TY - CHAP A1 - Albrecht, Jens A1 - Rudolph, Eric A1 - Poltermann, Aleksandra A1 - Lehmann, Robert T1 - The Virtual Client: Leveraging Generative AI for Innovative Online-Counselor Education T2 - ICERI Proceedings N2 - In an increasingly intricate world, individuals turn to online platforms for guidance from trained counselors regarding various psychological and social challenges. The training of competent and effective counselors is challenging, requiring specific strategies, methods and realistic role-playing scenarios. VirCo (Virtual Client for Online Counseling) is an innovative educational platform that employs an open-source large language model (LLM) to simulate clients facing psycho-social problems. VirCo enables learners to engage with a variety of client personas through realistic conversations, thereby enhancing their counseling abilities in a self-directed manner. Furthermore, it features tools for course development, assignments, and multiple feedback systems, fostering a collaborative learning environment. This innovative use of AI technology offers scalable and practical training solutions, contributing to a skilled and proficient workforce in online counseling. KW - Education, Role-Play, Counseling, Generative AI Y1 - 2024 U6 - https://doi.org/10.21125/iceri.2024.0454 SN - 2340-1095 SP - 1522 EP - 1531 PB - IATED ER - TY - CHAP A1 - Lehmann, Robert A1 - Albrecht, Jens A1 - Zauter, Sigrid T1 - Die Computerunterstützte Analyse Sozialwissenschaftlicher Texte – Ergebnisse des Forschungsprojekts „Casotex“ T2 - Gegenwart und Zukunft sozialer Dienstleistungsarbeit N2 - Ein Datensatz aus mehreren deutschsprachigen Onlineberatungsforen wurde mit verschiedenen maschinellen Lernverfahren untersucht. Dabei können unüberwachte Lernverfahren nur in Bezug auf die linguistische Gestaltung der Beiträge relevante Ergebnisse zeigen. Überwachte maschinelle Lernverfahren zeigen ein sehr großes Potenzial zur Klassifikation von Textstellen analog zur qualitativen Inhaltsanalyse. Damit eignen sich maschinelle Lernverfahren prinzipiell für die qualitative Analyse großer Textmengen, wie sie z. B. in der Onlineberatung vorliegen. Y1 - 2021 SN - 9783658325558 U6 - https://doi.org/10.1007/978-3-658-32556-5_12 SN - 2569-2127 SP - 167 EP - 180 PB - Springer Fachmedien Wiesbaden CY - Wiesbaden ER - TY - CHAP A1 - Poltermann, Aleksandra A1 - Lehmann, Robert A1 - Albrecht, Jens ED - Macsenaere, Michael T1 - Machine-Learning-Verfahren als Forschungsinstrument in der Kinder- und Jugendhilfe T2 - Künstliche Intelligenz in der Kinder- und Jugendhilfe N2 - Künstliche Intelligenz (KI) eröffnet Fach- und Führungskräften im Bereich der Kinder- und Jugendhilfe weitreichende Möglichkeiten im Praxisalltag. Anhand von Fallstudien und konkreten Anwendungsbeispielen zeigen die Autoren, wie KI in der Sozialen Arbeit konkret eingesetzt werden kann, um Arbeitsprozesse zu optimieren, Arbeitszeit zu reduzieren und individuelleres pädagogisches Handeln zu ermöglichen. Dabei werden auch ethische Aspekte und Anforderungen an den Datenschutz kritisch beleuchtet und es wird diskutiert, wie KI nachhaltig und verantwortungsvoll zum Wohle junger Menschen eingesetzt werden kann. KW - Maschinelles Lernen KW - Jugendhilfe KW - Künstliche Intelligenz KW - Kinder- und Jugendhilfe KW - KI KW - Soziale Arbeit Y1 - 0024 SN - 978-3-497-61953-5 PB - Ernst Reinhardt Verlag CY - München ER - TY - JOUR A1 - Albrecht, Jens A1 - Rudolph, Eric ED - Dexheimer, Andreas ED - Rothballer, Marc T1 - Künstliche Intelligenz und Machine Learning BT - Grundwissen für Sozialarbeiter/-innen JF - jugendhilfe KW - Maschinelles Lernen KW - Künstliche Intelligenz KW - Sozialarbeit KW - Soziale Arbeit KW - KI KW - Grundwissen Y1 - 2023 SN - 0022-5940 VL - 61 IS - 5 PB - Luchterhand CY - München ER - TY - CHAP A1 - Grandeit, Philipp A1 - Haberkern, Carolyn A1 - Lang, Maximiliane A1 - Albrecht, Jens A1 - Lehmann, Robert T1 - Using BERT for Qualitative Content Analysis in Psychosocial Online Counseling T2 - Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science N2 - Qualitative content analysis is a systematic method commonly used in the social sciences to analyze textual data from interviews or online discussions. However, this method usually requires high expertise and manual effort because human coders need to read, interpret, and manually annotate text passages. This is especially true if the system of categories used for annotation is complex and semantically rich. Therefore, qualitative content analysis could benefit greatly from automated coding. In this work, we investigate the usage of machine learning-based text classification models for automatic coding in the area of psycho-social online counseling. We developed a system of over 50 categories to analyze counseling conversations, labeled over 10.000 text passages manually, and evaluated the performance of different machine learning-based classifiers against human coders. Y1 - 2020 U6 - https://doi.org/10.18653/v1/2020.nlpcss-1.2 SP - 11 EP - 23 PB - Association for Computational Linguistics CY - Stroudsburg, PA, USA ER - TY - CHAP A1 - Ranzenberger, Thomas A1 - Freier, Carolin A1 - Reinold, Luca A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Simon, Claudia A1 - Freisinger, Steffen A1 - Georges, Munir A1 - Bocklet, Tobias T1 - A Multidisciplinary Approach to AI-based self-motivated Learning and Teaching with Large Language Models T2 - Proceedings of DELFI 2024 N2 - We present a learning experience platform that uses machine learning methods to support students and lecturers in self-motivated online learning and teaching processes. The platform is being developed as an agile open-source collaborative project supported by multiple universities and partners. The development is guided didactically, reviewed, and scientifically evaluated in several cycles. Transparency, data protection and the copyright compliant use of the system is a central part of the project. The system further employs large language models (LLMs). Due to privacy concerns, we utilize locally hosted LLM instances and explicitly do not rely on available cloud products. Students and lecturers can interact with an LLM-based chatbot in the current prototype. The AI-generated outputs contain cross-references to the current educational video’s context, indicating if sections are based on the lectures context or world knowledge. We present the prototype and results of our qualitative evaluation from the perspective of lecturers and students. KW - Artificial Intelligence in Education; Learning Experience Platform; Open Source Software; Large Language Models Y1 - 2024 U6 - https://doi.org/10.18420/delfi2024_11 SN - 2944-7682 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - JOUR A1 - Lippert, Carolyn A1 - Rudolph, Eric A1 - Poltermann, Aleksandra A1 - Engert, Natalie A1 - Lehmann, Robert A1 - Albrecht, Jens T1 - Generative KI in der beraterischen Ausbildung BT - Der Einsatz eines*r virtuellen Klient*in als Übungstool für angehende Onlineberater*innen JF - e-beratungsjournal.net – Zeitschrift für Online-Beratung und computervermittelte Kommunikation N2 - Der Beitrag beleuchtet das Projekt "Virtuelle*r Klient*in (ViKl)" am Institut für E-Beratung der Technischen Hochschule Nürnberg Georg Simon Ohm, das sich mit der Nutzung eines KI-basierten Chatbots für die Simulation von Klient*innen in der Lehrveranstaltung "Querschnitt Onlineberatung" im Bachelor-Studiengang Soziale Arbeit befasst. Ziel ist es, praxisnahe Erfahrungen zu vermitteln und die Fähigkeiten der Studierenden im digitalen Beratungsgespräch zu verbessern. Durch den Einsatz verschiedener maschineller Lernarchitekturen werden passende Antworten generiert, die zum Gesprächsverlauf und zur psychosozialen Problemstellung passen. Die Nutzung einer umfangreichen Datenbasis aus dem Zertifikatskurs "Onlineberatung" sowie ein zielgruppenadäquates didaktisches Konzept tragen zur Qualität des*der ViKl bei. Die Diskussion umfasst auch zukünftige Entwicklungen in der Onlineberatung, die didaktische Gestaltung von KI-basierten Lehr-Lern-Formaten, sowie Herausforderungen und Vorteile dieser Ansätze. KW - Generative KI KW - Hochschule KW - Lehre KW - Computerunterstützte Kommunikation KW - Psychosoziale Beratung KW - Onlinekommunikation KW - digitale Kommunikation KW - Generative KI KW - Onlineberatung KW - Chatberatung Y1 - 2024 U6 - https://doi.org/10.48341/tcgc-st69 SN - 1816-7632 VL - 20 IS - 1 ER - TY - CHAP A1 - Steigerwald, Philipp A1 - Albrecht, Jens T1 - Enhancing Psychosocial Counselling with AI: A Multifaceted Support System for Professionals T2 - Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications N2 - The expanding field of psychosocial online email counselling has created a demand for advanced tools to support counsellors in managing diverse client interactions. This paper introduces CAIA (Counsellor Artificial Intelligence Assistant), a comprehensive AI-driven system currently under active development, designed to condense and present information to counsellors in a structured manner. CAIA integrates several AI-based features, including Automatic Subject Generation, Case Summarisation, Timeline Management, Social Network Graph creation, and Case Classification. The features are designed to assist the counsellors, allowing them to engage more deeply with each case while ensuring that the client's needs remain paramount. Each feature presented in the paper addresses a specific problem in psychosocial online counselling, proposes a solution, discusses a technical implementation approach, and highlights the challenges encountered. Additionally, the paper discusses ethical considerations associated with an AI integration. Y1 - 2025 SN - 9789819679447 U6 - https://doi.org/10.1007/978-981-96-7945-4_35 SN - 2731-8125 SP - 597 EP - 611 PB - Springer Nature CY - Singapore ER - TY - CHAP A1 - Steigerwald, Philipp A1 - Albrecht, Jens T1 - Comparing Large Language Models for Automated Subject Line Generation in e-Mental Health: A Performance Study T2 - Proceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health N2 - Large Language Models (LLMs) have the potential to enhance e-mental health and psychosocial e-mail counselling by automating tasks such as generating concise and relevant subject lines for client communications. However, concerns regarding accuracy, reliability, data privacy and resource efficiency persist. This study investigates the performance of several LLMs in generating subject lines for e-mail threads, yielding a total of 253 generated subjects. Each subject line was assessed by six raters, including five counselling professionals and one AI system, using a three-category quality scale (Good, Fair, Poor). The results show that LLMs can generally produce concise subject lines considered helpful by experts. While GPT-4o and GPT-3.5 Turbo outperformed other models, their use is restricted in mental health settings due to data protection concerns, making the evaluation of open-source models crucial. Among open-source models, SauerkrautLM LLama 3 70b (4-bit) and SauerkrautLM Mixtral 8x7b (both 8-bit and 4-bit versions) delivered promising results with potential for further development. In contrast, models with lower parameter counts produced predominantly poor outputs. Y1 - 2025 U6 - https://doi.org/10.5220/0013294100003938 VL - 2025 SP - 70 EP - 77 PB - SCITEPRESS - Science and Technology Publications ER - TY - CHAP A1 - Steigerwald, Philipp A1 - Bienlein, Nico A1 - Burghardt, Jennifer A1 - Stieler, Mara A1 - Lehmann, Robert A1 - Albrecht, Jens T1 - CAIA in Practice: Field Evaluation of an AI-Assisted Support System for Text-Based Online Counselling T2 - 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI) N2 - Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a crucial low-threshold channel for accessing care. This paper presents CAIA, a co-designed AIbased tool suite that demonstrates responsible AI integration into counselling practice through seven LLM-driven functions enhanced by retrieval-augmented generation. A field evaluation involved 34 professional counsellors conducting authentic sessions with trained student counsellees (36 threads, 321 messages, 1,257 AI outputs). User behaviour analysis confirms substantial adoption, revealing that professional autonomy and information accuracy are decisive for sustained acceptance, with counsellors particularly valuing interpretive functionalities that provide new perspectives and stimulate professional reflection. KW - AI-Assisted Counselling KW - Email Counselling KW - Large Language Models KW - Retrieval-Augmented Generation KW - User KW - Adoption Study KW - Human-AI Collaboration Y1 - 2025 U6 - https://doi.org/10.1109/ICTAI66417.2025.00214 SP - 1476 EP - 1483 PB - IEEE ER -