The rise of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is accelerating the integration of social robots into education. These technologies enhance robots' abilities in natural language interaction, adaptive behaviour, and personalised learning support. To advance real-world implementation, it is essential to identify the main challenges and opportunities in this field. We conducted a two-round Delphi study with 16 experts in human-robot interaction and educational technology. In the first round, participants outlined opportunities, challenges, and potential robot roles expected in the short term (1 year) and medium term (5 years). Content analysis revealed 8 opportunities, 10 challenges and 10 roles. In the second round, experts ranked their importance and feasibility across both time horizons. The results show that the most critical opportunities and challenges are also the least feasible to achieve in practice. Conversely, the proposed roles of educational robots demonstrated alignment between importance and feasibility. Experts highlighted three promising roles for robots in the GenAI era: supporting teachers in boosting learner engagement, serving as conversational interfaces for students to access knowledge and assisting teachers in supporting disadvantaged learners. These findings provide a roadmap for prioritising feasible innovations in educational robotics.

Engagement is a concept of the utmost importance in human-computer interaction, not only for informing the design and implementation of interfaces, but also for enabling more sophisticated interfaces capable of adapting to users. While the notion of engagement is actively being studied in a diverse set of domains, the term has been used to refer to a number of related, but different concepts. In fact it has been referred to across different disciplines under different names and with different connotations in mind. Therefore, it can be quite difficult to understand what the meaning of engagement is and how one study relates to another one accordingly. Engagement has been studied not only in human-human, but also in human-agent interactions i.e., interactions with physical robots and embodied virtual agents. In this overview article we focus on different factors involved in engagement studies, distinguishing especially between those studies that address task and social engagement, involve children and adults, are conducted in a lab or aimed for long term interaction. We also present models for detecting engagement and for generating multimodal behaviors to show engagement.