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Open Questions Towards Skill-Sustaining Reliance in Reflective AI Engagement

Sander de Jong

arXiv:2609.27726v1cs.HC

TL;DR

Research on reflective AI mechanisms has largely assessed short-term decision outcomes, leaving open whether they sustain professional expertise and independent reasoning over time. This paper discusses challenges and open questions about users, organisational conditions, and evaluation. It concludes that long-term viability depends on who uses reflective mechanisms, whether organisations support them, and whether they build lasting capacity.

  • Problem

    Reflective mechanisms have mainly been evaluated in single sessions, leaving uncertain whether they sustain professionals’ independent reasoning and expertise over time.

    Source: §1 · p. 1, §5 · pp. 2–3

  • Method

    The paper discusses four challenges and raises research questions about sustaining human agency and expertise through reflective human-AI interaction.

    Source: §1 · p. 1, §6 · p. 3

  • Results

    The paper concludes that reflective AI engagement’s long-term viability depends on who uses it, organisational support, and whether it builds lasting capacity.

    Source: §6 · p. 3

  • Takeaways & Limitations

    Evaluating skill sustainability requires attention to independent reasoning over time, not only immediate task performance, trust, or reliance measures.

    Source: §5 · pp. 2–3

    Most empirical work measures session-level outcomes, while longitudinal studies of reliance do not establish whether independent reasoning is maintained or reduced.

    Source: §5 · pp. 2–3

Abstract

from arXiv · show

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.

1 Introduction

Prior work finds that reflection strategies can reduce overreliance and improve decision quality, but their evaluation has largely focused on short-term outcomes. This paper raises the longer-term question of whether reflective AI interaction sustains professional expertise and agency.

1 Introduction

  • Reflection strategies such as pre-commitment, uncertainty communication, and cognitive forcing can mitigate overreliance and improve decision quality, but require workers’ effort.Studies have primarily evaluated these strategies as short-term interventions for task performance, trust calibration, or appropriate reliance.Source: §1 · p. 1
  • LLM rationales may support reflection, yet fluent incorrect outputs can appear credible and discourage critical engagement.In groups, LLM persuasiveness may amplify majority viewpoints, while effective use has also been associated with durable changes in beliefs and common ground,.Source: §1 · p. 1
  • The paper asks whether professionals’ expertise and independent judgement develop or erode through repeated reliance on reflective mechanisms.It notes that AI-assisted performance in-session does not establish whether clinicians retain diagnostic reasoning, and cites concerns about healthcare deskilling.Source: §1 · p. 1
  • The paper presents four challenges and open questions about the conditions for sustaining human agency and expertise through reflective human-AI interaction.Source: §1 · p. 1

2 Varying Effects of Reflective Support

Reflective support does not engage all users equally: its effects vary with users’ characteristics and task difficulty. Adaptive scaffolding could address these differences, but raises questions about professional autonomy and who decides when support is used.

2 Varying Effects of Reflective Support

  • Cognitive forcing reduced overreliance most prominently among participants with higher Need for Cognition, and engagement also varied with task difficulty.Participants engaged when they felt capable of reasoning independently, but otherwise relied on AI suggestions.Source: §2 · p. 1
  • Users weigh the effort of processing explanations against their perceived benefit, and effectiveness also depends on expertise, domain familiarity, and personal characteristics.Source: §2 · p. 1
  • Adaptive scaffolding could help novices develop independent reasoning, but assessing competence for adaptation may reduce professionals’ autonomy.Withholding AI suggestions may be tedious for experts who can critically evaluate advice; over-scaffolding may constrain them, while under-scaffolding may fail to support developing judgement.Source: §2 · p. 2
  • The paper asks when reflective scaffolding undermines autonomy and who should decide which professionals receive it.Source: §2 · p. 2

3 Reflection in Efficiency-Driven Workflows

Reflective mechanisms add time and cognitive effort, conflicting with workplace goals of speed, throughput, and reduced cognitive load. The paper asks how skill-sustaining reliance can persist amid organisational and competitive pressure.

3 Reflection in Efficiency-Driven Workflows

  • Reflective mechanisms introduce friction and cognitive effort, conflicting with professional-work goals of faster decisions, higher throughput, and reduced cognitive load.Source: §3 · p. 2
  • Without organisational recognition of reflection as an investment in long-term competence, productivity targets and the cumulative time cost of many daily decisions may undermine sustained use.The tension is particularly salient in high-stakes, risk-averse domains such as healthcare, where reflection and skill retention are linked to patient safety.Source: §3 · p. 2
  • Equivalent AI tools that omit reflective friction may pressure organisations toward faster alternatives, making skill-sustaining designs harder to justify.The paper asks whether this challenge calls for interaction design, organisational policy, governmental policy, or some combination.Source: §3 · p. 2

4 Sustaining Independent Metacognition

Reflective scaffolding may prompt short-term engagement, but repeated prompts could habituate users and substitute for their own metacognitive habits. The paper asks whether reliance on reflective prompts can itself become deskilling.

4 Sustaining Independent Metacognition

  • LLM rationales and prompts are intended to support reflection, but users may rely on single prompts without follow-up, while linguistic confidence can weaken self-monitoring.Socratic questions have been proposed to promote critical reflection in AI-assisted clinical decisions.Source: §4 · p. 2
  • Repeated reflective prompts may wear off through habituation, leading users to respond without critical engagement and potentially replacing independent metacognitive habits.Removing or changing the tool could leave professionals without AI support and without self-monitoring habits developed through independent practice.Source: §4 · p. 2
  • The paper asks whether relying on AI prompts to maintain reasoning practice may replace rather than strengthen professionals’ own metacognitive habits.Source: §4 · p. 2

5 From Short-Term Evidence to Long-Term Claims

Longitudinal studies show that trust and reliance evolve across repeated interactions, but do not establish whether independent reasoning is maintained. The paper asks how to act on promising short-term findings when their implications for sustained expertise remain unclear.

5 From Short-Term Evidence to Long-Term Claims

  • Repeated-interaction studies show trust and reliance are dynamic, but do not assess whether users’ independent reasoning is maintained or reduced.Trust development has been linked to AI accuracy, explanation type, and error timing; chess players’ engagement with AI feedback was associated with improvement or stagnation.Source: §5 · pp. 2–3
  • Longitudinal studies of trust and reliance do not show whether repeated AI interaction maintains or reduces independent reasoning.The existing studies are an important step toward longitudinal evaluation, but focus on appropriate reliance over time.Source: §5 · p. 3
  • AI support can improve task performance while reducing users’ ability to judge their own accuracy, and LLM access did not improve physicians’ diagnostic reasoning.The LLM outperformed the physicians, but that capability did not translate into better human reasoning.Source: §5 · p. 3
  • Q4 asks what evidence is sufficient to justify deploying reflective mechanisms—or deciding against deployment—when long-term evidence is insufficient.The paper contrasts evidence that reflective mechanisms activate reasoning in controlled short-term sessions with uncertainty about whether they sustain expertise longitudinally.Source: §5 · p. 3

6 Conclusion

The paper argues that reflective AI engagement’s long-term viability depends on conditions beyond interaction design, including who uses the mechanisms, organisational support, and whether they build lasting capacity.

6 Conclusion

  • Reflective AI engagement’s long-term viability depends on conditions beyond interaction design.The paper identifies users, organisational support, and lasting capacity as conditions relevant to whether reflective mechanisms sustain expertise.Source: §6 · p. 3
  • Whether reflective mechanisms sustain expertise depends on who uses them, organisational support, and whether they build lasting capacity.Source: §6 · p. 3
  • The questions are offered as a starting point for discussion about sustaining human agency and expertise in AI-supported work.Source: §6 · p. 3

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