Utilizing AI in the workplace (5cr)

Evaluation scale

H-5

Content scheduling

Course Content - Fundamentals of artificial intelligence (AI) and machine learning - Key concepts and principles of AI - The role of machine learning and data in AI development - Applications of AI across different sectors - Utilization of AI in social and health care environments - Challenges and prerequisites for AI implementation in social and health care organizations - Responsible and sustainable AI, including ethical considerations - Social impacts of AI on individuals and society - Ecological impacts of AI and the perspective of sustainable development - Principles and practical application of responsible AI - AI in the future of working life Learning Outcomes: - After completing the course, the student is able to: - Describe the basic concepts, principles, and application areas of artificial intelligence and machine learning across different sectors. - Identify the possibilities and limitations of AI in working life, particularly in social and health care contexts. - Analyze the effects of AI utilization on work roles, service processes, and decision-making support. - Evaluate the ethical, social, and ecological dimensions of AI applications and apply the principles of responsible AI to examples related to their own field. - Examine the prerequisites and development opportunities for AI implementation from organizational and societal perspectives. - Discuss the role of AI in the future of working life and reflect on how the development of AI may transform their own professional field. The course is implemented as online studies, conducted remotely via Zoom. Teaching sessions of the course: 22.1.2026 klo 10.15-16.00 11.3.2026 klo 12.30-16.00 17.4. 2026 klo12.30-16.00 The teaching sessions of the course are mandatory. Any absence must be compensated by completing a separate assignment, which will be provided in itsLearning after the missed session. According to the degree regulations, attendance at the first teaching session is compulsory. If a student is absent from the first in-person session, they will be removed from the course and must complete it in a future implementation.

Objective

After completing the course, the student: - Can explain what Artificial Intelligence (AI), Machine Learning, Generative AI, Large Language Models, Natural Language Processing and other related concepts are, and how they are interconnected. - Can use generative AI. - Can apply AI to solve practical problems within their own field. - Can use AI responsibly and ethically, taking into account sustainability and ecological considerations.

Content

Key concepts and operating principles related to AI. Large Language Models and Generative AI. Ethical and social challenges of AI. Ecological impacts of AI and sustainable development. Use cases of AI across different industries. Utilization and implementation of AI. The future of AI.

Teaching methods

Instructor-led key lectures: The instructor-led key lectures provide students with an overall understanding of the main themes of the course and guide deeper learning. The lectures cover the fundamentals of artificial intelligence and machine learning, the use of AI in social and health care, and the principles of responsible and sustainable AI. Independent study: The student explores the topics through learning materials, literature, videos, and articles. Discussions and peer learning: Students participate in group discussions and share their perspectives and expertise. Reflection and application: The student reflects on the impact of AI on their own work and professional field and evaluates the ethical and societal dimensions of AI during course sessions and assignments.

International connections

The pedagogical implementation of the course is based on constructive, active, and inquiry-based learning, where students build their own understanding of how artificial intelligence can be utilized in working life from various perspectives. The teaching methods support self-directed learning, critical thinking, and reflective learning. The course applies an experiential learning model, in which learning occurs through doing, experimentation, and reflection. Learning is guided through instructor-led key lectures, learning assignments, and peer interaction. Students apply their acquired knowledge to practical examples and situations related to their own professional field.

Completion alternatives

The course can be accredited in two ways: 1) by recognizing a previously completed equivalent course, or 2) through a demonstration of competence. The demonstration of competence must be arranged with the instructors before the first teaching session of the course. The instructors will provide detailed guidance on the methods and requirements for the demonstration.

Student workload

The course consists of three modules: Fundamentals of Artificial Intelligence and Machine Learning, Utilizing AI in Social and Health Care Environments, and Responsible AI – Ecological and Social Perspectives. The course includes discussion, group, and individual assignments, as well as a learning test. Workload and task allocation for a 5-credit course (total 135 hours of student work): Teaching sessions: 12 h Discussion assignment: 8 h Group assignments: 30 h Independent work and individual assignment: 45 h Learning test: 40 h (including familiarization with materials and completing the test)