The rapidly evolving job market and the growing number of students entering the workforce present significant challenges for delivering career guidance in higher education. Traditional career counseling, involving face-to-face interactions with counselors, is facing a lot of limitations in efficiency and personalization [1]. The rise of Artificial Intelligence (AI) in the 21st century offers new solutions for complex pattern recognition and multi-factor matching. This research explores the transformative potential of AI in career guidance for future graduates through a comparative analysis of the three possible approaches: traditional counseling, AI-integrated counseling, and personalized AI-integrated counseling. Building on previous research of both traditional and AIintegrated counseling, this study investigates the potential of personalized AI through the design and implementation of an AI-driven web application crafted to empower future graduates in their career journeys. The platform collects comprehensive data via a structured questionnaire and an application programming interface (API) to generate a personalized recommendation. These recommendations are then assessed for accuracy against traditional counseling Moreover, a feedback platform is implemented to measure applicants’ satisfaction. Since findings show that while traditional counseling offers valuable emotional support, it lacks the efficiency and scalability of AI solutions. This paper highlights the need for an AI-personalized model that combines AI’s analytical strengths with the empathetic insights of human counselors, allowing for a data-driven personalized career advising.

With the rise of social media and artificial intelligence, AI-generated misinformation is rapidly eroding public trust, blurring the line between truth and deception. George Orwell once said, “Who controls the past, controls the present and who controls the present controls the future” showcasing the crucial role that information plays in shaping the public opinion, thus the future. Misinformation has led to Orwell’s dystopian society, with the people blindly accepting what has been given to them. With the rise and politicization of AI, correlates an increasingly issue of misinformation synonymously, as AI has allowed the quick generation of information, whether false or true. This has caused one of the greatest false information crises in human history: Artificial intelligence has dominated modern social media with its generated false information. Building its analysis on previous research, this paper compares a few solutions to the previously mentioned problem that have gained recognition. Specifically, this paper examines three key solutions: AI detection tools, fact-checking initiatives, and media literacy programs, evaluating them based on effectiveness, scalability, feasibility, and cost. This study concludes by proposing a hybrid approach, combining AI-powered detection for immediate mitigation with media literacy for long-term resilience. By integrating technology with education, this strategy ensures a proactive and sustainable response to AI-driven misinformation.

Digital badges are an effective avenue for students to obtain recognition for their achievements. Current digital badging platforms are mainly developed to assist earners in sharing their achievements on social media platforms. These systems focus less on in-demand skills required by the labor market. However, our proposed system introduces a criteria-based badging system, requiring layers of evaluations and verifications to assure originality and quality in the process of earning a single badge. As a result, students can engage further in courses that will benefit them in terms of job readiness and preparedness. This leads students to earn badges associated with top in-demand skills required by the labor market. In turn, this can increase students’ opportunities to obtain jobs related to their skills. These badges can influence recruiting decisions because employers may find and display their required candidate qualifications and skills via recruiting channels.

Chronic diseases such as heart diseases, cancer, diabetes, and asthma continue to increase in general public these days. With monitoring of observed symptoms along with reasonable levels of knowledge on those diseases, those may be detected early and managed properly. For that to happen, the awareness of the symptoms and proper knowledge about the diseases need to be provided to each individual. In order to acquire related health knowledge, individuals may need to collect necessary health information from various sources such as the Internet, articles, or some type of e-learning systems. However, the available information is often overwhelming and is mostly unorganized. Patients or learners are then struggling to find a way to retrieve relevant health information from such unorganized chunks of collected information. In this study, we attempt to provide only the relevant learning materials specific to each individual's symptoms or clinical conditions through a carefully designed e-learning system that provides customized learning. In our approach, we utilized observed symptoms and vital signs to identify potential diseases of each patient. Such factors are used to build patient profiles that are used to provide specific sets of learning materials called study plans. Such customized learning enables patients to take control of their symptoms and potential diseases to help improve patient outcomes as a result.

Patient generated data or personal clinical data in general is considered an important aspect in improving patient outcomes. However, personal clinical data is difficult to collect and manage due to their distributed nature, i.e., located over multiple places such as doctor's office, radiology center, hospitals, or some clinics, and heterogeneous data types such as text, image, chart, or paper based documents. In case of emergency, this situation makes necessary personal clinical data retrieval almost impossible. In addition, since the amount and types of personal clinical data continue to grow, finding relevant clinical data when needed is getting more difficult if no actions are taken. In response to such scenarios, we propose an approach that manages personal health data by utilizing meta-data for organization and easy retrieval of clinical data and cloud storage for easy access and sharing with caregivers to implement the continuity of care and evidence-based treatment. In case of emergency, we make critical medical information such as current medication and allergies available to relevant caregivers with valid license numbers only.