AI as a Practice Partner: A Feasibility Study of MentaClassAI, a Conversational LLM Tool for Training Educators’ Mentalizing Responses to Child Dysregulation

Background: Teachers routinely encounter children whose behaviour reflects emotional distress and dysregulation, yet they have limited opportunities to practise the relational skills required to respond effectively. These challenges are particularly pronounced in Alternative Provision (AP), which serves children who frequently present with histories of trauma, neurodevelopmental differences, and complex emotional and behavioural needs. Mentalization, the capacity to understand behaviour in terms of underlying mental states, is central to effective relational practice in such contexts. Conversational Artificial Intelligence (AI) may offer a scalable means of supporting this form of skills development, but its feasibility as a teacher-training modality remains largely unexplored. Methods: This mixed-methods proof-of-concept feasibility study evaluated MentaClassAI, a novel AI-based training tool in which educators engaged in simulated voice conversations with AI child characters portraying classroom dysregulation and subsequently received individualised, mentalization-informed feedback. Eleven staff members from a single AP school (four teachers and seven teaching assistants) completed a single training session and were allocated to either a psychoeducation video condition (n = 6) or a no-video condition (n = 5). The video condition received a brief introduction to mentalization and epistemic trust prior to engaging with the simulation. Pre- and post-engagement measures included the Reflective Functioning Questionnaire (RFQ-8) and a Teacher Self-Efficacy Scale. Post-engagement measures included an 18-item acceptability questionnaire, a Technology Acceptance Model scale, and open-ended questions analysed using thematic analysis. Results: Acceptability was high, with 84.8% of questionnaire responses falling within the positive range (overall M = 5.60/7). Feedback accuracy (M = 6.55) and clarity (M = 6.36) received the highest ratings. Participants reported higher teacher self-efficacy after the session than before (d = 1.20, p = 0.003), with 10 of 11 participants demonstrating improvement. Self-reported hypomentalizing was lower after the session (d = −0.86, p = 0.017). Between-condition differences (video versus no-video) were not statistically significant. The video condition scored numerically higher on the directional indicators. Qualitative analysis identified five themes: the value of consequence-free rehearsal; the specificity and usefulness of feedback; appreciation of the focus on the child’s emotional experience; limitations in the ecological diversity of AI child characters; and a desire for more naturalistic interaction. Conclusions: These findings provide preliminary support for the feasibility and acceptability of AI-based mentalization practice for AP staff. The principal value of the tool appears to lie not only in the simulation itself but in the quality of the reflective feedback generated. Although based on a small sample, the observed pre–post changes provide an encouraging signal that may justify a controlled trial. The contribution of pre-session psychoeducation to training outcomes remains an important question for future research.