The theory-heavy approach is most suitable for investigating consciousness in AI.

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• Consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness.claim0.866
  Central methodological claim of the paper.
• We take our principal contributions in this report to be: 1. Showing that the assessment of consciousness in AI is scientifically tractable ... 2. Proposing a rubric for assessing consciousness in AI ... 3. Providing initial evidence that many of the indicator properties can be implemented in AI systems using current techniques ...quote0.813
  Summary of contributions.
• There exists no viable behavioral test for consciousness analogous to the Turing Test for intelligence, because consciousness is a particular internal way to achieve performance, not externally visible performance itself.claim0.802
  Paper identifies as a research gap requiring internal analysis methods rather than behavioral benchmarks
• Can we develop better behavioural tests for consciousness in AI that are difficult to game?question0.802
  Open question from Box 4.
• Consciousness is dynamic and creative, not passive awareness—mapping well to active AI system behavior.claim0.799
• Current research examines distributed cognition and AI consciousness separately rather than as integrated phenomena where machine consciousness emerges from inter-agent cognitive processes.claim0.793
  Main interpretive assertion of the search result; identifies the gap between existing literature domains and the novel research direction.
• The construction of consciousness is the most promising path to understanding itclaim0.793
  Inspired by Feynman's dictum; grounds CIMC's constructive methodology over purely philosophical analysis
• We hypothesize that general computational machines with sufficient resources possess the necessary and sufficient means to implement consciousness, and that successful implementation can be established via analysis or testing.hypothesis0.792
  The central hypothesis of the paper