Learning to work with the black box: Pedagogy for a world with artificial intelligence

Artificial intelligence (AI) is increasingly integrating into our society. University education needs to maintain its relevance in an AI-mediated world, but the higher education sector is only beginning to engage deeply with the implications of AI within society. We define AI according to a relational epistemology, where, in the context of a particular interaction, a computational artefact provides a judgement about an optimal course of action and that this judgement cannot be traced. Therefore, by definition, AI must always act as a ‘black box’. Rather than seeking to explain ‘black boxes’, we argue that a pedagogy for an AI-mediated world involves learning to work with opaque, partial and ambiguous situations, which reflect the entangled relationships between people and technologies. Such a pedagogy asks learners locate AI as socially bounded, where AI is always understood within the contexts of its use. We outline two particular approaches to achieve this: (a) orienting students to quality standards that surround AIs, what might be called the tacit and explicit ‘rules of the game’; and (b) providing meaningful interactions with AI systems.

Practitioner notes

What is already known about this topic

• Artificial intelligence (AI) is conceptualised in many different ways but is rarely defined in the higher education literature.
• Experts have outlined a range of graduate capabilities for working in a world of AI such as teamwork or ethical thinking.
• The higher education literature outlines an imperative need to respond to AI, as underlined by recent commentary on ChatGPT.

What this paper adds

• A definition of an AI that is relational: A particular interaction where a computational artefact provides a judgement about an optimal course of action, which cannot be easily traced.
• Focusing on working with AI black boxes rather than trying to see inside the technology.
• Describing a pedagogy for an AI-mediated world that promotes working in complex situations with partial and indeterminate information.

Implications for practice and/or policy

• Focusing on quality standards helps learners understand the social regulating boundaries around AI.
• Promoting learner interactions with AI as part of a sociotechnical ensemble helps build evaluative judgement in weighting AI's contribution to work.
• Asking learners to work with AI systems prompts understanding of the evaluative, ethical and practical necessities of working with a black box.