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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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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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Adoption of artificial intelligence is on the rise: According to research firm Gartner, 37 percent of organizations have now “implemented AI in some form,” and adoption is up 270 percent over the past four years. 

Schools are following suit: Technavio’s “Artificial Intelligence Market in the US Education Sector 2018-2022” report predicts a nearly 48 percent growth rate for AI tools over the next three years. 

The challenge? Separating market interest from authentic impact. As noted by MIT Technology Review, the rapid development and uptake of AI solutions has created an environment where companies may “obfuscate and oversell” AI abilities even as organizations race to implement new solutions and keep up with the competition.

The key to AI success is specificity. It is crucial to define key needs AI tools can meet and shortcomings it can address. This is especially true for K–12 institutions faced with limited time and budgets.

5 Roles AI Solutions Have in K–12 Classrooms

There’s no rulebook for deploying artificial intelligence in schools. While solutions such as IBM’s Watson Education and Google’s recently announced Google Cloud Platform storage and AI initiativeoffers the potential for schools to deliver personalized learning strategies and offer analytics-based performance insight; how K–12 institutions leverage these tools is entirely up to them. 

Some of the most common concerns are around human bias and overall accuracy. As RAND senior policy researcher Robert Murphy notes in a post for Education Week, “maybe 10 percent, 20 percent, 40 percent of the time [the system] will get it wrong,” making AI in schools an excellent supplemental tool, but no replacement for teachers. 

As noted by Education Technology, the increasing use of AI in schools also brings ethical questions to the fore. Organizations must now consider what type of data is being collected, how this information is being used and what controls are in place to safeguard student privacy. In addition, administrators are worried about how increasing AI adoption will impact human staffing.

The best bet for schools adopting AI is to define how these solutions can drive positive outcomes before allowing them access to student data. While individual usage may vary, effective AI in education can be grouped into five general roles:

  1. Automation: The simplest application of AI often provides the most immediate benefit: By automating straightforward tasks such as grading, digital asset categorization or timetable scheduling, educators can increase the amount of time they spend actively engaging with students.

  2. Integration: AI solutions can integrate with other IT initiatives such as smart technology and IoT-driven networks to provide personalized learning solutions for students.

  3. Acclimation: Technology is now an integral part of both educational and business environments. Recent Pew Research data shows that 95 percent of teens have access to a smartphone and 45 percent are online “almost constantly.” AI in schools can help acclimate students to the pace of technological change.

  4. Delineation: Students’ needs and curriculum priorities are constantly shifting, making it difficult for educators to ensure the content they deliver remains relevant and actionable. AI-driven analytics in education can help spot critical trends and delineate key markers to help teachers design the most effective classroom experience and drive digital transformation.

  5. Identification: Data analytics informed by adaptative AI solutions can help identify critical areas for student and teacher performance. Combined with robust security and access controls, AI can help spot and remedy potential problems in their formative stages.

How K–12 Schools Are Using AI Tools Now

So, what does this look like in practice? 

In Florida’s Putnam County School District, educators are leveraging new content monitoring software to both automate the process of flagging potentially sensitive internet searches and add critical context to flagged requests. 

And in New Jersey, Slackwood Elementary School is using an AI-assisted teaching assistant called Happy Numbers to identify where students are struggling with math benchmarks and provide personalized assistance.

Solutions such as the Presentation Translator — a free PowerPoint plug-in — provide real-time integration of multilanguage subtitles to help students better understand instructions in class or provide remote access for those dealing with illness or other family concerns.

Put simply, the current climate of AI in schools one of hopeful hesitation. While technology is rapidly advancing, educators are taking a step-by-step approach to ensure the solutions they adopt deliver specific outcomes to address existing needs.

What Does the Future Hold for AI in the Classroom?

As artificial intelligence solutions inch ever closer to replicating the basics of authentic human thought patterns, what’s the impact for education? Broadly speaking, next steps for AI in school take three key forms:

  1. Personalizing performance: Increased processing power and sophistication will empower AI solutions to better collect and extrapolate information, in turn helping educators create personalized learning plans for each student. New solutions such as Brightspace Insights are designed to capture, aggregate and analyze data from multiple sources, allowing teachers to gain student insight “based on the entire ecosystem of learning tools” according to Nick Oddson, senior vice president of product development for D2L, the creator of Brightspace.

  2. Breaking bias: Human bias remains a stumbling block in education — and, as noted above, is also an emerging concern for AI tools. The future of AI in schools will leverage solutions capable of grading papers and evaluating exams using established rubrics and benchmarks to both automate completion and eliminate bias.

  3. Aggregating assistance: Educators are typically masters of their craft; many have multiple degrees and often specialize in niche areas of student development and performance. The problem is that necessary administrative work often frustrates teacher efforts to engage with students. Here, the future of classroom intelligence takes the form of AI-driven assistants that deliver essential data to help teachers do what they do best: connect with students.

Schools Must Balance AI Effectively with Security

AI in education has reached a tipping point. Adoption is on the rise, but concerns around bias, privacy and human-machine partnerships necessitate a methodical and measured approach. 

Current applications leverage machine learning and real-time data integration, but the future of AI depends on broader applications capable of adaptively applying practical intelligence to empower the critical emotional connections that drive student performance.

in EdTech
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