About This Course

We Are AI is a dynamic collaboration between the Center for Responsible AI (R/AI) at New York University’s Tandon School of Engineering, Peer 2 Peer University (P2PU), and the Queens Public Library (QPL).

Course materials were developed under the leadership of Dr. Eric Corbett and Dr. Julia Stoyanovich, with input and participation of Dr. Mona Sloane, Falaah Arif Khan, Meghan McDermott from R/AI, Becky Margraf and Grif Peterson from P2PU, and Jeffrey Lambert, Sadie Coughlin-Prego, Kaven Vohra from QPL.

Course development was supported in part by NSF Awards No. 1934464 and 1916505.

Design Notes from the course creators

Most existing education programs for AI assume an interest (and eventual career goal) in engineering or data science for participants. These programs are technical in nature and preclude the vast majority of people who do not have these interests or goals but are still subjected to AI in their lives. What form of AI literacy the other 99% should possess remains an ongoing question for society. A 2020 paper on AI literacy has provided an intellectual framework for this question by defining AI literacy and providing a set of competencies that comprise it. AI literacy is defined as:

“A set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace.”

We draw from that paper by designing the course around a subset of the competencies and design considerations they developed. We selected the following five as the most appropriate to the time and format of learning circles:

  • Competency 1: The ability to recognize AI
  • Competency 12: Understand that learning happens from data
  • Competency 13: Be critical interpreting data
  • Competency 16: Identify and describe key ethical issues surrounding AI
  • Design Consideration 8: Encourage learners to be critical consumers of AI

Each module draws from these competencies:

  • Module 1: What Is AI? -> Competency 1
  • Module 2: Learning From Data -> Competency 12
  • Module 3: Who Lives, Who Dies, Who Decides? -> Competency 16
  • Module 4: All About That Bias -> Competency 13
  • Module 5: We Are AI -> Design Consideration 6

These five modules are intended to accomplish the goal of closing the distance people feel towards AI: the social distance (e.g., “AI is for computer scientists…”); the distance in knowledge (e.g., “AI is too complex for me, too much math…”); and the distance in power (e.g., “there is nothing I can do about AI…”). The learning circle needs to engender a sense of civic agency within participants towards AI to close these feelings of distance. The social constructivist view of learning aligns well with this goal of closing distance, so we drew from it to guide the pedagogical design.

Social constructivists believe individuals produce knowledge socially and culturally and lso that individuals’ activities construct their understanding of reality. Put more simply, they believe that learning itself is a social process (Kim, 2001). Accordingly, Richardson (2003) notes that the focus of social constructivist pedagogy is “the consideration of how the individual learner learns ways of facilitating that learning, first in individual learners and then in groups of learners.” He adds that “if the course content is related to the learner’s particular social context and their cultural and value system, learning is more likely to occur.”

Following this view, we designed the course in such a way that requires learners to draw upon their experiences and allow them to “make meaning in dialogues and activities about shared problems or tasks.” We want learners to perform the competencies that underlie each module together. We designed short videos to provide learners with concepts to draw upon directly in the activities to accomplish this. The post-activity discussions are intended to consummate the learning process through collective dialogue.

The following notes are post hoc design reflections that provide deeper insights into how the content enacts the competencies from the AI Literature paper.

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