Conference Program

The rise of Generative Artificial Intelligence (GenAI) in schools introduces a fundamental pedagogical tension: how can GenAI be integrated into classroom practice without encouraging cognitive delegation or passive reliance on AI-generated outputs? This issue connects to broader debates on the purposes of education and the risks of delegating educational processes to technological systems (Biesta, 2020; Selwyn, 2019). This study presents an intervention conducted in an Italian upper secondary school using a Specialised Conversational Agent (SCA) grounded in a Metacognitive Tutoring Model (MTM), designed to foster critical GenAI literacy, student agency, and reflective judgment.

We explore the conditions under which AI can enter classroom practice without replacing educational responsibility or teacher judgment. This perspective aligns with critical analyses warning against technologically deterministic narratives (Williamson & Eynon, 2020) and emphasizing the need to preserve teachers’ professional agency in AI-enhanced environments (Lan & Chen, 2024). The MTM underpinning the SCA aims to (1) reject the agent’s role as an oracle-like problem solver, (2) enact dialogic coaching and active scaffolding, and (3) create a “cognitive gym” supporting the transition from passive consumer to active inquirer. The model builds on foundational work on metacognition (Flavell, 1979) and on the role of self-efficacy in sustaining learner agency (Bandura, 1997). Through structured feedback, metacognitive prompts, and guided error analysis, the agent ensures that students remain the primary architects of the problem-solving process, learning to reformulate prompts and critically evaluate responses, including incomplete or misleading outputs. The agent is thus conceived not as a substitute for human intelligence, but as a system designed to augment and extend it (Luckin, 2018).

The intervention adopted a waitlist-controlled classroom design. All students received preliminary training in prompting, framed as a cognitive and deliberative practice emphasizing clarity, intentionality, and iterative refinement. In the first phase, one group participated in structured mathematics sessions with the SCA, while a second group followed traditional instruction. In the second phase, the latter received prompting training and used the SCA, ensuring full participation. This design enabled observation of learning trajectories and the development of competence in using GenAI systems. Rather than focusing on disciplinary performance, the study examines empowerment and metacognitive indicators. Data include anonymized student–agent interaction excerpts, the evolution of prompt patterns, and a self-perception questionnaire measuring perceived agency, awareness of GenAI limitations, prompt reformulation ability, and recognition of incomplete or ambiguous responses. The central question concerns students’ positioning toward GenAI: do they adopt uncritical cognitive delegation (GenAI as an “epistemic oracle”), or maintain cognitive agency, treating GenAI as a co-cognitive dialogic partner? This inquiry reflects concerns about the future of teaching in AI-mediated contexts (Selwyn, 2019) and the need to design systems that support rather than displace pedagogical judgment (Lan & Chen, 2024). We argue that critical AI literacy cannot be reduced to ethical awareness or technical proficiency. It requires pedagogical devices that preserve deliberation (Biesta, 2020), safeguard teacher judgment, and foster students’ responsibility in learning. The proposed model is presented as a replicable instructional framework currently undergoing empirical validation.