Editorial: Artificial Intelligence is Awesome, but Good Teaching Should Always Come First

The explosion of generative artificial intelligence into the mainstream of society some twelve months ago has seriously challenged learning and teaching practice. Since then, AI companies such as OpenAI are constantly improving their language models and releasing new features to make them more capable and useful. So, given there have been many disruptors in the past and emerging disruptions in the present, what can we learn in this situation, where Generative AI stands poised to challenge the purpose and relevance of assessment models? From our examples, disruptive technologies only have a major impact when they positively transform practice and are informed by pedagogic models and learning theory. GenAI as a disruptor is only likely to have this positive impact when it informs quality learning and teaching practice. We should be focused on the opportunities that GenAI now presents to higher education. It is argued here and elsewhere that the relative weakness of GenAI is that it creates poor quality output, delivering uninformed, incorrect, biased and bland responses. In itself, this offers opportunities for ‘teachable moments’ (Newell et al, 2023) and gives us room to support students with their capabilities in an AI informed world. Historically, these opportunities enable higher education to grow and progress. What we have learned so far would appears to be that for research to contribute to the literature, they needed to be informed by it. Likewise, need to ensure that pedagogy, andragogy, and heutagogy come first. We also need to remember that people processes happen, artificial intelligence happens around them, and that artificial intelligence comes after human intelligence.