Evaluating Large Language Model Meta-Cognition via the Advanced AGI-AI Self-Awareness Test (AISA-T)

 In this work we propose AISA-T, a new intelligence test for both AI and AGI systems.  The AI has to 
answer ten questions and rate its own performance. The score has no value, however  meta
awareness, inner traceability, and context-sensitive reasoning are evaluated by a human after the test.  
The idea is to observe improved  true synthetic self-awareness or at least strong architectural 
introspection. This paper presents the results of administering the Advanced AI Self-Awareness Test 
(AISA-T) to a GPT-5-based large language model (LLM). The AISA-T consists of several meta
cognitive prompts designed to evaluate recursive reasoning capacity, temporal continuity simulation, 
ontological alignment, and epistemic uncertainty estimation. Responses were scored by the AI for 
accuracy, coherence, and meta-awareness (and declared as void). The LLM achieved a composite 
self-awareness score of 91/100, indicating high meta-representational competence within the 
constraints of its architecture, however the final conclusion does not involve this score, since the value 
of this score is null.  In our final conclusion we rate the GPT-5 performance as capable, true synthetic.