“Let me introduce our new TA, Alexa!” This may be a plot in a science fiction movie, but that day may soon come true in school. Recently, “the toy-giant Mattel announced it had pulled the plug on plans to sell an interactive gadget for children” (NPR). The device, named Aristotle, looked similar to a baby monitor with a camera, but could “displace essential parenting functions, like soothing a crying baby or reading a bedtime story.” Aristotle, powered by artificial intelligence, can collect large-scale data about a child’s behavior by tracking and surveillance and then through computation, interact with the child.
Artificial Intelligence (AI) and Machine Learning (ML)
Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now. AI is a broader concept about machines being able to carry out tasks in a smart way (Forbes). ML refers to some specific application of AI, namely, feeding machines with data and let machines learn for themselves. The life of AI and ML depends on ubiquity and big data.
What questions should educators ask before AI and ML creep into classrooms?
Increasing the collection and computing of big data in children’s lives is the trend of AI and ML, but it challenges educators. The 2018 Interaction Design and Children Conference (IDC) discussed at least four areas where scientists and educators should consider using the ubiquity of technologies and big data to benefit children. The identified areas include:
Control and ownership –
- To what degree can and should students and parents control data about them?
- Are control, ownership, and data privacy transparent and easy to understand for all stakeholders (i.e., students, parents, teachers and school administrative staff)?
A singular versus a holistic view of the impact of technology –
- Should technologies focus on maximizing personalization and benefiting individual students?
- Or should priority be given to societal goals, such as integration and inclusion in education?
- Should technologies focus on single outcomes (e.g., learning a very specific skill) or overall development (e.g., learning to self-regulate)? Can educators achieve both when using AI technologies?
Superficiality versus depth when introducing AI and ML devices –
- How much data should be reasonably collected to have AI and ML devices engage in students learning and benefit students?
- What data should be collected for the interaction between machines and students to assist students’ learning?
Educational needs and human skills –
- How should we prepare students for a world in which we interact with people in person and interact increasingly with machines?
- How do we teach students to be critical and reflective about AI and ML?
- How do we support students to stay safe online when using industrial, social and technical mediation?
In summary, educators need to seriously consider one overarching question – Do computational thinking, computational practices, and computational perspectives as a framework sufficiently embrace and support the skills and competences needed in the 21st century? In other words, can Alexa effectively replace TAs in our classrooms? The question is indeed challenging; the answers may substantially change our views on the future of education in the era of Artificial Intelligence.