Pier Paolo Benedetti (2026) — (Independent/Doctoral Research). arXiv:2605.20665 [cs.HC] — Accepted at ICAIE 2026
Benedetti (2026) introduces a theoretically grounded framework for AI-enabled pedagogical accompaniment that explicitly centers human agency — an approach described as "human-in-command" rather than merely human-in-the-loop. The Amico prototype embodies five design principles: transparency of system identity and limits, scaffolding toward human contact, maieutic questioning, prevention of dependency dynamics, and data minimization. Each principle is mapped to observable indicators suitable for real educational settings.
The dual-mode design — AmicoMio for structured, task-oriented interaction and AmicoTuo for reflective, supportive engagement — represents a novel contribution to intelligent-tutoring architecture. Rather than pursuing a single interaction style, the system adapts its mode to the pedagogical context. This aligns with scaffolding theory's emphasis on calibrating support to learner needs and connects to recent work on ai-tutor-behavioral-evaluation that stresses context-sensitive deployment.
The cross-context pilot in Italy and China provides initial evidence of feasibility in vocational education, an underserved domain in the ai-k12-evidence-base. The framework's emphasis on AI as a "relational bridge" to human interaction — not a replacement — addresses concerns raised in ai-tutor-safety-harms about dependency and over-reliance. The principle of data minimization further connects to privacy and equity discussions in educational AI deployment. For faculty-development, the observable indicators offer concrete assessment tools for evaluating AI integration quality.
Related Pages
- intelligent-tutoring — related
- scaffolding — related
- human-in-the-loop — related
- ai-tutor-safety-harms — related
- ai-tutor-behavioral-evaluation — related
- over-reliance — related
- ai-k12-evidence-base — related
- faculty-development — related
- equity — related
- ai-literacy — related
Citation
APA: Pier Paolo Benedetti (2026). Design Principles and Observable Indicators for AI-Enabled Pedagogical Accompaniment: Evidence from the Amico Dual-Mode Prototype in Italy and China. arXiv:2605.20665. arXiv:2605.20665 [cs.HC] — Accepted at ICAIE 2026.