Ai and Organizational Development
A.Y. 2025/2026
Learning objectives
This course has three main learning objectives. First, it offers a view of organizational development—and thus of planned interventions aimed at increasing organizational effectiveness and supporting organizational learning and change—and the topics linked to it. Second, it gives several examples of studies and tools that link AI to an organizational development measure or topic (e.g., mentoring, leadership development, feedback ). Third, it allows students to develop projects and test ideas that are put into practice and partially develop the topics discussed in class. This will result in more experiential learning and encourage students to reflect on the course's practical implications.
Knowledge and understanding
. Defining an organization
. Organizational culture and its impact on attitudes towards AI
. Defining, describing, and making a typology of organizational development (OD) measures
. Managing OD measures through the process consultation approach
. Exploring some OD measures and tools in greater depth (e.g., mentoring, career development and the use of feedback, leadership development, cultural change, etc.), focusing on those where there are studies or cases that adopt AI
. Some applications of AI to OD measures and tools and their results.
Applying knowledge and understanding
. Linking the material presented during the course coherently
. Preparing for initial meetings with corporate clients and approaching them as constructively as possible
. Interpreting the organizational setting and its dynamics in order to propose OD interventions and new tools
. Gaining the ability to think about and discuss AI tools as means of fostering OD
. Designing and proposing an AI tool for the OD of a hypothetical organization.
Knowledge and understanding
. Defining an organization
. Organizational culture and its impact on attitudes towards AI
. Defining, describing, and making a typology of organizational development (OD) measures
. Managing OD measures through the process consultation approach
. Exploring some OD measures and tools in greater depth (e.g., mentoring, career development and the use of feedback, leadership development, cultural change, etc.), focusing on those where there are studies or cases that adopt AI
. Some applications of AI to OD measures and tools and their results.
Applying knowledge and understanding
. Linking the material presented during the course coherently
. Preparing for initial meetings with corporate clients and approaching them as constructively as possible
. Interpreting the organizational setting and its dynamics in order to propose OD interventions and new tools
. Gaining the ability to think about and discuss AI tools as means of fostering OD
. Designing and proposing an AI tool for the OD of a hypothetical organization.
Expected learning outcomes
At the end of the class, the students are expected to:
. Be aware of the structure, functioning, and dynamics that characterize an organization, with a particular focus on its culture
. Be able to discuss the links between workers' lives in an organization and the attitude that they have and develop toward AI measures and tools
. Be able to describe and discuss organizational development (OD) measures and tools (individual, group, and organizational-level measures) with their strengths and weaknesses
. Have a clear idea of how OD measures can be introduced and managed in a real setting, specifically through the process consultation approach
. Be familiar with some of the most recent studies that describe AI tools and measures designed for OD (e.g., studies on organizational feedback, mentoring, etc.) and being able to assess the pros and cons of these solutions
. Be able to approach an organizational setting and interpret its dynamics to propose new OD interventions and tools
. Be able to think about and discuss AI tools as means of fostering OD, thus emphasizing not only the technical characteristics of these solutions but also embedding them in a real work setting
. Design and propose an AI tool (at the prototype level) for the OD of a hypothetical organization.
. Be aware of the structure, functioning, and dynamics that characterize an organization, with a particular focus on its culture
. Be able to discuss the links between workers' lives in an organization and the attitude that they have and develop toward AI measures and tools
. Be able to describe and discuss organizational development (OD) measures and tools (individual, group, and organizational-level measures) with their strengths and weaknesses
. Have a clear idea of how OD measures can be introduced and managed in a real setting, specifically through the process consultation approach
. Be familiar with some of the most recent studies that describe AI tools and measures designed for OD (e.g., studies on organizational feedback, mentoring, etc.) and being able to assess the pros and cons of these solutions
. Be able to approach an organizational setting and interpret its dynamics to propose new OD interventions and tools
. Be able to think about and discuss AI tools as means of fostering OD, thus emphasizing not only the technical characteristics of these solutions but also embedding them in a real work setting
. Design and propose an AI tool (at the prototype level) for the OD of a hypothetical organization.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Second semester
Course syllabus
· Defining an organization
· Organizational culture and its impact on attitudes towards AI
· Defining, describing, and making a typology of organizational development (OD) measures
· Analyzing OD research interventions based on action research and collaborative management research
· Managing OD measures through the process consultation approach
· Discussing a general overview of the opportunities AI affords for HR with a focus on the HR activities linked to OD
· Analyzing some OD measures and tools in greater depth (e.g., mentoring, career development, and the use of feedback, leadership development, cultural change, etc.), focusing on those where there are studies or cases that adopt AI
· Some applications of AI to OD measures and tools, and analysis of their outcomes.
· Organizational culture and its impact on attitudes towards AI
· Defining, describing, and making a typology of organizational development (OD) measures
· Analyzing OD research interventions based on action research and collaborative management research
· Managing OD measures through the process consultation approach
· Discussing a general overview of the opportunities AI affords for HR with a focus on the HR activities linked to OD
· Analyzing some OD measures and tools in greater depth (e.g., mentoring, career development, and the use of feedback, leadership development, cultural change, etc.), focusing on those where there are studies or cases that adopt AI
· Some applications of AI to OD measures and tools, and analysis of their outcomes.
Prerequisites for admission
There are no formal prerequisites for this course.
However, a basic understanding of work and organizational psychology, organizational behavior, and/or organization theory would be beneficial.
However, a basic understanding of work and organizational psychology, organizational behavior, and/or organization theory would be beneficial.
Teaching methods
Teaching will involve classroom lectures on theoretical and methodological topics linked to OD, in-class discussions, and individual and group work in the class on cases, film cases, and exercises assigned by the instructor to develop the students' capacity to apply what they have learned and to develop critical thinking about the topics covered in the course.
Teaching Resources
Coghlan, D. & Shani A.B. (2016) "Organizational-development research interventions: Perspectives from action research and collaborative management research" in Skipton Leonard et al., The Psychology of Leadership, Change, and Organizational Development, chapter 21, Wiley Blackwell.
Day, D. (2012), "The nature of leadership development" in Day & Antonakis, The Nature of Leadership, chapter 4, Sage.
Eubanks, B. (2022, second edition). Artificial intelligence for HR: Use AI to support and develop a successful workforce, chapters 6 ("Learning and Development") and 7 ("Talent management"), Kogan Page.
Hodges, J. (2017) "Designing and delivering interventions" in Hodges, Consultancy, Organizational Development and Change, chapter 5, Kogan Page.
Yao, M. & Koidan, K. (2024) "Human resources and talent" in Yao & Koidan, Applied Artificial Intelligence: A Handbook for Business Leaders, chapter 18, TOPBOTS.
The following peer-reviewed articles are part of the program (and others will be announced at the beginning of the course):
- Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159-182.
- Quaquebeke, N. V., & Gerpott, F. H. (2023). The now, new, and next of digital leadership: How Artificial Intelligence (AI) will take over and change leadership as we know it. Journal of Leadership & Organizational Studies, 30(3), 265-275.
- Luo, X., Qin, M. S., Fang, Z., & Qu, Z. (2021). Artificial intelligence coaches for sales agents: Caveats and solutions. Journal of Marketing, 85(2), 14-32.
- Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600-1631.
Course slides provided on the e-learning platform are part of the exam program.
Day, D. (2012), "The nature of leadership development" in Day & Antonakis, The Nature of Leadership, chapter 4, Sage.
Eubanks, B. (2022, second edition). Artificial intelligence for HR: Use AI to support and develop a successful workforce, chapters 6 ("Learning and Development") and 7 ("Talent management"), Kogan Page.
Hodges, J. (2017) "Designing and delivering interventions" in Hodges, Consultancy, Organizational Development and Change, chapter 5, Kogan Page.
Yao, M. & Koidan, K. (2024) "Human resources and talent" in Yao & Koidan, Applied Artificial Intelligence: A Handbook for Business Leaders, chapter 18, TOPBOTS.
The following peer-reviewed articles are part of the program (and others will be announced at the beginning of the course):
- Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159-182.
- Quaquebeke, N. V., & Gerpott, F. H. (2023). The now, new, and next of digital leadership: How Artificial Intelligence (AI) will take over and change leadership as we know it. Journal of Leadership & Organizational Studies, 30(3), 265-275.
- Luo, X., Qin, M. S., Fang, Z., & Qu, Z. (2021). Artificial intelligence coaches for sales agents: Caveats and solutions. Journal of Marketing, 85(2), 14-32.
- Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600-1631.
Course slides provided on the e-learning platform are part of the exam program.
Assessment methods and Criteria
The final exam is divided into two parts.
All students must complete a final written exam with open-ended questions. The written exam will take one hour and focus on the course slides and reading material (chapters and papers).
The second part of the exam will be an oral presentation/discussion. The procedure will partially differ for attending and non-attending students.
Attending students will be required to present in groups. They will pretend to "sell" a new AI tool for organizational development that they designed partly in class (benefitting from the supervision of an expert) and partly in groups outside the class. This tool should be developed with a real company or a "type" of company in mind.
Non-attending students will have an individual discussion with the teacher regarding reading material dealing with cases, tools, and AI applications for organizations.
For all students, the second part of the exam will focus on AI's potential applications for the OD practice. It will thus be aimed at verifying the ability to apply knowledge and have a critical point of view on the course topics.
The minimum passing grade for both parts of the exam is 18 out of 30 points.
All students must complete a final written exam with open-ended questions. The written exam will take one hour and focus on the course slides and reading material (chapters and papers).
The second part of the exam will be an oral presentation/discussion. The procedure will partially differ for attending and non-attending students.
Attending students will be required to present in groups. They will pretend to "sell" a new AI tool for organizational development that they designed partly in class (benefitting from the supervision of an expert) and partly in groups outside the class. This tool should be developed with a real company or a "type" of company in mind.
Non-attending students will have an individual discussion with the teacher regarding reading material dealing with cases, tools, and AI applications for organizations.
For all students, the second part of the exam will focus on AI's potential applications for the OD practice. It will thus be aimed at verifying the ability to apply knowledge and have a critical point of view on the course topics.
The minimum passing grade for both parts of the exam is 18 out of 30 points.
M-PSI/06 - WORK AND ORGANIZATIONAL PSYCHOLOGY - University credits: 6
Lessons: 48 hours
Professor:
Gatti Paola