MICROCREDENTIAL Generative AI in Academic and Professional Practice

Learning Outcomes:

1. The learner demonstrates advanced knowledge and understanding of the operating principles, functionalities, and technical limitations of generative artificial intelligence tools used for analytical and text-based tasks.

2. The learner knows and understands the legal, ethical, and procedural frameworks governing data protection (including GDPR compliance), copyright, and the risks associated with the misuse of artificial intelligence in academic and professional contexts.

3. The learner is able to design, test, and critically optimise a workflow using Generative AI tools for a selected academic or professional task.

4. The learner is able to effectively verify the accuracy and reliability of AI-generated outputs, identify errors and hallucinations, and transparently document the digital process through which such outputs are produced.

5. The learner is prepared to adopt a critical and reflective approach to algorithms and digital technologies, while maintaining personal responsibility for the final outcomes of their work.

6. The learner is prepared to integrate modern digital tools into interdisciplinary academic and professional activities in a responsible, transparent, and ethically sound manner, in accordance with the principles of academic and professional integrity.

Verification process:

1. Preparation and digital submission of an original final project together with the artefacts produced during its development. The project consists of the practical implementation and documentation of the optimisation of a selected academic or professional task using Generative AI (GenAI) tools.

Assessment Criteria:

• Technical correctness and sophistication of the implemented workflow, as well as demonstrated understanding of the capabilities and limitations of the AI tools employed.

• Reliability and comprehensiveness of the verification procedure applied to AI-generated outputs, including transparent documentation of the entire process.

• Accurate identification and management of legal, ethical, and data protection risks,including GDPR compliance and copyright considerations.

Passing requirement: Successful completion requires a positive evaluation in each of the three assessment criteria listed above.

2. Oral presentation delivered during the final course session. Participants present the objectives, workflow, and outcomes of their digital project, including the artefacts produced with the support of Generative AI (GenAI) tools.

Assessment Criteria:

• Clear justification of the prompt-design strategies and workflow adopted in the project, demonstrating the ability to design and optimise AI-assisted processes (U1).

• Critical reflection on the limitations and risks associated with the tools used, together with evidence of a comprehensive verification process applied to the generated outputs (U2).

• Demonstration of a responsible and autonomous approach to the use of AI technologies, including a well-reasoned explanation of decisions ensuring accountability for the final outcomes of the project (K1, K2).

Passing Requirement: Successful completion requires a positive evaluation of the participant’s substantive justification of the project and demonstrated evidence of a critical and responsible approach to the use of AI tools.

https://usosweb.us.edu.pl/kontroler.php?_action=katalog2/przedmioty/pokazPrzedmiot&prz_kod=US-OMU-S1-W1-GAIPA