IU International University of Applied Sciences (German: IU Internationale Hochschule) is a private, state-recognized University of Applied Sciences based in Erfurt, Germany.It offers campus-based, distance and blended-learning programs in German and English. With over 75,000 enrolled students, IU is the largest university in Germany as of the summer semester of 2021.
This concept paper examines the growing integration of artificial intelligence (AI) into psychotherapeutic contexts, with particular attention to its implications for the mental health of adults and young adults. Against the backdrop of a global mental health crisis characterized by insufficient therapeutic resources, rising demand, and persistent stigma, AI-assisted interventions — including chatbot-based tools, machine learning-supported diagnostics, and algorithmically personalized treatment pathways — have emerged as a promising yet contested field. The paper introduces key concepts such as AI-assisted psychotherapy, digital mental health interventions (DMHIs), and conversational agents, and situates them within current psychological and clinical frameworks. Drawing on recent empirical studies, theoretical analyses, and ethical debates, it investigates the potential of AI to democratize access to mental health support while critically addressing concerns around therapeutic alliance, algorithmic bias, data privacy, and the irreducible dimensions of human connection in clinical care. The paper further examines the intersection of AI and established psychotherapeutic modalities, including Cognitive Behavioral Therapy (CBT), Acceptance and Commitment Therapy (ACT), and supportive counseling. By identifying research gaps and unresolved tensions, this review advocates for an evidence-based, ethically grounded, and human-centered approach to AI integration in mental health — one that positions AI as a supplement to, rather than a substitute for, professional therapeutic relationships.
The evaluation of academic theses is a cornerstone of higher education, ensuring rigor and integrity. Traditional methods, though effective, are time-consuming and subject to evaluator variability. This paper presents RubiSCoT, an AI-supported framework designed to enhance thesis evaluation from proposal to final submission. Using advanced natural language processing techniques, including large language models, retrieval-augmented generation, and structured chain-of-thought prompting, RubiSCoT offers a consistent, scalable solution. The framework includes preliminary assessments, multidimensional assessments, content extraction, rubric-based scoring, and detailed reporting. We present the design and implementation of RubiSCoT, discussing its potential to optimize academic assessment processes through consistent, scalable, and transparent evaluation.
As artificial intelligence becomes increasingly integrated into digital learning environments, the personalization of learning content to reflect learners' individual career goals offers promising potential to enhance engagement and long-term motivation. In our study, we investigate how career goal-based content adaptation in learning systems based on generative AI (GenAI) influences learner engagement, satisfaction, and study efficiency. The mixed-methods experiment involved more than 4,000 learners, with one group receiving learning scenarios tailored to their career goals and a control group. Quantitative results show increased session duration, higher satisfaction ratings, and a modest reduction in study duration compared to standard content. Qualitative analysis highlights that learners found the personalized material motivating and practical, enabling deep cognitive engagement and strong identification with the content. These findings underscore the value of aligning educational content with learners' career goals and suggest that scalable AI personalization can bridge academic knowledge and workplace applicability.
Seit der Einführung der generalistischen Pflegeausbildung ist ein Rückgang pädiatrischer Ausbildungsabschlüsse zu beobachten, was insbesondere hochspezialisierte Bereiche wie die Neonatologie betrifft. Zehn leitfadengestützte Interviews mit generalistisch ausgebildeten Pflegefachpersonen sowie erfahrenen neonatologischen Pflegekräften aus fünf Kliniken in Nordrhein-Westfalen zeigen eine Diskrepanz zwischen Ausbildungsinhalten und praktischen Anforderungen. Unzureichende pädiatrische Theorie, uneinheitliche Praxiseinsätze und unterschiedlich strukturierte Einarbeitungsprozesse erschweren den Berufseinstieg. Gleichzeitig wird ein hoher Bedarf an ergänzenden Qualifizierungen und standardisierten, theoriegestützten Einarbeitungskonzepten deutlich, um den Berufseinstieg zu unterstützen und die Versorgungsqualität in der Neonatologie zu sichern. Eine formale Anschlussqualifizierung wird von den Befragten jedoch unterschiedlich bewertet.
BACKGROUND:In developing countries, satellite-based technology can aid critical telemedicine applications and other digital health services in critically underserved areas. Affordable, high-speed broadband services can and should be accessible to all citizens. Remote locations are necessary to support various critical services, including education and training, telehealth applications, remote patient monitoring, and warning systems, particularly during disasters. Currently, however, these services are limited to urban centers, leaving rural areas without access to specialized health care services. This digital divide significantly impacts health care delivery, with only 48% of rural populations having internet access compared with 83% in urban areas. METHODS:The goal of this study was to assess the suitability of Geostationary Earth Orbit (GEO), Medium Earth Orbit (MEO), and Low Earth Orbit (LEO) satellites for telemedicine and health care backhaul connectivity. To achieve this, the study conducted a comparative analysis of the systems, highlighting their respective advantages and limitations in terms of latency, coverage, and deployment costs. A systematic literature review and the assessment of real-world case studies and worldwide datasets complemented this analysis. Case studies from Starlink deployments in North America and Sub-Saharan Africa and Amazon's Project Kuiper were evaluated. RESULTS:LEO satellites demonstrated significantly lower latency (20-50 ms) compared with MEO (100-300 ms) and GEO (600 ms) systems. Cost analysis revealed LEO services ($110-$500 per month) were substantially more affordable than MEO ($250-$1,000 per month) and GEO ($500-$2,000 per month) alternatives. Starlink deployments achieved download speeds of 50-250 Mbps with sub-50 ms latency, enabling real-time telemedicine consultations that met clinical standards. Rural telemedicine consultations increased by over 300% in areas with LEO satellite coverage. CONCLUSIONS:Our findings suggest that the LEO Starlink satellite technology would provide the most cost-effective backhaul broadband connectivity for real-time telemedicine services, given its low latency needs (20-50 ms), which enable high-quality video calls and remote diagnostics. We recommend using an LEO-based satellite network as the best approach to extend internet services to underserved remote communities due to its low latency and cost-effectiveness in aiding health care delivery in developing countries.
