in national strategic subservience—something simply unimaginable.

Many Americans grew up with the understanding that the American capacity to fight and win a nuclear war was defined by its superiority in the Strategic Triad, the three legs of our strategic deterrence: our missile squadrons, our bomber fleet, and our ballistic missile submarines.  Behind that dizzying array of hardware was the undisputed power of U.S. intellectual and technical capabilities, and behind that was a near unlimited supply of talented engineers, each trained by a system of education undisputed in its excellence. That system was built from the ground up to produce crucial STEM (science, technology, engineering, and math) protégés in the quantities needed to ensure American strategic superiority, which contributed directly to the U.S. and its allies prevail in the Cold War. For the health of our American way of life, our competitive advantage, and the strategic security of our nation, the basis for tomorrow’s system of education must reflect a deliberately tuned and calibrated system that proactively emphasizes AI/ET, big data analytics, and super-computing.

Unfortunately, in both relative and absolute terms, the U.S. is falling behind in the race for superiority in these key technologies. Where the U.S. strategic advantage of the 20th Century was secured by American nuclear superiority, U.S. superiority in the 21st Century will likely be preserved, safeguarded, and sustained through a system of education that envisages the changes necessary and sufficient to embrace and apply relevant technologies. It will also be underwritten by educators who grasp the profound shifts in the pedagogical skills essential to the educational needs of the 21st Century.

in EdTech
+1 vote

The University of Oxford has become the first UK institution to top Times Higher Education’s computer science and engineering and technology subject rankings.

Oxford overtook three prestigious US universities renowned for their strength in technology to take pole position in the two tables. In the computer science ranking, it outperformed Stanford University, which fell two places to third, and Massachusetts Institute of Technology, which dropped three places to fifth.

Top US technology universities lose ground in computer science and engineering

Meanwhile, in engineering and technology, Oxford achieved a higher overall score than Stanford, which dropped one place to second, and the California Institute of Technology, which fell two places to fourth.

Oxford is not the only European success story at the top of the computer science ranking; ETH Zurich rose two places to second, while the University of Cambridge climbed one place to fourth. However, in the engineering table, Cambridge dropped one place to sixth, while ETH remained at ninth. 


Best universities for computer science degrees
Best universities for engineering and technology degrees
Browse Times Higher Education’s university rankings portfolio


Oxford improved its scores for a teaching environment and industry income in both tables, and saw a boost in its scores for research environment and international outlook in the engineering and technology ranking.

In contrast, both Stanford and Caltech received lower scores for research environment in the engineering table while Stanford and MIT received lower scores for the teaching environment, research environment and industry income in the computer science list.

This year, THE made a small adjustment to the eligibility criterion for academic staff in the subject tables, which explains why there are some highly ranked new entrants in the lists. For example, Harvard University has joined the engineering table in the third place.


World University Rankings 2019 by subject: computer science methodology
World University Rankings 2019 by subject: engineering and technology methodology


THE’s 11 subject rankings have each been expanded this year. The computer science ranking now includes 684 universities, up from 301 last year, while the engineering and technology ranking includes 903 institutions, up from 501.

The subject rankings are based on the same range of 13 performance indicators used in the overall THE World University Rankings 2019, but the methodologies have been recalibrated to suit the individual fields.

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