Sem01 (2024-2025) Developing and Organizing Expertise in the Age of AI

Course details

• Practical information

  Academic year
  2024-2025

  Semester
  1

  Period
  2

  Day(s)
  Tuesday & Thursday 

  Time
  19:00 – 21:00 

  Number of meetings
  12 (including trips to the wonderland of expertise transformation!)

  Dates of all meetings
  29, 31 October;
  5, 7, 12, 14, 19, 21, 26, 28 November;
  3, 5 December

  Location
  Vrije Universiteit, De Boelelaan 1105, 1081 HV Amsterdam

  Room
  tbd

  Credits
  6

  Course Coordinator

  • Dr. M.H. Rezazade Mehrizi, Associate Professor, Knowledge, Information and Networks Research Group, School of Business and Economics, Vrije Universiteit Amsterdam

  Guest Lecturers

  • A diverse group of guest lecturers from different domains of expertise (e.g., medicine, data science, cybersecurity, psychology, legal, …) coming from different institutions across the world.
  • Some lecturers from KIN center for digital innovation with strong international research and teaching profiles in the domain of data and analytics and working and organizational dynamics will also contribute to the course.

• Learning objectives

  After completing this advance bachelor course, students will be able to:

  • Critically reflect on the traditional concepts and theories of expertise in respect to today’s technological developments;
  • Engage with designing their own career and developing their expertise with regards to the new dynamics of developing expertise;
  • Actively participate as future leaders in the organization of expertise at the levels of teams (e.g., supporting their peer experts), departments (e.g., mentoring and advising the policies and procedures for developing and organizing expertise), organizations (e.g., leading initiatives related to the development of expertise in public and private organizations), and communities (e.g., actively participate in their communities of practice and learning communities based on new dynamics of expertise);
  • Mindfully shape the development and implementation of novel technologies to ensure effective development and organization of expertise (e.g., experts-in-the-loop development of analytics technologies, effective configurations of technology and work, and mindful operations of technologies for performing expert work);
  • Responsibly participate in the social, cultural, and policy movements for ensuring responsible and effective development and organization of expertise (e.g., movements related to responsibility, transparency, fairness, and inclusivity).

• Working formats & structure

  The course includes a natural integration of four teaching methods:

  • Dialectic lectures: where students come to the lectures already covered the reading materials with a critical mindset; this means that they will bring their own critical summary of the materials and questions to the table. The lectures engage students in sharing, debating, and co-creating around examples and cases (both from their own experiences and the guest lecturers). At the end of lecturers, there will be reflective quizzes and practices;
  • Peer-coaching tutorials: students will discuss on the weekly basis their “own project” during which they focus on one domain of expertise (often resonating with their own career) and apply the concepts and theories discussed in the lectures to it in order to critically reflect and design for their learning journey for developing their expertise;
  • Empirical investigation: Students learn through their empirical research in domains of expertise (often related to their career and interests) by observing the work of experts, interviewing them, and examining the challenges and opportunities of developing and organizing expertise. This offers them a direct exposure to the reality of work and organizing where they must engage with the development of expertise;
  • Reflective practices: Through “research project” and “engagement activities”, the students learn through critical reflection on the concepts and practices and learn how to use these insights for developing ideas for their own career and other stakeholders.

• Assessment methods

  The students will demonstrate their learning through:

  • 30%: Reflective and engagement activities during lectures
  • 70%: Team-based research project (a fun research in a specific domain of expertise)

• Study load

  • Participating in lectures and quizzes:                                    44 hours
  • Reading and critically reflecting on the materials:                60 hours
  • Research project:                                                                     60 hours

• Study material

  Each lecture will be supplied by reading materials from classic and recent readings, such as selective book chapters from (Abbott 2014), (Collins and Evans 2008), (Collins 2018), (Nichols 2017), (Susskind and Susskind 2015), (Willinsky 2000), and (Leonardi and Neeley 2022), as well as journal articles (to be published on Canvas).

• References

  (We will read selective pieces of a wide range of literature, all inspiring and thought provocative; here are some classic references)

  • Abbott, Andrew. 2014. The System of Professions: An Essay on the Division of Expert Labor. University of Chicago Press.
  • Collins, Harry. 2018. Artifictional Intelligence: Against Humanity’s Surrender to Computers. John Wiley & Sons.
  • Collins, Harry, and Robert Evans. 2008. Rethinking Expertise. University of Chicago Press.
  • Leonardi, Paul, and Tsedal Neeley. 2022. The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI. Harvard Business Review Press.
  • Nichols, Tom. 2017. The Death of Expertise: The Campaign against Established Knowledge and Why It Matters. Oxford University Press.
  • Susskind, Richard, and Daniel Susskind. 2015. The Future of the Professions: How Technology Will Transform the Work of Human Experts. Oxford University Press, USA.
  • Willinsky, John. 2000. Technologies of Knowing: A Proposal for the Human Sciences. Beacon Press.