Caricamento in corso...
Impact of AI on Occupations (Grandi Sfide - Digitale)
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A.A. 2025/26
Course Language
Inglese
Degree programme(s)
1st degree and Bachelor-level of the Bologna process in Ingegneria Informatica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Meccanica (Mechanical Engineering) - Torino
1st degree and Bachelor-level of the Bologna process in Design E Comunicazione - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dell'Autoveicolo (Automotive Engineering) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Informatica (Computer Engineering) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dell'Autoveicolo - Torino
1st degree and Bachelor-level of the Bologna process in Electronic And Communications Engineering (Ingegneria Elettronica E Delle Comunicazioni) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dei Materiali - Torino
1st degree and Bachelor-level of the Bologna process in Architettura (Architecture) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Elettrica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Aerospaziale - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Biomedica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Chimica E Alimentare - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Civile - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Edile - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Energetica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Meccanica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Per L'Ambiente E Il Territorio - Torino
1st degree and Bachelor-level of the Bologna process in Matematica Per L'Ingegneria - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Elettronica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Fisica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Del Cinema E Dei Mezzi Di Comunicazione - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Gestionale - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Gestionale - Torino
1st degree and Bachelor-level of the Bologna process in Architettura - Torino
1st degree and Bachelor-level of the Bologna process in Pianificazione Territoriale, Urbanistica E Paesaggistico-Ambientale - Torino
1st degree and Bachelor-level of the Bologna process in Civil And Environmental Engineering - Torino
Course structure
| Teaching | Hours |
|---|
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|
Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/05 M-FIL/03 |
3 3 |
D - A scelta dello studente D - A scelta dello studente |
A scelta dello studente A scelta dello studente |
|---|
2025/26
Artificial intelligence (AI) is changing the way we work. This course looks at how new technologies are reshaping jobs: changing tasks, creating new skills, and raising the risk of exclusion, job loss, or lower-quality work.
Students will explore what their future profession might look like. They will start by thinking about the tasks involved in their chosen job and how AI could change them.
With the help of tools that focus on long-term thinking and human values, students will choose the values that matter most in their field. They will design practical and policy-based ideas to make sure that changes at work are fair, inclusive, and good for society.
The course ends with a group project and a final presentation. In both, students will show how their profession could change with AI, explaining their choices from ethical, social, and technical points of view.
Artificial intelligence (AI) is changing the way we work. This course looks at how new technologies are reshaping jobs: changing tasks, creating new skills, and raising the risk of exclusion, job loss, or lower-quality work.
Students will explore what their future profession might look like. They will start by thinking about the tasks involved in their chosen job and how AI could change them.
With the help of tools that focus on long-term thinking and human values, students will choose the values that matter most in their field. They will design practical and policy-based ideas to make sure that changes at work are fair, inclusive, and good for society.
The course ends with a group project and a final presentation. In both, students will show how their profession could change with AI, explaining their choices from ethical, social, and technical points of view.
By the end of the course, students will be able to:
1. Study how AI affects specific jobs and tasks.
2. Use design tools to spot key people and values affected by digital changes at work.
3. Tell apart tasks that can be automated, jobs with little meaning (as described by David Graeber), and those that rely on human skills.
4. Plan career paths that focus on ethics, sustainability, and public benefit.
5. Imagine future work scenarios using creative and long-term thinking methods.
6. Create design and policy ideas that rethink job roles through the careful use of AI tools.
By the end of the course, students will be able to:
1. Study how AI affects specific jobs and tasks.
2. Use design tools to spot key people and values affected by digital changes at work.
3. Tell apart tasks that can be automated, jobs with little meaning (as described by David Graeber), and those that rely on human skills.
4. Plan career paths that focus on ethics, sustainability, and public benefit.
5. Imagine future work scenarios using creative and long-term thinking methods.
6. Create design and policy ideas that rethink job roles through the careful use of AI tools.
None
None
1. Clear thinking and writing;
2. How to write and present a report;
3. How AI is changing work;
4. Breaking down a job into tasks;
5. What work we value and why;
6. Tools for mapping values and roles in tech-driven change;
7. Jobs with little meaning and what makes work matter;
8. Planning for better futures and how to get there;
9. Debating values and choices in a final group forum.
1. Clear thinking and writing;
2. How to write and present a report;
3. How AI is changing work;
4. Breaking down a job into tasks;
5. What work we value and why;
6. Tools for mapping values and roles in tech-driven change;
7. Jobs with little meaning and what makes work matter;
8. Planning for better futures and how to get there;
9. Debating values and choices in a final group forum.
Class Projects
Each student picks a job they hope to do after graduation. Before starting their analysis, they do a scan of how AI is being used in that field. This includes talking to experts (such as teachers or former students) and building a list of AI examples, ranked by how much they might change the field.
Working alone and in teams, students will:
1. Scan the field: Find and rank key uses of AI in their future job.
2. Map the tasks: Break down the job into its daily tasks.
3. Write Story/Vignette 1: Describe a typical day in that job today.
4. Analyse AI’s impact: Sort the tasks into those AI could do, share, or not do at all.
5. Write Story/Vignette 2: Imagine the same job after AI has changed it.
6. Reflect and compare: What changed between the two stories? Which tasks mattered? Which values are at risk? Then:
a. Map who is affected and what values are in play.
b. Choose a future worth working toward and plan how to get there.
c. The reflection leads to a final project that includes:
i. Two reports (one on the field, one on the job), and
ii. A video on how AI may change the field.
These form the students’ vision for fair and meaningful jobs shaped by AI.
7. Join the Design Court: In a structured debate, students play judge, defence, or prosecution. They argue which parts of the job should or shouldn’t be automated. The court ends with a final decision and a group review.
Assessment
Assessment is based on:
1. Your personal work on the stories, task maps, and written reflection.
2. How actively you join in the group debate and team work;
3. The clarity and consistency of your final project (video and written report);
4. How well you identify key values, people affected, risks, and chances for change.
Class Projects
Each student picks a job they hope to do after graduation. Before starting their analysis, they do a scan of how AI is being used in that field. This includes talking to experts (such as teachers or former students) and building a list of AI examples, ranked by how much they might change the field.
Working alone and in teams, students will:
1. Scan the field: Find and rank key uses of AI in their future job.
2. Map the tasks: Break down the job into its daily tasks.
3. Write Story/Vignette 1: Describe a typical day in that job today.
4. Analyse AI’s impact: Sort the tasks into those AI could do, share, or not do at all.
5. Write Story/Vignette 2: Imagine the same job after AI has changed it.
6. Reflect and compare: What changed between the two stories? Which tasks mattered? Which values are at risk? Then:
a. Map who is affected and what values are in play.
b. Choose a future worth working toward and plan how to get there.
c. The reflection leads to a final project that includes:
i. Two reports (one on the field, one on the job), and
ii. A video on how AI may change the field.
These form the students’ vision for fair and meaningful jobs shaped by AI.
7. Join the Design Court: In a structured debate, students play judge, defence, or prosecution. They argue which parts of the job should or shouldn’t be automated. The court ends with a final decision and a group review.
Assessment
Assessment is based on:
1. Your personal work on the stories, task maps, and written reflection.
2. How actively you join in the group debate and team work;
3. The clarity and consistency of your final project (video and written report);
4. How well you identify key values, people affected, risks, and chances for change.
The course includes 60 hours of teaching, divided as follows:
• 20% will be shared with the “Global Grand Challenges” program
• 30% will be lectures
• 50% will be group project work
Each instructor will teach their own set of lectures. For the project, students will work in teams to identify real-world problems, design public policy ideas, and suggest clear, practical solutions.
Both instructors will guide the project work and, when possible, will work together in class to support truly cross-disciplinary projects.
The course includes 60 hours of teaching, divided as follows:
• 20% will be shared with the “Global Grand Challenges” program
• 30% will be lectures
• 50% will be group project work
Each instructor will teach their own set of lectures. For the project, students will work in teams to identify real-world problems, design public policy ideas, and suggest clear, practical solutions.
Both instructors will guide the project work and, when possible, will work together in class to support truly cross-disciplinary projects.
1. Readings for the shared “Grand Challenges” section: Set by the university.
2. Readings for the Digital Challenge section: The instructor will provide these for all related courses.
3. Readings for the “AI and Work” course: The instructors will share the main reading list at the start of the course. Suggested readings are listed below.
[Minto 1981] Barbara Minto. The Pyramid Principle: Logic in Writing and Thinking. Financial Times/ Prentice Hall. 1981.
[Primlani 2025] N. Primlani et al. Design Courts: Workshops for Exploring Emerging Technology Ethics. ACM CHI 2025.
[Septiandri 2024] Ali Akbar Septiandri et al. The Potential Impact of AI Innovations on U.S. Occupations. PNAS Nexus. 2024
[Septiandri 2025] Ali Akbar Septiandri et al. AI and the economic divide: How Artificial Intelligence could widen the divide in the U.S. EPJ Data Science. 2025.
[Umbrello 2021] S. Umbrello and O. Gambelin. A Value Sensitive Design Toolkit for Agile Project Management. 2021.
[Weissman 2021] Jerry Weissman. Presenting to Win. Pearson. 2021.
1. Readings for the shared “Grand Challenges” section: Set by the university.
2. Readings for the Digital Challenge section: The instructor will provide these for all related courses.
3. Readings for the “AI and Work” course: The instructors will share the main reading list at the start of the course. Suggested readings are listed below.
[Minto 1981] Barbara Minto. The Pyramid Principle: Logic in Writing and Thinking. Financial Times/ Prentice Hall. 1981.
[Primlani 2025] N. Primlani et al. Design Courts: Workshops for Exploring Emerging Technology Ethics. ACM CHI 2025.
[Septiandri 2024] Ali Akbar Septiandri et al. The Potential Impact of AI Innovations on U.S. Occupations. PNAS Nexus. 2024
[Septiandri 2025] Ali Akbar Septiandri et al. AI and the economic divide: How Artificial Intelligence could widen the divide in the U.S. EPJ Data Science. 2025.
[Umbrello 2021] S. Umbrello and O. Gambelin. A Value Sensitive Design Toolkit for Agile Project Management. 2021.
[Weissman 2021] Jerry Weissman. Presenting to Win. Pearson. 2021.
Slides;
Lecture slides;
Modalità di esame: Prova orale obbligatoria; Elaborato scritto individuale; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Individual essay; Group essay;
...
The final exam includes an oral test and both individual and group written work.
Final Grade Breakdown: The overall grade is a weighted average of the following components:
• 15% – Two short personal stories/vignettes (individual)
• 30% – Report on AI’s impact on the job sector (group)
• 15% – Examination of AI’s impact on a specific job (individual)
• 20% – Video with critical reflections on the sector (group)
• 20% – Design Court debate (group)
Passing the Course: To pass, students must earn at least 18 out of 30 on each component. The project grade depends on active participation in the group. Written work is graded on clarity, relevance, and how well the ideas are presented. A final score of 31 or higher (on a 30-point scale) qualifies for honours (cum laude). Students who do not take part in their group project cannot take the oral exam in the same academic year, as they will not have met the minimum grade requirement for the project.
Accessibility and Support: Students with disabilities or specific learning needs (SLDs) must complete the online registration process and also contact the course instructor directly. Please do this at least one week before the exam session, so that the agreed accommodations can be applied properly.
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Individual essay; Group essay;
The final exam includes an oral test and both individual and group written work.
Final Grade Breakdown: The overall grade is a weighted average of the following components:
• 15% – Two short personal stories/vignettes (individual)
• 30% – Report on AI’s impact on the job sector (group)
• 15% – Examination of AI’s impact on a specific job (individual)
• 20% – Video with critical reflections on the sector (group)
• 20% – Design Court debate (group)
Passing the Course: To pass, students must earn at least 18 out of 30 on each component. The project grade depends on active participation in the group. Written work is graded on clarity, relevance, and how well the ideas are presented. A final score of 31 or higher (on a 30-point scale) qualifies for honours (cum laude). Students who do not take part in their group project cannot take the oral exam in the same academic year, as they will not have met the minimum grade requirement for the project.
Accessibility and Support: Students with disabilities or specific learning needs (SLDs) must complete the online registration process and also contact the course instructor directly. Please do this at least one week before the exam session, so that the agreed accommodations can be applied properly.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.