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Purpose: This study investigates the impact of activity-based learning and the utilization of ChatGPT on students' academic performance within the educational framework. Objectives: The study aims to assess the effectiveness of activity-based learning in comparison to traditional methods, while also evaluating the potential benefits and drawbacks of integrating ChatGPT as an educational tool. Methods: The study employs a comparative approach, analyzing the outcomes of students exposed to activity-based learning versus those using conventional methods. Additionally, the study examines the usage of ChatGPT in education through surveys and trials to determine its contribution to personalized feedback, interactive learning, and innovative teaching methods. Results: The findings reveal that activity-based learning enhances students' engagement, motivation, and critical thinking skills. Students participating in activity-based learning demonstrate improved academic achievement, which is attributed to their active involvement and practical application of knowledge. Similarly, the integration of ChatGPT offers novel avenues for interactive learning and individualized assistance, fostering students' understanding and exploration of complex concepts. Conclusion: In conclusion, activity-based learning proves to be a student-centered approach that enhances learning outcomes by fostering active participation and practical engagement. The utilization of ChatGPT in education showcases its potential to enhance educational experiences through interactive conversations and innovative teaching methodologies, despite considerations regarding potential limitations and ethical implications.

This research endeavor delves into the utilization of non-orthogonal multiple access (NOMA) techniques within the context of optical wireless sensor networks (OWSNs). The study entails an exploration of the viability of integrating NOMA into OWSNs and introduces a novel architectural framework that amalgamates successive interference cancellation (SIC) and superposition coding methodologies. This amalgamation aims to enhance the overall performance and spectral efficiency of the network. The investigation meticulously assesses the performance of the proposed architecture, employing diverse metrics such as bit error rate (BER), outage probability, and environmental impact. Empirical results underscore the commendable precision of the NOMA-based architecture when juxtaposed with conventional orthogonal multiple access (OMA) approaches. This distinction is particularly pronounced in scenarios involving a substantial number of sensor nodes. Furthermore, the research undertakes a comprehensive scrutiny of varying system parameters, such as user densities and power allocation factors, to gauge their influence on network accuracy. The discerned outcomes elucidate NOMA’s capacity to augment the operational efficacy of optical wireless sensor connections. The proposed architectural paradigm thus emerges as a promising avenue for optimizing resource allocation and utilization within OWSNs, imparting newfound potential to a spectrum of applications spanning industrial automation, environmental monitoring, and smart city implementations.