Conference Program

The integration of Artificial Intelligence (AI) into educational systems raises significant concerns regarding the automation of pedagogical judgement and its potential impact on educational professionals’ agency. This contribution explores the use of language-model-based multi-agent systems (LLM-MAS) (Cheng, Y., et al., 2024) as cognitive mediators supporting collective ideation and the construction of school visions, avoiding passive delegation to technology. Grounded in a sociocultural framework, AI is conceptualised as a mediating artefact capable of expanding human cognition operating within the Zone of Proximal Development (ZPD), providing adaptive support for tasks that would otherwise be difficult to manage independently (Vygotsky, 1978). In this view, AI does not replace professional judgement but can strengthen agency as the capacity to intervene intentionally in educational processes and act as co-authors of institutional change (Bandura, 2001).

The study presents preliminary findings from a project conducted in three small schools located in central Italy, complex contexts where challenges take locally specific forms and require highly contextualised innovation strategies. Teachers, school leaders and key stakeholders participated in imagination workshops supported by an LLM-MAS designed to complement Design Thinking practices (Brown, 2009; Zampolini et al., 2025a). Unlike generalist language models, the system employs multiple intelligent agents organised with distinct roles (Zampolini et al., 2025b) — Challenger, Jester, Gardener and Conceptualiser — enabling proactive interaction and supporting reflective ideation. Equipped with contextual memory, the system functions not as a mere assistant but as a “cognitive teammate”, participating in problem-setting processes while maintaining human control.

Functional differentiation among agents allows simulation of dialogical and collaborative dynamics, fostering negotiation of meanings and co-construction of knowledge. Such dynamics support professional creativity as a systemic competence emerging from collective problem reformulation and shared solution generation rather than an individual trait (Harris, 2018; Sawyer, 2012).

Analysis of the imagination workshops indicates that participants developed increasingly articulated and context-sensitive school visions through iterative cycles of problem reformulation and solution generation. Interaction with LLM-MAS appeared to facilitate dialogue, the exploration of alternative scenarios and the externalisation of tacit assumptions, contributing to the transformation of imagination from an individual activity into a structured collective process. Rather than replacing human thinking, the system functioned as a cognitive and social scaffold that supported reflective deliberation within the workshops (Belland, 2014). By providing a low-risk environment for experimentation, it also helped reduce barriers to creative engagement, such as fear of error and resistance to ambiguity (Zampolini et. al, 2025a).

Overall, interaction with LLM-MAS supported deeper and more reflective ideation processes, strengthening professional agency and the development of locally grounded solutions. Rather than replacing human thinking, AI functioned as a cognitive and social scaffold that stimulated dialogue, made design alternatives visible, and transformed imagination from an individual capacity into a structured collective practice (Belland, 2014). By providing a safe, low-risk environment for experimentation, multi-agent systems also reduced psychological barriers to creativity, such as fear of error and resistance to ambiguity (Zampolini et al., 2025a).