Chatbots are about to change the world in more ways than we can imagine. Already, bots around the globe can complete a diverse set of varying tasks. From ordering pizza online to conducting AI-driven job interviews, chatbots are about to become a normal element in everyday life.

As the scope of chatbots becomes broader every day, there are new applications popping up constantly. Education has traditionally been known as a sector where innovation moves slowly. During the most recent years, there has been a large hype over innovative tools to enhance teaching and learning through educational technology.

The time has come for chatbots and artificial intelligence to meet the educational sector. Already, there’s a lot happening but there is no question technology will have an even deeper reach in the near future.

We’ve come across six applications of both chatbots and artificial intelligence within the educational area that could have an astounding impact on the whole industry.

1. Automatic Essay Scoring

Giving feedback on individual written essays is an enormously time demanding task that many teachers struggle with, and in massive open online courses, the problem is even larger. Because there are often over 1000 students in one class, there’s simply no realistic way to give individual feedback to written essays.

To combat this problem, innovators have been flirting with the artificial intelligence (AI) industry, and a solution is relatively close at hand.

By feeding a machine-learning algorithm thousands and thousands of essays, many people believe there’s a good chance of replacing human feedback on essays with AI systems. The project has made rapid improvement since 2012 when The Hewlett Foundation sponsored a competition between essay grading systems. The winner presented a 0.81 correlation, on average, with human graders.

Since then, researchers and scholars have continued accelerating and improving systems in full force. One report claims to have achieved a 0.945 correlation on the same data as in the Hewlett competition. Astonishing!

However, there’s also strong opposition towards relying on only technology when setting grades. Skeptic Les Perelman has set out to expose the true nature of grading algorithms and has successfully managed to point out weak points amongst auto-grading vendors.

How will auto-grading turn out? The future is not yet clear, but realistically there should be a chance of replacing at least one or two of the necessary graders with AI in a few years time.

2. Spaced Interval Learning

Repeating old lessons right when you are about to forget them is an optimal learning super-hack. It’s called The Spacing Effect.

Polish inventor Piotr Wozniak has come up with a learning app built around the spacing effect. This app keeps track of what you learn and when you learn it. By incorporating artificial intelligence, the app is able to learn when you are most likely to forget information and remind you to repeat it. It only takes a couple of repetitions to make sure the information sticks for years to come.

Instead of students studying intensely before finals only to forget everything a few weeks later, schools and universities should aim for more lasting knowledge retention using this method.

Sadly, findings like the spacing effect have had a small impact on the educational system, which lives up to its reputation of being a sector of slow adoption of technology and innovation.

3. Conversational Course Assessments & Student Ratings

Student evaluations of teaching-surveys have been around for almost 100 years. Despite moving from paper to online surveys, there has been minimal progress to make the feedback process better in any way.

As student evaluations of teaching are often the most valued source of information, it’s obvious that they need improvement.

Because of modern day technology, such as AI-driven chatbots, machine learning, and natural language processing, there are lots of exciting opportunities to explore within the teacher-feedback-area.

Using a chatbot to collect feedback is the ultimate compromise between a qualitative and a quantitative research method. As teachers are normally way too busy to collect qualitative feedback from each student, an end-course survey is often used.

A chatbot can collect opinions trough a conversational interface with the same advantages as a ‘real’ interview but with a fraction of the required work. The conversation can be tailored according to the responses and personality of the student, ask follow-up questions, and find out the reason behind opinions. It’s also possible to filter out personal insults and foul language, which are sometimes present in teacher ratings.

Other than being a compelling option to surveys and with more qualitative data, a chatbot brings many other advantages for teachers who seek to improve efficiency in teaching. By involving more data sources such as self-assessment, grades, peer feedback, and the latest scientific findings on how to teach effectively, it’s possible to form a more nuanced picture of teaching performance. Comparing the data to that of other teachers around the world should make it possible for the system to suggest new and powerful ways to improve teaching and share findings throughout the teacher’s community.

4. Watson, the Teacher Assistant

At the Georgia Institute of Technology, students were charmed by the new teachers assistant, Jill Watson, who managed to respond to student inquiries in a fast and accurate way.

What the students didn’t know was that Ms. Watson’s true identity actually was a computer and powered by IBMs AI-system with the same name. Computer Science Professor Ashok Goel fed Watson more than 40.000 forum posts to get the system up and running. Answering common questions is a perfect application for a chatbot and a much more interactive approach than using an FAQ-tab.

After getting huge publicity, Jill Watson is spreading her wings and is now being implemented in universities across the globe. One of the latest to be added to the list is BI Norwegian Business School in Oslo, Norway.

Featured CBM: Building an IBM Watson Powered AI Chatbot

5. The Chatbot Campus Genie

At the Deakin University in Victoria, Australia, development is in full swing to complete the first ever chatbot campus genie. Just like in the case of the AI teacher assistant, the intelligence behind it comes from IBM’s supercomputer system, Watson.

Once operational, the Deakin genie will be able to answer questions related to everything a student needs to know about life on campus. How to find the next lecture hall, how to apply for next semester’s class, how to submit assignments, where to find parking or where a counselor can be reached are all questions that can be handled by the genie.

When new students swarm the campus, they have similar questions every year which makes for a perfect application of a chatbot.

William Confalonieri is the driving force behind the genie-project and the CIO at Deakins University.

“The most promising opportunity to use this technology,” he says, “Is to support a much more personalized approach to on-campus services that still appeals to a large crowd. The system will also help lower the burden on stressed-out faculty, as they no longer have to explain the same things over and over to different students.”

Confalonieri hopes to be able to expand the capabilities of the system rapidly in the coming years and have it handle significantly more complex tasks in the future.

6. Student-Centered Feedback

The current educational system can, a bit maliciously, be described as a factory-line where the end-goal is to produce competent students to fill employee needs. The factory expects the same raw material (students), the same response to treatment (lessons) and the same result in the same time frame.

As human beings are complex beyond the reach of even the most advanced science, the factory approach is not ideal when it comes to transferring knowledge to a diversified new generation.

Entrepreneurs are now exploring a new take on the educational model. A student-centered system where student’s own personality and interest is the most decisive factor when it comes to curriculum configuration.

The content adapts to individual learning pace and can present gradually harder problems to accelerate learning as the student comprehend more and more. This way, both fast and slow learners can keep going at their own pace without being discouraged by other students.

It’s also possible for an AI-driven curriculum to foresee future trouble areas and present more problems related to it in order to prevent future struggles.

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