Our professional lives are now increasingly entangled with artificial intelligence (AI). Organisations now routinely use AI for a range of operations which in turn re-define us and our work. AI is increasingly challenging our professionalism by taking over our decision-making, recruiting us as well as managing us through monitoring our emotions and behaviour. In some workplaces, facial recognition is enhanced to enable mood-recognition, and organisations can monitor our mood to ensure we are optimally productive. Emotion recognition is now a 20 billion dollar industry. In these hybrid times, what it means to be a professional in different sectors is being re-inscribed.
AI and big data have also entered our schools and universities. Data analytics and predictive data are sorting, organising, prioritising and excluding prospective students and employees. As more and more assessments and other learning activities are done online, a range of data, including click behaviour and eyeball tracking, are now being collected to generate massive datasets that enable prediction and intervention. The use of gaming and the introduction of wearable technologies enable the collection of biometric data and real-time intervention based on these data.
What are the implications of this increasingly hybrid world where humans and AI are becoming more entangled and more indistinguishable? In particular, what are the implications for professionals in education – and what can we learn from other fields of endeavour?
Join the conversation as experts across disciplines ponder these questions and raise new ones.
- Ben Williamson (University of Edinburgh)
- Sarah Pink (Monash University)
- Neil Selwyn (Monash University)
- Michelle Fitzgerald (City of Melbourne)
Dr Ben Williamson: New digital laboratories of experimentation and expertise: How education research became a data-intensive science
"Education data scientists", "learning engineers", and "precision education specialists" are new experts in knowledge production in educational research, bringing together data science methodologies and advanced artificial intelligence systems with disciplinary expertise from the psychological, biological, and brain sciences. Data-intensive human sciences such as algorithmic psychometrics, biometrics, neuroscience, and bioinformatics, all powered by AI, have entered educational research settings, enabling new kinds of data practices and generating new knowledge about the minds, bodies, brains, and genomes of students.
These arrangements of AI technologies, scientific expertise, and data practices make it possible to see and know students in novel ways, and, as the data are made actionable, make students governable through learning engineering experiments. This presentation will examine how education research is being remade as an experimental data-intensive science, with AI combining with the human sciences in new kinds of digital laboratories that exist inside computer machinery. What does it mean for the field when education research is increasingly performed by machines that are powered by code and trained by the human sciences to produce novel data and knowledge?
Dr Ben Williamson is a Chancellor’s Fellow in the Centre for Research in Digital Education and the Edinburgh Futures Institute at the University of Edinburgh. He maintains the research blog Code Acts in Education, tweets @BenPatrickWill, and wrote Big Data in Education: The digital future of learning, policy and practice (Sage, 2017).
Date and time
Monday 11 November 2019
Deakin Burwood Corporate Centre
Level 2, Building BC
221 Burwood Highway