In higher education contexts, where GenAI is increasinglty embedded in teaching, assessment, and academic work, listening pedagogies offer a way to slow down and critically attend to how AI shapes learning, authorship and decision-making. Listening pedagogies have the potential to deepen our understanding of the textural complexity of educational technologies including GenAI (Wilde et al., 2024). Developing such pedagogies is vital at a time when technological ‘noise‘ is pervasive.

This presentation addresses the conference sub-theme of pedagogical responses to AI reconfigurations of education. Building on research in the areas of postdigital sound and soundscape ecology, the presentation introduces a framework to support the development of listening pedagogies in the age of AI (Wilson, 2026, in press). Such pedagogies are needed to surface the inconspicuous elements of GenAI and support ethical decision-making around AI use in higher edcuation. The framework includes seven interwoven attributes: revealing the hidden; naming sonic phenomena; attending to the relationship between humans, technologies and more-than human voices; listening to the intersticies; cultivating awareness of the interplay of the senses; fostering expansiveness, and connecting past, present and future. The framework can be used by educators and researchers to construct listening pedagogies that work with sound as materiality to help us attune to our entanglements with digital systems. In practice, these attributes translate into pedagogical approaches that can be used in course design, assessment and classroom interaction, such as guiding students to attend to the ‘voice‘ of GenAI, designing learning activities that surface algorithmic influence, and using reflective listening tasks to question authorship, authority, and agency in interactions involving AI.

This presentation invites participants to reflect on their own uses of GenAI in teaching and assessment, and to consider how listening pedagogies might be incorporated into their disciplinary contexts to support more ethical and critically attentive AI practices.