Digitalization has impacted almost every sphere of our lives. We take care of our finances through online banking, communicate with our friends via social media, and search for potential partners on dating platforms. However, digitalization has not only affected our private lives but also the vocational world. In her book “The New Education”, university professor Davidson predicts that 65% of jobs that will be available for students in 15 years do not exist yet. Most of these new jobs are expected to evolve in the quaternary sector, which is composed of knowledge-based and highly demanding service jobs such as in business, legal, and IT functions.
To cope with these rapid developments, we require an excellent education system which prepares students adequately for a career in an unforeseeable environment. So far, the measures taken by universities and schools to satisfy these high requirements are disappointing. For a long time, employers report an increasing gap between graduates’ skill set and employers’ expectations, while students complain that the cost of higher education is increasing rapidly. As a result of these issues, the education technology (EdTech) industry has evolved to a considerable instance with a market size estimated to be around US$165 billion in 2016. One of the forerunners of the EdTech industry is the former head of Google’s self-driving car team and Stanford professor Sebastian Thrun. In 2011, he launched the online course “Introduction into AI” in which 160,000 students enrolled in the first batch. Based on the success of the online course, Thrun went on to found the massive open online course (MOOC) platform Udacity in 2012. His main motivation was to make high-quality and practice-oriented education accessible to people all around the world independent of their social background:
I felt that if we could just build a new kind of university that could democratize education and really reach everybody, we could have a bigger impact on the world than just building a self-driving car.
Will MOOCs be the future of education and teachers become dispensable?
Unlikely. The hype around MOOCs has flattened out and despite enormous investments in recent years, MOOCs do not seem to become the first-choice learning format for students, which is also indicated by the current high dropout rates (often >95%). Blended learning approaches, in which classroom learning is combined with digital sessions outside the classroom, are much more likely to disrupt traditional education. This concept offers the best of both worlds; the benefits of education technology such as the permanent accessibility of content and the advantages of face-to-face classes, for example, the formation of a community and the development of social skills. Because educational institutions have continued to rely on traditional lecture/classroom settings in recent years, they oversaw unconceivable potential from AI solutions to complement traditional learning by improving current common practices and unburdening teachers.
Here are three of the hottest topics that the EdTech scene is currently working on using artificial intelligence:
Because of simple practicality, our current education system is based on grouping students merely by age, disregarding the differences in students’ learning paces, interests, and talents. Consequently, in every classroom there are a few students who become bored because they have understood a subject very fast and others who are discouraged quickly because they cannot follow the teacher’s explanations. In other words, education has moved towards a one-size-fits-all solution.
Rochelle, head of product management at Google, expects that AI applications will soon personalize the learning experience of students by suggesting individual learning objectives, selecting instructional approaches and displaying exercises that are based on the interests and skill level of every student. Just like Netflix shows us the film that we could also like, or Spotify creates a personal playlist based on our historical music choice, AI could suggest to students their most suitable educational setting. The provision of an individual learning journey would allow students not only to learn at their own pace but also regain the enjoyment and excitement that excellent education offers.
What is needed to implement personalized learning successfully is a close interplay between teachers and AI machines. While AI can help students to study content and complete exercises at home, teachers will have to adjust their lecture formats and focus on integrating interactive elements, revising content, and practicing soft competencies such as communication and presentation skills. Realizing that AI systems can only do half of the job towards high-quality education, the importance of well-trained teachers is once again emphasized.
I can still recall my former English teacher claiming that the worst part of his profession is grading. It is a repetitive task that consumes a lot of resources which could be utilized as valuable classroom time interacting with students. In fact, Kraus, president of the German teacher union, estimates that teachers in Germany spend up to 1,000 hours every year on grading depending on the classes and subjects they are teaching. Apart from teachers’ dissatisfaction with the status quo, also students criticize that the grades they receive are in part subjective, inconsistent, and opaque.
AI technologists are working on solutions to automate the tedious grading process and one successful example is the AI-based grading solution Gradescope, a system which is already used in many universities including Berkeley and Stanford University. Gradescope asks teachers to scan the students’ handwritten test solutions and automatically applies pre-defined grading criteria to all tests, thereby reducing grading time significantly and providing a transparent grading key to students.
While Gradescope already works robustly for standardized test formats such as multiple choice tests, a further challenge lies in the grading of longer texts, a discipline also referred to as automated essay scoring (AES). One approach to AES is finding objective measures such as the word length, the number of spelling mistakes, and the ratio of upper case to lower case letters. However, these obvious and quantifiable measures are not insightful for evaluating crucial aspects of an essay such as the argument strength or conclusiveness. To see how well machines would grade thousands of essays and replicate teachers’ gradings, the William and Flora Hewlett Foundation started a competition in 2012. Almost surprisingly, the output of the winning team was in 81% agreement with the teachers’ gradings, an impressive result that marked a turning point in teachers’ perceptions towards education technology. Today, AES engines are used to support human raters in scoring academic essays such as the GRE and TOEFL. For anyone who is interested in finding out how an AES engine would score their writing skills, I can recommend to sign up for the TOEFL test preparation for free and jump to the writing practice test.
24/7 personal student and teacher support
Have you ever seen your teaching assistant, who forwards you relevant course information and answers all your questions regarding the course syllabus on campus? If not, he or she probably does not even exist but is just an AI bot. At least this is what happened to 400 students at the Georgia Institute of Technology when professor Goel introduced the new teaching assistant Jill Watson to the class. Ms. Watson replied to questions, sent out reminders for due dates, and asked questions in the middle of the week to trigger discussions. Students described her responses as reliable and her tone as colloquial. The only difference to an ordinary teaching assistant was that Ms. Watson was not a real person but an AI bot developed to reduce the workload of professor Goel’s Ph.D. students. Ms. Watson took over tasks in which she was at least 97% confident that she knew the right answer. Such an AI-based bot is helpful for both teachers because it reduces their overall workload and students because they can get help on common problems instantly and round the clock.
Educational institutions have to find new approaches to prepare students sufficiently for their future. To achieve that, teachers and digital systems such AI engines will have to learn to collaborate effectively to overcome the remaining challenges on the path towards better education. Therefore, it is crucial that AI systems are tailored to act side-by-side with teachers, taking on the repetitive tasks and leaving the challenging ones to the human. This will allow educational institutions to increase productivity, save cost, and create new opportunities. The recent developments in education technology and particularly in AI make me confident that we will be successful in providing future students a high-quality education and enabling teachers to do more of what they love most — teaching.
Sebastian graduated top of his class with an MSc in electrical engineering from TU Munich and the CDTM. During his second MSc at Stanford he focused on management science and machine learning. He worked as a consultant at McKinsey before returning to engineering at Intel and deep tech startups.