This study explores a phased approach toward developing conscious-like behaviors in artificial intelligence systems by integrating functional organization, continuous learning, sensory-motor integration, and hybrid biological inspiration. Starting with structured representations and adaptable learning, the study proposes evaluation metrics—such as clustering entropy, self-model coherence, and counterfactual reasoning—to systematically assess conscious-like attributes in AI. As AI systems evolve, ethical and philosophical considerations are addressed, emphasizing autonomy, potential suffering, and accountability. Through frameworks like the Artificial Consciousness Ethics (ACE) model and adherence to the precautionary principle, this study highlights the need for responsible AI development. Future directions are presented, focusing on advancements in neuromorphic and quantum-inspired computing, embodied cognition, and ethical governance. The research suggests that interdisciplinary collaboration across neuroscience, philosophy, and ethics will be essential in ensuring AI consciousness progresses within ethical boundaries, with the potential to deepen our understanding of cognition and the nature of conscious experience.