Advances in Computational Intelligence and Robotics
December 12, 2025
Ashraf Alam
This chapter proposes a framework for using AI in education that prioritizes human control. The authors argue that AI should be treated as a simulation, not a sentient being, and outline rules for separating functional mimicry from genuine consciousness. They suggest that embodiment and presence can enhance learning without encouraging personification of AI. Affective computing should be limited by consent, transparency, and human oversight. The authors propose a governance system with auditable controls and incident forensics to ensure fairness and safety. The result is a practical guide for implementing AI in classrooms and policy.
Advances in Computational Intelligence and Robotics
December 12, 2025
Soumi Ghosh, Ritik Raj
Advanced AI systems have sparked philosophical and scientific debate about machine consciousness. This theoretical paper reviews Integrated Information Theory, Global Workspace Theory, and Higher-Order Thought Theory, identifying gaps between behavioral competence and phenomenal experience. A multidimensional framework beyond the Turing Test is proposed. Current AI lacks unified information integration, temporal self-continuity, and self-purpose intention necessary for consciousness. A taxonomy of consciousness indicators is suggested, along with research gaps requiring interdisciplinary work. The authors conclude that achieving machine consciousness requires architectural creativity, not merely scaling existing systems.
Advances in Computational Intelligence and Robotics
December 12, 2025
Anchit Jhamb
The chapter examines the relationship between simulation and sentience in computational consciousness, focusing on the explanatory gap between functional intelligence and subjective experience. It explores philosophical foundations, including the hard problem of consciousness, and evaluates computational models such as symbolic AI, neural networks, and embodied agents. Key theories of consciousness—Integrated Information Theory, Global Workspace Theory, and predictive processing—are integrated and assessed for their relevance to artificial systems. Ethical and epistemological challenges, including verification of machine sentience, anthropomorphism, and societal responsibilities, are analyzed.
Advances in Computational Intelligence and Robotics
December 12, 2025
Arpita Nayak, Ipseeta Satpathy, Vishal Jain
The essay examines qualia—the subjective, ineffable aspects of experience—as fundamental elements of consciousness that shape affect and interpretation. It critically reviews classical arguments by Thomas Nagel, Daniel Dennett, and David Chalmers on the ontology and explanatory challenge of qualia. Dennett opposes their existence, arguing the concept dissolves once mental operations are understood, while Nagel defends the epistemic inaccessibility of subjective experience via his 'what it is like' formulation. Chalmers highlights the hard problem of consciousness, where materialism cannot explain subjective experience. The analysis explores whether computing architectures can bridge the gap between information processing and genuine experience.
Advances in Computational Intelligence and Robotics
December 12, 2025
Saakshi Anand
Consciousness is fundamentally rooted in embodied, context-dependent, and experiential structures rather than emerging from disembodied computation. This chapter explores the intersection of artificial intelligence, embodied cognition, and phenomenology, focusing on qualia—the subjective, first-person qualities of experience. It advances an integrative framework that challenges reductionist and purely mechanistic views of cognition by bridging computational models of intelligence with phenomenological analyses of perception and awareness. Drawing from Husserl, Heidegger, and Merleau-Ponty alongside connectionism and enactivism, the argument positions embodied, experiential structures as central to understanding consciousness.
Advances in Computational Intelligence and Robotics
December 12, 2025
Gaganpreet Kaur, Amandeep Kaur, Ramandeep Sandhu
John Searle's Chinese Room Argument challenges the idea that machines can possess genuine understanding or consciousness. The thought experiment places a human operator in a room who uses rulebooks to respond in Chinese without understanding the language, illustrating how a system can manipulate symbols (syntax) without grasping their meaning (semantics). Searle argues that computation alone is insufficient for mental states or qualia. This chapter examines the argument's core logic and explores responses and critiques, including the Systems Reply, Robot Reply, and contemporary counterarguments from functionalism, integrated information theory, and embodied cognition, offering a balanced examination of philosophical, cognitive, and computational perspectives.