New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.

Robot-assisted social role-play can help neurodivergent individuals practice social skills in a safe environment. Large language models (LLM) facilitate the implementation of such agents. However, high quality standards must be ensured in this sensitive setting. This article argues that current evaluation methods of generated language are not sufficient because they are grounded in beliefs about language as an external code to describe the world (referential functions of language). We argue that non-referential functions of language must be part of the evaluation of LLM-generated language when LLMs engage in social interactions with users. We test the feasibility of our approach in a pilot implementation of a platform for robot-assisted social role-play. Out proposed evaluation framework helps to assess systematically referential and non-referential functions of LLM-generated language. We argue that the evaluation framework can be also applied to multimodal interaction.