AI Project Manager

Wyższa Szkoła Zarządzania i Bankowości - Akademia Nauk Stosowanych w Krakowie działa nieprzerwanie od 1995 roku, łącząc akademicką wiedzę z praktyką. Uczelnia kształci na kierunkach: Informatyka, Informatyka stosowana, Zarządzanie, Finanse i rachunkowość, Logistyka, Marketing cyfrowy, Przedsiębiorczość cyfrowa oraz Komunikacja i psychologia w biznesie. Programy studiów są projektowane z myślą o potrzebach współczesnej gospodarki i współpracy z otoczeniem biznesowym. W ofercie znajdują się także studia podyplomowe oraz kursy rozwijające kompetencje zawodowe. Uczelnia stawia na rozwój praktycznych umiejętności, przedsiębiorczości, innowacyjności oraz kompetencji cenionych na rynku pracy.

Learning Outcomes

The micro-credential confirms that the learner possesses an in-depth knowledge and practical skills in managing projects that utilize artificial intelligence, data analysis, and modern digital technologies.

The participant in the "AI Project Manager" postgraduate program understands the fundamentals of artificial intelligence, machine learning, data analysis, process automation, and the potential applications of AI in business and project environments. The participants comprehend the methods, tools, and conditions for managing AI projects, Data Science, and digital transformation, including the principles of agile management, project portfolio planning, implementation effectiveness assessment, and project risk management. They are also familiar with the organizational, technological, social, and ethical considerations of implementing AI solutions, including the importance of cybersecurity, data quality, organizational culture, digital leadership, and the responsible use of artificial intelligence within an organization.

In terms of skills, the participant is capable of analyzing organizational needs, data, and business processes, and utilizing analytical tools, scripting languages, machine learning models, and generative artificial intelligence to support business decisions and design AI solutions. He/she can plan, coordinate, and monitor AI and Data Science projects, select project management methodologies, define objectives, scope, resources, risks, schedules, and success metrics, as well as manage AI project portfolio in the organization. They are also able to collaborate with technical and business teams, communicate the assumptions, benefits, limitations, and risks of AI implementations, support the digital transformation process, and use AI tools to automate work, create AI agents, and streamline organizational processes.

In the area of social competencies, the participant is prepared to responsibly fulfill the role of a manager, coordinator, or leader of AI projects, considering the impact of implementing modern technologies on the organization, employees, customers, and the socio-economic environment. They demonstrate readiness for critical evaluation of applied solutions, ethical use of artificial intelligence, fostering a culture of innovation, openness to change, and independently enhancing their professional competencies.

Estimated Workload

30 ECTS credits

Level (referenced to the Polish Qualifications Framework / European Qualifications Framework – EQF)

PRK7/EQF7

Assessment Method(s)

The micro-credential was awarded based on the participant's achievement of the intended learning outcomes, as confirmed by the results obtained during the postgraduate program and the final examination.

The assessment particularly focused on the level of mastery in areas such as artificial intelligence, AI project management, data analysis, digital transformation, and cybersecurity. The assessment also included the development of practical skills related to planning and coordinating AI projects, data analysis, the use of AI tools, and collaboration with technical and business teams. Additionally, it took into account the participant's engagement during classes, participation in workshop exercises, case study analysis, and the ability to apply acquired competencies in organizational and project practice.

Mode of Delivery

The micro-credential confirms the completion of approximately 750 hours of work by the participant in the "AI Project Manager" postgraduate program. The workload structure includes various forms of educational activities that create a cohesive learning process with a practical-managerial, analytical, and technological focus, aimed at developing the competencies needed to plan, coordinate, and implement projects utilizing artificial intelligence.

During theoretical classes, participants acquire structured knowledge in the fields of artificial intelligence, machine learning, data analysis, AI project management, digital transformation, cybersecurity, digital leadership, and change management.

A crucial component of the program are the practical sessions conducted in the form of exercises, workshops, case studies, and project work. These aim to develop skills in data analysis, AI solution design, AI agent creation, the use of generative artificial intelligence, technology project management, and assessing the potential for AI implementation within an organization.

An integral part of the education process is the participant's independent work, which includes preparation for classes, analysis of educational materials, completion of practical tasks, working with digital tools, developing project solutions, and analyzing examples of AI implementations in business. The entire educational process is complemented by a final exam, which allows for the summary of the acquired knowledge, verification of skills, and assessment of the readiness to apply the learned solutions in professional practice.

Quality Assurance Arrangements Underpinning the Micro-credential

The issuance of the micro-credential is based on the participant's achievement of the intended learning outcomes in terms of knowledge, skills, and social competencies, as defined for the postgraduate program. These outcomes are linked to the program content, the adopted teaching methods, and the assessment approaches.

The verification of learning outcomes is continuous and multi-stage process. It includes evaluating the participant's activity during classes, the completion of practical tasks, participation in exercises and workshops, case study analysis, project work, and a final exam. This comprehensive assessment allows for a thorough evaluation of the level of knowledge mastery, the development of practical skills, and the readiness to apply them in organizational, technological, and project environments.

The quality of education is ensured by:

• clearly defined criteria and conditions for passing,
• systematic evaluation of participant progress,
• a practical approach to classes based on exercises, workshops, case analyses, and project work,
• the use of tools and methods appropriate for AI project management, data analysis, machine learning, generative artificial intelligence, cybersecurity, and digital transformation,
• adapting program content to current trends in artificial intelligence, data science, process automation, digital leadership, and change management,
• incorporating feedback from participants and stakeholders in the educational process.

This approach fosters a high level of education and adequately prepares participants to apply their acquired competencies in professional practice, particularly in areas such as AI project management, data analysis, process automation, digital transformation, and the implementation of modern technologies within organizations.