In this last and optional week, we attempt to distill our training – good machine learners that we are – into some pragmatic lessons and thoughts for what lies ahead. Freed temporarily from ever-watchful (though always loving) eyes of AI peer review, we can speculate as to the implications of AI for what we need to learn and need to teach.
We ended last week with a hypothetical series of questions:
Imagine you are teaching a class. You meet a new and unusual student, “Gen”. “Gen” wants to be a teacher, but admits to having a strange problem: they say they are a synthetic being, and lack a sense of self. They say they worry this means their own students will see through them – not take them seriously.
Given this unique case of imposter syndrome – what can we do to help? How do we combine aspects of the history, technical underpinnings, pedagogical assumptions, alignment issues, critical perspectives and accelerationist debates to map out an education agenda for AI?
- What values do we want to align “Gen” to?
- What should happen when “Gen” needs to align aberrant human students?
Does the human override the AI? Does it depend on the human? Does it
depend on the values?
- How do we instill in this synthetic being the “metic” or “phronetic” –
to use two Greek terms connoting practical wisdom – to navigate human
communication situations?
- What curriculum, syllabus & rubric do we need?
- How would we assess & evaluate?
- How do we avoid gamification of evaluations – or is that integral to
meta-cognition, true AI?
What, in short, would be our collective manifesto for AI in education?
And in response to all of this, we are also asking implicit questions about human learning. In the new pedagogical loops opened up by humans labelling and training machines that learn, who then in turn teach humans, we are faced with novel challenges of mutual imitation and differentiation. Do humans follow the machines? Or need to distinguish ourselves from them? And if so, how?
These questions – of how to teach AI, so that it can in turn help and teach us – stretch beyond the hypothetical. They drive corporate agendas for how to steer AI. We could argue these questions deserve being opened up to wider public discourse, and perhaps in certain ways – through open source, feedback loops and so on – they already are. As AI begins to play pivotal roles in public institutions, we might also ask whether these questions are now also integral to democratic governance.
Readings:
Łodzikowski, K., Foltz, P. W., & Behrens, J. T. (2024). Generative
AI and Its Educational Implications. In T. Kourkoulou, A. O. Tzirides,
B. Cope, & M. Kalantzis (Eds.), Trust and Inclusion in AI-Mediated
Education: Where Human Learning Meets Learning Machines. Springer.