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Principle-to-indicator mapping as an accountability device for AI-enabled pedagogical systems

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The framework translates pedagogical design principles into observable indicators using a compact mapping (Table I) from principle to indicator to observation method: transparency of identity and limits (misinterpretation rate via survey/observation), scaffolding toward humans (transition-to-human prompts and follow-through signals via logs/observation), directional micro-mediations (micro-action formulation and completion via logs/rubrics), prevention of dependency (looping and exclusive-use signals via logs), communicative autonomy support (goal/question articulation quality via rubrics), and data minimization (sensitive-data solicitation frequency, which should be near zero). The mapping is treated as an accountability device making the system's pedagogical function testable through concrete traces, structured judgments, and participant perceptions.

Accounts

  • Principle-to-indicator mapping as an accountability device for AI-enabled pedagogical systems: The framework translates pedagogical design principles into observable indicators using a compact mapping (Table I) from principle to indicator to observation method: transparency of identity and limits (misinterpretation rate via survey/observation), scaffolding toward humans (transition-to-human prompts and follow-through signals via logs/observation), directional micro-mediations (micro-action formulation and completion via logs/rubrics), prevention of dependency (looping and exclusive-use signals via logs), communicative autonomy support (goal/question articulation quality via rubrics), and data minimization (sensitive-data solicitation frequency, which should be near zero). The mapping is treated as an accountability device making the system's pedagogical function testable through concrete traces, structured judgments, and participant perceptions. (Pier Paolo Benedetti (2026))

Claims

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Key Sources

  • 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. Accepted at the 2026 International Conference on Artificial Intelligence and Education (ICAIE 2026), IEEE.
Page metadata
Field Value
type research-method
id principle-to-indicator-mapping-as-an-accountability-device-for-ai-enabled-pedagogical-syst
title Principle-to-indicator mapping as an accountability device for AI-enabled pedagogical systems
description The framework translates pedagogical design principles into observable indicators using a compact mapping (Table I) from principle to indicator to observation method: transparency of identity and limits (misinterpretation rate via survey/observation), scaffolding toward humans (transition-to-human p
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generated by process:settle-candidates on 2026-10-10
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