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How blockchain, virtual assistants and AI are changing higher ed

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Dive Brief:

  • In the coming years, advanced technologies like mixed reality, artificial intelligence (AI), blockchain and virtual assistants could play a bigger role at colleges and universities, according to a new report from Educause, a nonprofit focused on IT's role in higher ed.

  • The 2019 Horizon Report, based on a panel of higher ed experts, zeroes in on trends, challenges and developments in educational technology. Challenges range from the "solvable," such as improving digital fluency and increasing demand for digital learning experiences, to the "wicked." The latter includes rethinking teaching and advancing digital equity.

  • The panel contemplated blockchain's use in higher ed for the first time in the 2019 report. Specifically, the authors looked at its potential for creating alternative forms of academic records that "could follow students from one institution to another, serving as verifiable evidence of learning and enabling simpler transfer of credits across institutions."

Dive Insight:

The Educause report looks broadly at how a variety of technologies might become more woven into the fabric of higher ed and help address some of its long-term challenges.

With blockchain, the authors considered how it might fit in a broader push toward lifelong learning by recording and verifying student learning in school, in the workplace and in other contexts. The technology could "could provide the means for individual students to maintain an accurate record of their knowledge and skills," they write. "This could be invaluable, particularly for students who transfer among several institutions or those who want to transition, for example, from military service into higher education and the civilian workplace."

The push for digital credentials to be more easily shareable and verifiable through technology such as blockchain is picking up steam. Earlier this week, nine universities announced an initiative to explore the development of digital academic records, including badges, certifications, internships and traditional degrees, and the use of technologies such as blockchain to support them.

One of the participants, the Massachusetts Institute of Technology, piloted the concept in 2017 by giving about 100 of its graduates a digital version of their degrees, which are verified against the blockchain and shareable via a companion mobile app.

This year's Educause report also examines the emergence of virtual assistants. Although they are primarily a consumer technology — such as Amazon's Alexa and the Google Home — a handful of universities have adapted them to support students, such as by helping them with academic and financial aid advising. The report notes Northeastern University's "Husky Helper" virtual assistant, which responds to the top 20 questions students asked the university's call center over the past three years.

As AI and virtual assistants' conversational capacity advance, applications in higher ed could come to include research, tutoring, writing and editing, as well as in adaptive learning platforms. Some, though, are concerned about the technology's passive listening capabilities and other privacy concerns. As with blockchain, the authors gauge the time to adoption of the technology at four to five years away.

Colleges are already using AI more broadly to deliver digital push notifications to students to help them complete their work and to monitor learning development, the report notes. It also holds the potential to make college more accessible, including for students with disabilities, such as by scanning class materials for accessibility issues, improving learning tools and creating personalized resources.

As technology takes over campuses, college operations and learning itself, one of the hardest-to-solve challenges that the report identifies becomes all the more relevant: digital equity. The report notes survey data showing 7% of students had no or poor access to internet at home in the last 12 months, and access to broadband remains unequal across the globe.

posted May 3 by Lita

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+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
+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.

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