Date
Publisher
arXiv
AI-enabled systems are increasingly introduced into educational contexts, yet their effectiveness depends less on technological sophistication than on the quality of pedagogical mediation, ethical constraints, and context-sensitive design. This paper proposes a replicable framework for AI-enabled pedagogical accompaniment, grounded in a human-in-command approach in which adult responsibility remains central and AI functions as an enabling, non-substitutive infrastructure. Building on the Amico project, we operationalize the concept of a relational bridge as a sequence of micro-mediations that lower the threshold of access to educational relationships and facilitate transitions toward meaningful human interaction with teachers, peers, and communities of practice. The contribution synthesizes a set of design principles, including transparency of system identity and limits, scaffolding toward human contact, maieutic questioning, prevention of dependency dynamics, and data minimization, and maps them to observable indicators suitable for real educational settings. The paper also outlines an initial cross-context exploration of the prototype in Italy and China and discusses how the two interaction modes, AmicoMio (structured, task-oriented) and AmicoTuo (reflective, supportive), can be used as complementary pedagogical mediations. Pilot observations and participant feedback suggested feasibility and perceived usefulness in vocational contexts, motivating the present framework, informing the subsequent doctoral research program, and supporting the proposed collaborative research agenda.
Study at a glance
- Question: How can AI tools support learners while steering them toward human relationships rather than replacing them?
- Setting and data: Italy (12 participants) and China (18 participants); 30 learners and adults across ages 6 to 65 in vocational and technical settings; 20 pilot sessions.
- Method: Exploratory mixed-methods design-based prototype study combining interaction logs, rubrics, surveys, and facilitator observations.
Why it matters
AI often lacks pedagogical framing, risking relational fragility and over-reliance. Few studies provide replicable design principles linked to observable indicators that educators can use to distinguish supportive mediation from substitution in real educational settings.
Key findings
- Participants across Italy and China accepted the dual-mode AI prototype as a bounded support tool and experienced no major misunderstandings regarding its non-human identity.
- Learners perceived the two interaction modes as complementary, using the structured mode for task clarity and the reflective mode for clarifying goals and managing uncertainty.
- Interactions remained bounded within design limits without fostering dependency, and qualitative rubrics indicated improvements in how learners articulated goals and prepared questions for teachers.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
