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
Ce cours initie les étudiants à l’intelligence artificielle à travers le prisme du bâtisseur. Il aborde de manière accessible ce qu’est fondamentalement l’IA, ainsi que la façon de concevoir, prototyper et évaluer des flux de travail alimentés par l’IA pour résoudre des problèmes concrets liés aux études, au travail ou à la vie quotidienne.
Les étudiants passeront rapidement des fondamentaux de l’IA (comment fonctionnent les modèles modernes et où ils échouent) à des pratiques appliquées : prompting, évaluation, automatisation de workflows, création de contenu, personnalisation légère et déploiement responsable de l’IA.
Bloc de compétences
- 1. S’approprier les usages avancés et spécialisés des outils de l’intelligence digitale en s’assurant de leur impact durable et responsable
Compétences du bloc
- 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.
Objectifs d'apprentissage du cours
By the end of the course, students will have a portfolio of creative contents (e.g., videos) and productivity application (e.g., automated workflow for work assistant) they can use to present to future collaborators and employers.
Contenu : structure du module et agenda
Module 1: Evolution of AI Methods & Use of AI
Module 2: Harness Engineering & Psychology of AI
Module 3: Content Creation with AI
Module 4: Developing Apps with AI
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
Apprentissage
Mixte
Méthode pédagogique
Case-based learning; Problem-based learning; Group work & collaborative projects; Peer feedback and peer review; Portfolio-style assessment
Système de notation et modalités de rattrapage
L’évaluation repose sur des projets de groupe et individuels portant sur la mise en œuvre pratique et la démonstration d’applications d’IA terminales, avec les composantes suivantes : création de contenu et créativité (minimum 25 %), programmation et applications numériques (minimum 40 %, maximum 60 %), et productions textuelles basées sur la pensée critique (minimum 20 %).
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, creative production, and structured reasoning are recommended.