The use of Generative AI(GAI) as an embedded tool in traditional teaching processes has brought about new revolutionary ways that are changing the nature of educational activity. As we continue to evolve through phases of innovation, an increasing number of educators are seeking to incorporate AI into their teaching to enhance student creativity, streamline certain aspects of the teaching/learning process, and improve overall learning outcomes. Although existing research focuses on academic performance and engagement, the objective of this research is to identify the impact on the creative quotient of students in a real-time learning environment. Through an extensive real-time survey, students' perceptions and experiences of AI-driven educational tools were assessed. Therefore, the results collectively provide an all-encompassing perspective of how GAI is transforming education and offer potential paths toward future innovations and developments within the teaching and learning environment. An integration of both AI and traditional teaching methods will promote a balanced and forward-thinking educational model. There is insufficient real-time evidence on whether GAI influences creativity or leads to dependence on the technology. The novelty of this research aims at measuring and comparing the influences of GAI with traditional learning methods and outcomes. This proposed research work will be evaluated using real-time data collected from various university students via a research study. Real-time analysis of the data will provide insight into how GAI has impacted the creative quotient of students who prefer to utilize modern tools over the traditional teaching-learning methodology.
Renuka Devi D, Midhun Chakkaravarthy· International Journal of Ele...· 0 citations
Energy efficiency remains a major challenge in deploying IoT systems, especially in scenarios requiring large numbers of devices while balancing computational requirements and operational lifetimes. This paper proposes a federated reinforcement learning framework for adaptive load balancing in the edge-fog-cloud continuum that optimizes energy efficiency and supports diverse quality of service requirements. The proposed framework addresses the limitations of traditional centralized machine learning approaches that require collecting sensitive operational information and transmitting it to cloud servers for centralized analysis. This increases the risk of privacy violations and introduces communication overheads that limit the responsiveness of IoT systems. The proposed framework employs a federated reinforcement learning approach, enabling edge nodes to collaboratively learn an optimal load-balancing policy without transmitting operational information. The proposed framework uses a context-aware reward function that optimizes multiple objectives based on temporal patterns, device energy levels, and application criticality. This enables the proposed framework to adapt its optimization objectives and balance energy efficiency and performance maximization. The proposed framework introduces a new action-space pruning mechanism that accelerates the optimization process by leveraging domain knowledge of possible load-balancing patterns. The proposed framework uses a distributed experience replay buffer to reduce trial-and-error in reinforcement learning. The proposed framework demonstrates its effectiveness in optimizing energy efficiency through a series of experiments in a real-world IoT environment and a centralized machine learning approach. The proposed framework demonstrates that distributed machine learning approaches can outperform centralized ones for optimizing energy efficiency in IoT systems.
Si Liu, Midhun Chakkaravarthy· Future Technology· 0 citations
DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management, is presented.
Si Liu, Midhun Chakkaravarthy· Future Technology· 0 citations