Departmental workshop on AI.

This workshop aims to start a formal conversation about AI with the faculty of my department (sociology). While this is specific to our department and especially our field, the model should apply to all departments willing/needing to start or further formalize the discussion on AI.

Preparation

Pre-Workshop Survey: Conduct a survey to understand participants’ knowledge levels, expectations, and concerns about AI. This could also be done at the beginning of the meeting with a polling software such as Mentimeter or Slido. Based on the data collected before the meeting, the moderator/organizer can tailor time allocation to topics of interest and concerns.

Introduction

Whether we like it or not, fear it, or look forward to it, AI is here and here to stay. It is a technological revolution that will profoundly impact how and what we teach and learn. OpenAI just made a deal with Apple, and its products will be integrated into all Apple systems.

  1. What are your personal experiences or concerns about integrating AI into our academic practice?
  2. Here are a couple of demonstrations of the power of the most recent tools:
  1. Broadly:
  1. More specifically, videos highlighting the broad range of services students can use/purchase.
  1. Demonstrations: If feasible, demonstrate some AI tools or applications relevant to sociology. Request a few prompts and exam questions ahead of the meeting, and run the prompts as a group to examine the responses generated by GPT4. This can spark ideas and show practical examples of AI’s potential.

Facilitating strategies

  • Pre-Session Survey: Before the session, send a brief survey to participants, asking them to share their initial thoughts, concerns, and ideas about AI in sociology. This will help you tailor the session to their interests and generate initial momentum.
  • World Café Method: Set up small group discussions at different tables, each focused on one aspect (teaching, pedagogy, research, testing). Participants rotate tables every 15 minutes, contributing to each topic and building on previous discussions.
  • Mind Mapping: Use large sheets of paper or digital mind mapping tools. Start with a central question (e.g., “How can AI transform sociology teaching?”) and let participants add branches and sub-branches with their ideas and connections.
  • Role-Playing Scenarios
    • Future Scenario Planning: Divide participants into groups and assign each group a specific scenario involving AI in sociology (e.g., fully automated classrooms, AI-driven research labs). Ask them to discuss their scenario’s implications, benefits, and challenges and present their findings.
    • Stakeholder Perspectives: Assign roles such as students, professors, administrators, and AI developers. Participants discuss AI impacts from their assigned perspectives to uncover diverse viewpoints and potential conflicts.
  • SWOT Analysis: Conduct a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) for each aspect of AI’s impact. This structured approach helps in systematically analyzing different dimensions.
  • Brainwriting: Instead of vocal brainstorming, have participants write their ideas on sticky notes or index cards. Collect and display these ideas for group discussion. This can help quieter participants contribute equally.

The big questions

Below is a list of questions I brainstormed with a few colleagues. Those could be sent to the entire faculty before the meeting to determine the meeting’s focus. They could be shown at the beginning of the session, with people ranking them in order of importance, then used to break the group into small, focused groups. The list could also remain a backup while the workshop participants generate it. Regardless of how this list will be used, it is helpful for the organizers to have at least compiled their context/field-specific list of questions. Of course, another school of thought would advocate for an unbiased agenda and an iterative co-generation of those questions in the first part of the meeting.

What are the key questions?

Teaching and learning:

  • What are our teaching/learning goals?
  • What are we trying to measure/assess?
  • How should/will the development of AI tools change/influence/redirect our learning goals in sociology, our assessment tools, and our goals?
  • What are the concrete implications for teaching and teachers and students?
  • How can AI tools support student learning without undermining critical thinking and originality?
  • How can we integrate AI into our teaching practices to complement and not replace traditional learning methods?
  • What are the potential risks of students over-relying on AI for their assignments and research?
  • How can we design assignments and projects that encourage the responsible and critical use of AI?
  • What measures can we implement to detect and prevent academic dishonesty related to the use of AI tools? How can we (re)define academic integrity in this new age of content production?
  • How can we foster a collaborative environment where faculty and students can effectively share their experiences and knowledge about using AI?

Field-specific:

  • What are the current capabilities of AI in terms of data analysis and pattern recognition in sociology?
  • How can AI be used to enhance sociological research and teaching methodologies?
  • How can we ensure that the use of AI in academia does not reinforce existing biases or create new ones?
  • What ethical considerations should we know when using AI in sociological research?

Outcomes

To be engaging and considerate of the faculty time, this workshop cannot merely be about producing a list of questions. It should also aim to produce tools and resources that are usable to all quickly after the workshop. The following topics could be on the agenda.

Integrating AI-related topics into existing sociology courses.

The development of AI constitutes a key moment in science, technology, and the fields of knowledge and content production. Sociology classes are well-suited and probably must include AI-related topics in their curricula. List of courses and topics taught in sociology that could/should include a study or discussion of AI. The participants could be tasked to expand upon the following list and dive deeper into what it will look like in each course. How much time should be allocated? What resources are available? Are textbooks up-to-date on this fast-growing topic? Most academic fields deserve to have a similar conversation.

  • Criminal Justice System
  • Self and identity
  • Sociology of science
  • Knowledge and knowledge production
  • AI + social media
  • Culture
  • Race
  • Gender
  • Privacy and security
  • News, politics, bots, and fake news
  • Deviance and crime
  • The environment
  • Work and capitalism
  • Research methods

More specifically:

  1. AI and Social Inequality:
    • Impact of AI on job displacement and economic inequality.
    • AI bias and its effects on marginalized communities.
    • Access to AI technology and the digital divide.
  1. AI in Social Institutions:
    • Use of AI in criminal justice (e.g., predictive policing, sentencing algorithms).
    • AI in healthcare and its implications for patient care and privacy.
    • AI in education: personalized learning and ethical considerations.
  1. AI and Cultural Change:
    • Influence of AI on cultural production and consumption (e.g., AI-generated art, music, literature, and “knowledge”).
    • AI in social media and its impact on communication and social interactions.
    • Changing perceptions of AI in popular culture and media.
  1. Ethics and Governance of AI:
    • Ethical considerations in AI development and deployment.
    • Regulatory frameworks and policies governing AI.
    • The role of sociologists in shaping AI ethics and governance.
  1. AI and Identity:
    • AI and its impact on personal identity and self-perception.
    • Use of AI in gender and race recognition and the associated controversies.
    • AI-generated influencers and avatars in social media.
  1. AI in the Workplace:
    • Automation and its impact on various industries.
    • The future of work and AI-driven workplace dynamics.
    • AI and the gig economy.
  1. AI and Surveillance:
    • Use of AI in surveillance and its implications for privacy and civil liberties.
    • Societal impacts of mass data collection and AI-driven analysis.
    • Public attitudes towards AI surveillance.
    • The military uses of AI
  1. Human-AI Interaction:
    • Social dynamics of human-AI interaction.
    • Trust and reliance on AI systems.
    • AI as social actors (e.g., robots, virtual assistants).
  1. AI and Democracy:
    • Influence of AI on political processes (e.g., election interference, political ads targeting).
    • AI and public opinion formation.
    • Civic engagement and AI tools.
  1. Global Perspectives on AI:
    • Cross-cultural differences in AI adoption and attitudes.
    • Impact of AI on global development and international relations.
    • AI initiatives and policies in different countries.

Course goals and evaluation methods:

Discuss the value of reading, writing, and being in a sociology class with students, what AI can help with, what it can steal from their experience, and how it takes away their growth opportunities (growth mindset vs. outcome-oriented). What evaluation methods become obsolete or lose their value with the growth of AI tools available to students? To ensure that we are assessing what we think and want to be assessed, how can we/should we rethink evaluation methods? This group could create a list of evaluation methods, tools, and resources specific to the field.

Sociology-focused AI literacy:

This group could generate a list of activities and classroom resources to nurture field-specific AI literacy.

  • Deep fake/hoax-generating images to support hoax
  • Create and /or analyze political content generated by bots
  • Use AI to seek the limitations and biases
  • Create a list of literature
  • Scan articles
  • Draft writing/get feedback

Resources on the CU Teaching and Learning with AI repository can be adapted to sociology courses.

Syllabus statement

A unified syllabus statement on the use of AI would likely benefit both students and faculty. This statement could be drafted by one group and then submitted to the entire faculty for editing and vote.

List of resources

The following list of resources was compiled and provided to the 2024 Summer Studio attendees: Teaching & Learning with AI.

AI Pedagogy Project

Ethics/Critical AI Literacy

Research

Classroom Use Guidelines/Academic Integrity

AI as an Effective Study Tool

Creative Collaborations

Speaker Materials:

Maha Bali 

Slides: https://bit.ly/baliBoulder

Blog: https://blog.mahabali.me/

Lee Frankel-Goldwater

            Slides: AI Literacy in Action for Higher Education

Marc Watkins

            Slides: Rethinking Professional Development in Our AI Era: Moving Beyond ChatGPT

AI Tool: https://lex.page

Diane Sieber

Resources: Generating a First Draft of Your Paper Using Free ChatGPT

Additional Interesting Resources:

·       Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts, Ethan and Lilach Mollick

·       Assigning AI: Seven Approaches for Students, with Prompts, Ethan and Lilach Mollick (some overlap with above)

·       Prompting Diverse Ideas: Increasing AI Idea Variance, Lennart Meincke, Ethan R. Mollick, Christian Terwiesch

AI Tools

List Author(s) Name Here (required): Laurent Cilia
Photo Source (required): Here is a surrealist painting inspired by Salvador Daly, showing the sociology department faculty at CU Boulder discussing AI. Generated by DALL-E
Image Alternate Text: Here is a surrealist painting inspired by Salvador Daly, showing the sociology department faculty at CU Boulder discussing AI. Generated by DALL-E

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