Open Questions Towards Skill-Sustaining Reliance in Reflective AI Engagement

2026-09-23

AI Summary

研究探讨如何通过长期有效的反思机制设计，解决AI辅助决策中的人类技能持续性问题，考虑个体差异和组织条件。

Abstract

As AI systems are increasingly integrated into professional work, reflection strategies such as cognitive forcing and prompts that foster critical engagement have shown promise in reducing overreliance and improving decision quality. However, these strategies have primarily been evaluated as short-term interventions within single sessions. The next challenge is to assess whether such mechanisms sustain human agency and expertise over time. Drawing on prior work in AI-assisted decision-making, metacognition, and reflective AI engagement, we examine the challenges of designing and evaluating reflective mechanisms for long-term skill sustainability, considering individual differences in how users engage with such support, the organisational conditions under which it is implemented, and the gap between short-term evidence and long-term claims. We introduce open questions for the research community about the conditions under which reflective AI engagement can be sustained in practice.

Problem

• reflective AI engagement
• skill sustainability
• long-term effects

Innovation

• reflective AI engagement
• long-term skill sustainability
• cognitive forcing
• metacognition