Course in Academic Regulation of Responsible AI
300-hour online course in academic regulation of responsible AI. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2016/679 (GDPR) and Python (scikit-learn). Assigned tutor and enrolment open.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Explain the principles of responsible artificial intelligence.
Analyse the uses of AI in teaching, assessment and university management.
Apply ethical criteria to AI research projects.
Interpret the obligations of the AI Act and the GDPR in the academic field.
Evaluate bias, transparency and explainability of an AI system.
Design auditing and monitoring procedures for AI systems.
Develop an AI governance framework for an academic institution.
Who it's for
- AI governance manager wishing to specialise in Academic Regulation of Responsible AI.
- AI systems auditor wishing to specialise in Academic Regulation of Responsible AI.
- Research ethics adviser wishing to specialise in Academic Regulation of Responsible AI.
- Data protection officer wishing to specialise in Academic Regulation of Responsible AI.
- Educational technologist wishing to specialise in Academic Regulation of Responsible AI.
- Researcher in responsible AI wishing to specialise in Academic Regulation of Responsible AI.
- Engineering or science graduates seeking to enter universities and research centres.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR)
- ISO/IEC 42001 (AI management systems)
- UNESCO Recommendation on the Ethics of AI (2021)
- ISO/IEC 23894 (AI risk management)
- European Code of Conduct for Research Integrity (ALLEA)
- Organic Law 3/2018 (LOPDGDD)
6 modules · 300 h
1.1EU HLEG Ethics Guidelines for Trustworthy AI1.2OECD Principles on AI1.3UNESCO Recommendation on the Ethics of AI1.4Fairness, transparency and accountability1.5Human autonomy and oversight1.6Privacy and data governance1.7Technical robustness and safety1.8Sustainability and social impact1.9From principles to practice1.10Workshop: analysis of a case of algorithmic harm
2.1Generative AI in teaching and assessment2.2Academic integrity and plagiarism2.3Detectors of AI-generated text and their limits2.4Institutional policies on use2.5Learning analytics and privacy2.6Admissions systems and bias2.7AI in scientific research2.8Authorship and use of AI in publications2.9Publisher guidelines and COPE2.10Case study: drafting a university AI policy
3.1Research ethics committees3.2Informed consent and personal data3.3GDPR and scientific research3.4Synthetic data and anonymisation3.5Reproducibility and open science3.6Model cards and datasheets for datasets3.7Dual-use research3.8Responsible publication of models3.9Conflicts of interest with industry3.10Practice: preparing a model card
4.1EU AI Act: structure and timeline4.2Prohibited practices under Article 54.3Education as a high-risk area4.4AI literacy obligations4.5Exemption for scientific research4.6General-purpose models4.7Council of Europe Framework Convention on AI4.8Spanish regulation and AESIA4.9ISO/IEC 42001 in institutions4.10Case study: classifying an automated assessment system
5.1Algorithmic impact assessment5.2Fairness metrics and their conflicts5.3External and internal audits5.4Explainability with SHAP and LIME5.5Participation of students and teaching staff5.6AI incident management5.7Register of institutional AI systems5.8Staff training5.9Continuous improvement5.10Workshop: fairness audit of an admissions model
6.1Institutional diagnosis6.2Inventory of AI uses6.3Principles and values adopted6.4Governance structure6.5Assessment procedures6.6Training plan6.7Monitoring indicators6.8Communication plan6.9Report and presentation6.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the course with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), Fairlearn, SHAP, Jupyter, in free, academic or evaluation versions. The case studies work through real procedures for auditing a model's bias, ethical impact assessment in research and designing AI use policies in teaching. Assessment is continuous across each module, plus a final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Explain the principles of responsible artificial intelligence.
Analyse the uses of AI in teaching, assessment and university management.
Apply ethical criteria to AI research projects.
Interpret the obligations of the AI Act and the GDPR in the academic field.
Evaluate bias, transparency and explainability of an AI system.
Where this takes you
Requirements
- No prior degree is required.
- An internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours, spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
The course uses Python (scikit-learn), Fairlearn, SHAP, Jupyter. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
SEIUM's own diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the higher-level programme. Check the credit transfer policy when you enrol.
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