The post-pandemic landscape has ushered in a transformative phase in education, compelling a profound reexamination of the humanities' role. This study navigates the multifaceted dimensions of humanities education, including its response to digital transformation, interdisciplinary methodologies, emotional intelligence cultivation, inclusivity efforts, and its forward trajectory. Employing qualitative methods, including innovative approaches such as advanced data analysis techniques, intensive in-depth literature reviews, maximum purposeful sampling, informal interviews among thirty (30) participants from SMC, observation, and thematic analysis, this research captures diverse perspectives. Leveraging evolving research methodologies, the study resonates implications for educators and institutions, envisioning a humanities education poised for resilience and responsiveness, sculpted by the pandemic's challenges and the potential it has unveiled.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.