Code
MXUE INF 9001
Field
Informatique
Language
Anglais/English
ECTS Credits
0,5
Class hours
25
Total student load
60
Program Manager(s)
Department
- Data analytics, Économie et Finances
Educational team
Introduction to the module
This class introduces students to the foundations of artificial intelligence as a central driver of digital transformation, with a specific focus on sustainability and responsible innovation. Students explore how AI tools and techniques reshape organizational processes and support sustainable innovation. The week encourages students to critically assess both the opportunities and limitations of AI systems, and to reflect on how AI-driven digital transformation can be designed and deployed to deliver measurable social, environmental, and organizational impact, laying the groundwork for applied evaluation and project work in the next class.
Learning objectives
- 1.1 - Audit advanced and specialised uses of digital intelligence tools in order to deploy them appropriately, taking into account the strategic context of organisations.
- 1.2 - Use digital intelligence tools efficiently to support the societal, digital, energy and environmental transformations of organisations, ensuring their sustainable and responsible impact.
- 2.1 - Develop a critical awareness of highly specialised knowledge, some of which is at the forefront of knowledge, with a view to formulating innovative contributions to complex issues, in line with the strategic plan of organisations and with scientific
- 2.3 - Conduct a reflective and detached analysis that takes into account the challenges, issues and complexity of a request or situation in order to propose appropriate and/or innovative solutions in line with regulatory developments.
- 3.2 - Communicate effectively and appropriately for the purposes of training, knowledge transfer, skills development or innovation, in English and at least one other language, in a global and multicultural context.
- 4.2 - Lead a complex project with responsibility, with the aim of supporting the transformation of organisations (design, management, team coordination, implementation and management, control, dissemination), by mobilising multidisciplinary skills and bri
- 4.4 - Raise awareness and promote the application of ethical principles, professional conduct and environmental responsibility, in a context of organisational and societal transformation, and for the common good.
Content : structure and schedule
5 days of class corresponding to the first week of summer school.
Day 1: Morning Lecture AI Tools and Techniques. Afternoon Workshop AI Tools and Techniques
Day 2: Morning Lecture AI-Driven Digital Transformation. Afternoon Student project session - coding lab(python/excel)
Day 3: Morning Lecture AI Foundations & Prototyping. Afternoon Company visit
Day 4: Morning Lecture Generative & Predictive AI for Sustainable Innovation. Afternoon Student project session: Presentations
Day 5: Morning Lecture AI ethics & regulation. Afternoon Learning expedition in Paris
Sustainable Development Goals
ODD 9 — Industry, Innovation and Infrastructure: The week is fundamentally about AI-driven digital transformation and responsible innovation in organisations — this is the most direct fit.
ODD 10 — Reduced Inequalities: The ethics session explicitly addresses how AI can concentrate power, embed bias and widen inequality, and what can be done about it.
ODD 13 — Climate Action: Students examine AI applications for sustainability and the environmental cost of AI systems throughout the week.
Number of SDG's addressed among the 17
3
Learning delivery
synchrone
Pedagogical methods
Interactive lectures with business case illustrations, Hands-on labs (Python/Colab and Excel), Company visit, Group project (mini-consulting brief), AI tools used as learning aids throughout, Group presentations
Evaluation and grading system and catch up exams
Class activities (40%)
Applied exercises / short tests (20%)
Student Presentations (40%)
Catch up exam: individual written assignment + online oral evaluation
Module Policies
Professor-Student Communication
● The professor will contact the students through their school email address (IMT-BS/TSP) and the Moodle portal. No communication via personal email addresses will take place. It is the student responsibility to regularly check their IMT-BS/TSP mailbox.
● Students can communicate with the professor by emailing him/her to his institutional address. If necessary, it is possible to meet the professor in his office during office-hours or by appointment.
Students with accommodation needs
If a student has a disability that will prevent from completing the described work or require any kind of accommodation, he may inform the program director (with supporting documents) as soon as possible. Also, students are encouraged to discuss it with the professor.
Class behavior
● Out of courtesy for the professor and classmates, all mobile phones, electronic games or other devices that generate sound should be turned off during class.
● Students should avoid disruptive and disrespectful behavior such as: arriving late, leaving early, careless behavior (e.g. sleeping, reading a non-course material, using vulgar language, over-speaking, eating, drinking, etc.). A warning may be given on the first infraction of these rules. Repeated violators will be penalized and may face expulsion from the class and/or other disciplinary proceedings.
● The tolerated delay is 5 minutes. Attendance will be declared on Moodle during these 5 minutes via a QR code provided by the teacher at each course start.
● Student should arrive on time for exams and other assessments. No one will be allowed to enter the classroom once the first person has finished the exam and left the room. There is absolutely no exception to this rule. No student can continue to take an exam once the time is up. No student may leave the room during an examination unless he / she has finished and handed over all the documents.
● In the case of remote learning, the student must keep his camera on unless instructed otherwise by the professor.
Honor code
IMT-BS is committed to a policy of honesty in the academic community. Conduct that compromises this policy may result in academic and / or disciplinary sanctions. Students must refrain from cheating, lying, plagiarizing and stealing. This includes completing your own original work and giving credit to any other person whose ideas and printed materials (including those from the Internet) are paraphrased or quoted directly. Any student who violates or helps another student violate academic behavior standards will be penalized according to IMT-BS rules.
Textbook Required and Suggested Readings
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press
Ng, A. (2018). AI Transformation Playbook. Landing AI. Available at: landing.ai
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Chapters 1-2 (overview only). Pearson.
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, July 2017.
Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. In Machine Learning and the City. Wiley.
Acemoglu, D. (2025). The simple macroeconomics of AI. Economic Policy, 40(121), 13-58.
Google (2023). Introduction to Generative AI. Available at: cloud.google.com/training
DeepLearning.AI (2023). AI for Everyone. Available at: deeplearning.ai
European Commission (2024). EU AI Act Overview.
NIST (2023). AI Risk Management Framework 1.0.
UNESCO (2021). Recommendation on the Ethics of Artificial Intelligence.
Keywords
AI, Digital Transformation
Prerequisites
2 years of bachelor