Use Scenarios & Practical Examples of AI Use in Education

This report presents a set of use scenarios based on existing resources that teachers can use as inspiration to create their own, with the aim of introducing artificial intelligence (AI) at different pre-university levels, and with different goals. The Artificial Intelligence Education field (AIEd) is very active, with new resources and tools arising continuously. Those included in this document have already been tested with students and selected by experts in the field, but they must be taken just as practical examples to guide and inspire teachers creativity.Comment: Developed within the AI in Education working group of the European Digital Education Hu

Why is an integrated STEM approach an important element in the teaching of the future?

The global urgency to improve Science, Technology, Engineering, and Math (STEM) education is driven by environmental and social needs of the twenty-first century, which in turn jeopardizes global security and economic stability. The complexity of these global factors reaches beyond helping students achieve high scores in their class assessments and refining how we teach STEM courses is necessary to ensure our students are ready to confront the realities of their environments and communities. STEM is not a new discipline, nor is it a question of integrating science and scientific technology into all disciplines. It has a much wider scope than that, and it cannot be limited only to the four disciplines individually. In fact, STEM is about the different disciplines working together, making connections between each of them, as well as the school and communities where they are being taught. STEM subjects should be taught together, intertwined

AI report

We have seen in the previous discussion and scenarios that AI has the potential to deliver great benefits for education. However, we have also seen that there are also risks associated with its use. In many cases, we may determine that these are minimal risk. Examples we’ve discussed include the provision of formative feedback, help for teachers in creating lesson plans, and assistance in some of the administrative functions of schools. As we move away from the use of AI as a support system, so the risk increases. As we have seen, using AI for learning analytics may help teachers adjust their teaching strategies to cater to individual needs. However, using learning analytics without adequate teacher oversight may disadvantage students dealing with adverse life circumstances that are impacting their performance, thus increasing the risk level. When it comes to relying on AI for decisions that may impact a learner’s future opportunities, we are moving into the ‘high’ and perhaps ‘unacceptable’ risk territories. Therefore, we can see that the level of risk resides not so much within the tool as within the contexts in which they are used. While human oversight may help to mitigate some of the risks, we should be aware of the danger of dependence lock-in, in which humans become increasingly dependent to AI to make decisions. All this underscores the importance of the development of Explainable AI, as discussed above. In order to ensure its responsible use in educational settings, it is important to remain ever aware of the balance that needs to be struck between leveraging AI’s benefits and evaluating and mitigating potential risks and ensuring that human oversight is included and human values are served </p