Co-inference and collaboration: Empowering higher education staff and students through a GenAI literacy development framework based on mind-metaphor

Higher education staff and students are yet to emerge from the liminal space created due to the exponential rise in Generative Artificial Intelligence technologies such as Large Language Models. Both groups exhibit a low or developing understanding of such tools. On one hand, staff in many institutions are concerned about critical skills development, plagiarism, ethical implications and long-term cognitive effects of the use of such tools. Equally, students are concerned about the acceptability of the use of such tools at university and work related GenAI skills. On the other hand, foundational large language models are being trained on larger and better data sets and have evolved into “reasoning” models that can allegedly “think”. Furthermore, with the emergence of agentic AI, systems that can “act” independently, claims of shifting agency away from humans to machines are common. This has implications for the world of work and for higher education institutions. This opinion piece presents a new framework that I have developed for enhancing GenAI literacy of staff and students and to empower them to learn and work with GenAI tools in a balanced, meaningful and collaborative way, whilst maintaining their agency. The Mind-Metaphors framework includes techniques such as: mind-surfing, mind-mending, mind-bending, mind-gaming, mind-storming and mind-stretching, which can help staff and students in developing trust in their chosen GenAI models and mastering collaborative GenAI use. I have developed and evaluated the framework iteratively during several one-to-one and one-to-many training sessions with over 250 academic and industry staff and with student representatives from various disciplines. I show how these mind-metaphors empower staff and students to emerge from the liminal space by developing their GenAI literacy and their agency when using GenAI. This work shows examples of co-inferring and collaborating with GenAI for learning, teaching and research and draws upon some of the concerns reported in literature. Implications for the world of work for students are also highlighted.