| Dozent/in |
Nadine Bienefeld |
| Veranstaltungsart |
Vorlesung |
| Code |
FS261170 |
| Semester |
Frühjahrssemester 2026 |
| Durchführender Fachbereich |
Gesundheitswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Fr, 27.02.2026, 08:15 - 16:00 Uhr, 3.B58
Sa, 28.02.2026, 08:15 - 16:00 Uhr, 3.B58
Fr, 22.05.2026, 08:15 - 16:00 Uhr, 3.B58 |
| Umfang |
2 Semesterwochenstunden |
| Inhalt |
Many promising AI initiatives in healthcare fail not due to technical flaws, but because they overlook the human element. This intensive 3-day course addresses this critical gap, guiding participants through human-centered approaches and the psychological aspects of risk management for safe and effective AI adoption.
By examining theoretical concepts, key frameworks, and real-world case studies, students will learn to evaluate and implement AI solutions by integrating human factors, ethical principles, and socio-technical systems design. The course is designed to equip participants to enable the responsible and safe integration of AI into complex healthcare environments. |
| Lernziele |
Upon successful completion of this course, students will be able to:
• Evaluate the risks and benefits of AI health technologies using human factors, ethical, and socio-technical frameworks.
• Design targeted mitigation strategies to address common challenges and risks in health AI implementation and use.
• Apply established implementation science frameworks to plan the effective deployment and scaling of AI solutions in healthcare settings. |
| Voraussetzungen |
Participants should have a basic understanding of AI (machine learning and LLMs) applications in health. No coding or extensive prompting skills are required. |
| Sprache |
Englisch |
| Anmeldung |
Moodle: https://elearning.hsm-unilu.ch/course/view.php?id=1006
|
| Leistungsnachweis |
Successful completion of the course is based on the following:
• Group Project (Teams of 4): Analyze a real or hypothetical AI implementation challenge. Your team will develop and present a socio-technical integration plan and a human-factors risk mitigation strategy, applying frameworks from the course.
• Individual Reflection Paper (2-3 pages): A brief written reflection connecting key course concepts to your professional experience or personal insights.
• Class Participation: Meaningful and constructive engagement in class discussions, case analyses, and peer feedback sessions.
IMPORTANT: In order to earn credits and participate at the exam registration via Uni Portal within the exam registration period is MANDATORY. Further information: www.unilu.ch/en/study/courses-exams-regulations/health-sciences-and-medicine/exams/ |
| Abschlussform / Credits |
group project, paper, participation / 3 Credits
|
| Hinweise |
Teaching methods:
This course uses a blended approach that emphasizes hands-on application:
• Interactive Lectures on core theories and frameworks.
• In-depth Case Study Analyses of real-world AI implementations.
• Collaborative Group Projects that apply course concepts to practical challenges.
• Moderated Debates and Peer Feedback Sessions to foster critical thinking.
The course encourages students to connect learnings to their own (future) professional contexts for real-world impact.
This is an intensive and interactive block course. Full attendance and active participation across all three days are expected. The ideal participant is enthusiastic about bridging scientific theory with practical application and is eager to collaborate effectively with peers in a project-based learning environment. |
| Hörer-/innen |
Nein |
| Material |
All course material will be accessible via the course’s online learning platform Moodle |
| Literatur |
All course materials, including required readings (journal articles, book chapters) and case studies, will be available on the course's Moodle platform. Pre-reading is required for each session to facilitate informed discussion. |