“It’s a November morning in a school classroom and the year is 2027. As the winter sun streams through the windows, a fourth-grade teacher named Jude adjusts the blinds and is suddenly struck by how little the room has changed in the seven years since her first day. It is still arranged around several large, circular tables, and student pictures decorate the walls. In many senses, it’s the same old messy learning environment it always was.

But then again, there are those small but visible camera lenses mounted in the ceiling, the microphones embedded in the tables, and the virtual whiteboards that take their form out of nowhere. And at Jude’s side, there’s her AI Teaching Assistant, Colin, whom she’s named after a childhood friend. In fact, so many aspects of how Jude understands her students’ learning are different now thanks to her machine aide.

Through working with Colin, she has become somewhat of a metaphorical judo master, harnessing the data and analytical power of AI to tailor a new kind of education to each of her students. Her role at the helm of the classroom, however, is fundamentally unchanged.

Since Colin makes ongoing assessments based on daily student performance and engagement in the classroom, there is simply no longer any need for what were often inaccurate and stressful evaluations. The AI aide’s primary task is to build and maintain learner models for each child based on a combination of data gathered over time with things like voice recognition (which identifies who is doing and saying what in a team activity) and eye tracking (to note engagement and focus). The profiles are updated continuously, monitoring students’ progress against analysis of their emotional and motivational state.

Not only do students and their parents have their own interfaces for viewing how a student is progressing in various curricula and skills, but AI in education now means there is evidence on record at a class, school, district, and country level about academic performance. The need for national and international testing is indeed a venture of the past.”

In countries where modern technologies are widely used, artificial intelligence (AI) is currently a part of our daily lives. AI provides technology with visual capabilities, the ability to solve problems, make plans, and understand and produce natural language, both spoken and written. These AI applications are used in areas such as medical diagnosis, language translation, and autonomous vehicle design. AI is also already being applied to educational settings. For example, the company Alelo is developing culture and language learning products, specializing in virtual role play simulations powered by AI. The UK-based company Century Tech has developed a learning platform, with input from neuroscientists, that tracks students’ interactions with computers, from every mouse movement and each keystroke. Century’s AI looks for patterns and correlations in their data to offer a personalised learning journey for the student. It also provides teachers with a a real-time snapshot of the learning status of every child in their class. However, these examples are just the tip of the iceberg when it comes to how AI can contribute to helping students and educators progress their understanding and knowledge more effectively. The prospect of ‘Colin’, the AI assistant described at the start of this piece, is not a science fiction, we have the technology and the know-how to build Colin; it is only the will and the funding that is lacking.

The thoughtful approaches of AI to educational challenges has the potential to provide significant benefits to educators, learners, parents, and managers. However, the solutions can not start with the technology; rather they must start with a thorough exploration of the educational problem to be tackled. Without this enterprise, the technologists cannot design effective AI solutions to the key educational challenges recognised across the globe.

The changing workplace requires education to develop an adapted future workforce, and AI can help. The situation is complex, with many factors at play beyond automation: globalization, increasing inequality, and political uncertainty. There is little agreement between experts about exactly what jobs will remain or be created and which skills will be paramount. The only thing we can be sure of is that the future workplace will be uncertain and unpredictable. Our students must therefore learn to cope with this uncertainty; to be resilient, flexible, and lifelong learners. 

Self-efficiency is the key skill students will need for their working lives. Every individual needs to have an evidence-based, accurate belief in their ability to succeed and to accomplish tasks both alone and with others. A person’s sense of self-efficiency plays a key role in how they not only tackle tasks and challenges, but also how they set their goals as individuals and as collaborators. This is something that can be taught and mentored and it requires an extremely good knowledge of what one does and does not know, what one is and is not so good at, where one needs help and how to get this help. This self-knowledge, or meta-cognition, is not just about subject-specific knowledge and understanding, but also about one’s well-being, emotional strength, and intelligence.

This is where AI can help. Metacognition is an understanding of one’s own thought processes. Monitoring and regulating one’s cognition involves a variety of executive functions and strategies, such as planning, resource allocation, monitoring, checking, error detection, and correction. Good metacognitive awareness and regulation enhances cognitive performance, including attention, problem-solving and intelligence and it has been shown to increase learning outcomes. Successful students continually evaluate, plan, and regulate their progress, which makes them aware of their own learning and promotes deep-level processing. Metacognitive awareness and regulation can be taught and supported in classrooms, and can benefit learners of all abilities.

A series of studies conducted using an AI software simulation called the Ecolab demonstrated that AI could be employed to support learners to develop metacognitive skills, in particular during task difficulty selection skills. The results demonstrated that the students whose subject knowledge and ability had been assessed as being below average gained particular benefit and performed significantly better than more-able students.  
In addition to employing AI to support the development of these important learning skills, we can also use AI to help students visualise the trajectory of their progress and increase their self-awareness. For example, this technology will be able to map out the area of the curriculum that the child is studying on a screen, with each thumbnail representing a curriculum topic. When the user clicks on one of the thumbnails, a bar chart will indicate the level of difficulty of the work that the child has completed while working on this topic. In addition, dots on a ‘dice’ will be shown to indicate how much help the child has received.

What I hope is clear from the discussion about the future of the workforce is that we need to review what and how we teach in order to ensure that AI is used as a tool to make our students, and ourselves, smarter, not as a technology that takes over human roles and ‘dumbs us down.’ 

To achieve this we need to concentrate on developing teaching and schooling that accentuates the uniquely human abilities of our students as well as instilling within them the requisite subject knowledge in a flexible, interdisciplinary and accessible manner. In terms of teaching, we need AI assistants to relieve teachers from the routine and automatable parts of their job to enable them to focus on human communication and student’s well-being.