ArtificIal intelligence in Education

We live in a world where algorithms algorithmic decision making is pervasive. AI can help decide who gets into college, who is offered a job interview, who receives a home loan, and more. Since AI learns from its training-data, which can be skewed and biased, its decisions can be problematic. Lately, controversy over AI has has highlighted current lack of oversight, regulation, and formal auditing processes for algorithms.

2.1 Introduction

Global adoption of technology in education is transforming the way we teach and learn, in addition to making enrollment decisions. Artificial Intelligence is reconfiguring interaction between learners and teachers, and this technology is going to revolutionize teaching and learning in some distinct ways:

1) Personalized learning: AI can use student performance and interaction data to find a personalized schedule and diet of learning that cater to specific needs of students. Learning is less regimented, more flexible and tailored to students' needs.

2) Smart content production: Digital lessons, bite-sized contents, visualization, simulation, and other news ways of perceiving information will be developed. Learners today are already consuming learning content and interacting with their learning content in innovative ways and the AI technology is going to accelerate that.

3) Automation of administrative tasks: Smart AI assistants will simplify time-consuming tasks such as grading, assessing and providing feedback.

It is obvious that Using AI will transform learning experiences by removing some of the traditional limitations and introducing new possibilities. However, it is important to be mindful of the inner workings of Artificial Intelligence and the quality of the solutions AI claims to offer.

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2.2 AI in education – what the future holds

Artificial Intelligence is no longer a distant utopia. A lot has happened since John McCarthy coined the term in 1956.

What was once the realm of science fiction is now a reality – smart assistants, chatbots, smart home devices, self-driving cars, and other intelligent systems have become ubiquitous and continue to shape and change our lives.

A range of industries, from health care to finance, and ecommerce to transportation have embraced AI systems. But there’s one field that was initially hesitant to adopt AI, but it is probably going to see disrupting changes because of AI. Here we’re talking about teaching and learning.

Lately, educational institutions have been experimenting with AI technology, and there is a consensus that AI is essential to spearhead changes for the future of learning. This Global adoption of technology in education is transforming the way we teach and learn, in addition to making enrollment decisions. Artificial Intelligence is reconfiguring interaction between learners and teachers, and this technology is going to revolutionize teaching and learning in some distinct ways. Let’s discuss four distinct areas of changes that will be driven by AI.

1) Personalized learning:

We all know that everybody learns differently based upon their personal interests, preferences, motivation, and needs. Traditional teaching methods with their one-size-fits-all approach cannot account for the diverse abilities, needs, interests, and preferences of all learners. So not all learners reach their full potential under traditional educational approaches. Personalized learning enabled by AI can narrow achievement gap by providing personalized learning experiences.

Specifically, adaptive learning platforms can create learning profiles for students, based on their abilities, preferences, learning styles and the performance data. This information is then used to adapt content and learning style to suit students’ needs and provide targeted and timely feedback on progress.

2) Assisting with routine administrative tasks

Educators spend huge amount of their time carrying out routine administrative tasks. Educators can automate many of these tasks using AI. AI can do much of the heavy lifting for teachers by grading assignments, organizing paperwork, and managing communications with students.

Automated grading can save hours for teachers and provide feedback for students. AI can organize and retrieve paperwork efficiently. Furthermore, AI solutions can help with a range of administrative duties from such as processing student application forms and HR management. This will result in lower costs and increase administrative efficiency.

3) Education without boundaries

Traditional brick and mortar education has several limitations and require students and teachers to be present at a predetermined time and space. In contrast, AI enabled learning can help make high-quality instruction easily accessible for everyone, including those who cannot attend in-person learning.

AI can make flexible learning possible, and students can learn anytime. Anywhere and they can control the pace of learning that suits them.

4) Creation of digital content

AI is slowly changing the way students interact with and consume learning content. Use of digital text books, simulation, and virtual reality are redefining the ways students interact with learning content. Together with this, students receive timely feedback on their progress and educators can use this information to make critical decisions to support their learners.

What does all this mean for teachers?

It is obvious that AI will be handling some of the tasks previously performed by teachers and administrators, but this technology is unlikely to make teachers redundant. However, AI will continue to develop into a smart and efficient assistant that enables educators to work efficiently to meet their students’ needs better. There is only so much that AI can do in a highly complex social activity such as teaching. No machine can connect with students, guide and inspire them the way teachers do.

Final thought: It is obvious that Using AI will transform learning experiences by removing some of the traditional limitations and introducing new possibilities. However, it is important to be mindful of the inner workings of Artificial Intelligence and the quality of the solutions AI claims to offer.

3.3 Improving algorithm accountability

Concerns around accountability and ethics arise as institutions are increasingly turning to AI for recruitment and student assessment.

When human decision makers make mistakes, it is easy to assign blames and but how can we make machine algorithms accountable? Given the lack of transparency of AI due to companies not sharing their proprietary data sets, discriminatory practices may continue longer before it is spotted and challenged.

We are only beginning to think of algorithmic accountability. Only recently in 2019, Algorithmic Accountability Act was introduced, and is still under discussion, in the House of Representatives in the USA. The law would require businesses to conduct internal reviews of their automated decision-making processes to uncover any bias, unfairness, or discrimination in the algorithm’s output.

Meanwhile, higher education institutions must protect their students' personal information and take steps to prevent unintended bias caused by algorithms, according to experts in the field. In Algorithms, Diversity, and Privacy: Better Data Practices to Create Greater Student Equity, Rosa Calabrese, manager-digital design, WCET – WICHE Cooperative for Educational Technologies, suggests that humans should be included in the decision-making process alongside AI-powered algorithms, and that institutions should audit algorithms for bias on a regular basis.

Another way to minimize algorithmic bias is to diversify data sets and to diversify the group of people involved in creating the algorithms. Developers and designers should not always design for a typical, or a prototypical student, as such as approach can perpetuate bias against non-typical learners.

An example of such bias was reported by The Markup. Earlier this year, the technology news site The Markup investigated the advising software Navigate, which is widely used by many public universities in the USA. They found that certain student population were deemed 'high risk' and the 'Major explorer' function suggested administrators to push those students to another major where their risk of completion is lower. Such algorithmic decision making can leave advisors with life-changing impression of students and their chances of success within a given major. This can allow for misusing algorithmic driven recommendations or steering educational priorities in a certain way.