[CogSci] Proposal for a Symposium on Artificial Metacognition

[Christian Lebiere]

Paulo Shakarian (Syracuse), Nate Bastian (West Point, DARPA), Francesco
Restuccia (Northeastern), Arslan Basharat (KitWare) and I are preparing a
proposal for a “Symposium on Artificial Metacognition” at the 2026 AAAI
Fall Symposium Nov. 5-7.  A draft description is pasted below.



If you would like to help us with the proposal, please fill out this
1-question survey to voice your support: https://forms.gle/DuHB7xpGqSScYSPz9



If you want to get more involved, please send us an email – we would be
looking for:



   - Program committee members
   - Keynote speaker nominations
   - Sponsorship
   - Any advice based on our last two METACOG workshops



Thank you again for your support – and we look forward to getting this
approved and seeing you all in Arlington in November!



- Paulo, Nate, Francesco, Arslan and Christian



*---*


*METACOG-26: Symposium on Artificial Metacognition*

*Proposal for the AAAI 2026 Fall Symposium Series*

November 5–7, 2026  •  Westin Arlington, Arlington, VA

*1. Symposium Description*

Artificial Metacognition is the ability for an AI system to reason about
itself.  The idea is based on a concept in cognitive psychology [1, 2]. In
the early 2000s, AAAI held several symposia on this topic [3, 4]. Recently,
due to the advent of the LLM, neurosymbolic AI, and the need for more
robust AI systems, this topic has re-emerged in a series of small venues
[5, 6] and most recently an “emerging trends” talk at AAAI-2026 [7].  The
study of metacognition goes beyond related topics such as
out-of-distribution detection and uncertainty quantification by not only
detecting when a model could potentially be in an error mode, but
determining aspects about security, computational efficiency, power usage,
explainability, and corrective action in a unified, often cognitively
inspired, framework.  Recent events have brought together researchers from
computer science, cognitive psychology, electrical engineering, mechanical
engineering, systems engineering, and mathematics.  The proposed Symposium
on Artificial Metacognition will continue this exploration by inviting
papers featuring a variety of methodologies that have been explored in the
recent literature, including stress testing of robotic systems, model
introspection, model certification, performance prediction, critique models
for LLMs, metacognitive rule learning, and metacognitive extensions to
cognitive architectures such as ACT-R, SOAR and the Common Model of
Cognition.

*2. Objectives*

The objectives of the symposium are as follows:

   - Survey and synthesize current approaches to metacognition in AI
   systems, including monitoring, control, and metareasoning.
   - Understand the requirements for and trade-offs among various
   metacognitive approaches.
   - Identify novel methods for metacognition that improve AI performance
   in operational, out-of-distribution, and cross-domain settings.
   - Identify application areas suitable for the deployment of
   metacognitive methods, including autonomy, cyber, vision, robotics, and
   decision support.
   - Foster cross-disciplinary collaboration between AI, cognitive
   psychology, cognitive modeling, control theory, and systems engineering.
   - Examine the relationship between AI metacognition and human operators,
   including trust, calibration, and human-AI teaming.

*3. Topics of Interest*

Specific topics to be covered include, but are not limited to:

   - AI Agents with Metacognition (LLM-based agents, autonomous agents, and
   embodied agents that self-monitor and self-regulate).
   - Cognitive model architectures with metacognitive extensions (e.g.,
   ACT-R, Soar, the Common Model of Cognition).
   - Metacognitive rule learning (data-driven and neuro-symbolic learning
   of error-detection and constraint rules).
   - Critique models (training, evaluation, and deployment of models that
   produce natural-language feedback on the outputs of other AI systems).
   - Explainable performance prediction of black-box AI systems.
   - Stress testing of reinforcement learning and perception systems.
   - Metacognitive monitoring vs. metacognitive control, including
   metareasoning and resource regulation.
   - Neuro-symbolic AI architectures for metacognition.
   - Self-adaptive, self-healing, and self-repairing AI systems for new
   domains.
   - Out-of-distribution detection, abductive inference, and
   consistency-based verification as metacognitive cues.
   - Trust calibration, human-in-the-loop metacognition, and human-AI
   teaming.
   - Datasets, benchmarks, and evaluation methodology for metacognitive AI.
   - Applications of metacognitive AI to autonomy, robotics, cyber
   operations, and decision support.

*4. Symposium Format*

METACOG-26 will be a 2.5-day symposium following the standard AAAI Fall
Symposium structure. Programming will combine traditional paper sessions
with extended discussion, a poster session, two invited keynotes, and a
closing panel. This structure is consistent with the symposium series’
emphasis on intimate forums and substantive discussion. We anticipate
accepting up to 14 full papers (oral) and up to 12 posters. Submission will
be peer-reviewed by the organizing committee and a small program committee
drawn from the prior workshops. Accepted papers will be published in the
AAAI Technical Report series.
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