Code
MGSE MIS 4406
Niveau
M1
Discipline
Systèmes d’information
Langue
Anglais/English
Crédits ECTS
3
Heures programmées
20
Charge totale étudiant
60
Coordonnateur(s)
Département
- Technologies, Information et Management
Equipe pédagogique
Introduction au module
This course introduces students to artificial intelligence through a builder’s lens. It addresses what AI is fundamentally in relatable ways, and how to design, prototype, and evaluate AI-powered workflows that solve study/work/life problems. Students will move from AI fundamentals (how modern models “work” and where they fail) to applied practices: prompting, evaluation, workflow automation, retrieval (RAG), lightweight customization, and responsible deployment of AI.
The course is designed for business students and future managers: the emphasis is on decision quality, value creation, risk awareness, and practical prototyping rather than mathematics or engineering capacity. Students finish the course with a project-based portfolio.
Objectifs d'apprentissage (compétences mères)
- 1.1 - Auditer les usages avancés et spécialisés des outils de l'intelligence digitale, afin de les mobiliser avec pertinence, en tenant compte du contexte stratégique des organisations.
- 1.2 - Actionner les outils de l'intelligence digitale de manière efficiente, pour accompagner les transformations sociétale, numérique, énergétique et environnementale des organisations, en s'assurant de leur impact durable et responsable.
Contribution à l'atteinte des ODD (Objets du Développement Durable)
This course contributes primarily to ODD 4 (Quality Education), ODD 8 (Decent Work and Economic Growth), and ODD 12 (Responsible Consumption and Production) by developing practical AI literacy and the ability to design useful, responsible AI-powered workflows. Students learn how to prototype and evaluate AI solutions for real study and work problems, moving beyond hype to evidence-based adoption. The course emphasizes decision quality, value creation, and risk awareness (e.g., bias, privacy, over-automation), enabling future professionals to improve productivity while reducing wasteful or harmful deployments of AI in organizations.
Nombre d'ODD abordés parmi les 17
4, 8, 12
Système de notation et modalités de rattrapage
Evaluation is based on 3 - 5 group and individual projects on practical implementation and demo (80-100%). A written exam may be added depending on if students show sufficient theoretical understanding of key concepts towards the middle of the course (0-20%).
The catch up exam is mostly in the form of semi-open questions on AI application and ethics, as well as a practical breakdown of an AI agent application.
Règlement du module
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.
Références obligatoires et lectures suggérées
There is no required textbook. However, basic comfort with data (e.g., Excel sheets) and structured reasoning are recommended as a prerequisite.