AI curricula are being developed and tested in classrooms, but wider adoption is premised by teacher professional development and buy-in. When engaging in professional development, curricula are treated as set in stone, static and educators are prepared to offer the curriculum as written instead of empowered to be leaders in efforts to spread and sustain AI education. This limits the degree to which teachers tailor new curricula to student needs and interests, ultimately distancing students from new and potentially relevant content. This paper describes an AI Educator Make-a-Thon, a two-day gathering of 34 educators from across the United States that centered AI curriculum co-design as the culminating experience of a year-long professional development program called [anonymized] in which educators studied and practiced implementing an innovative curriculum for [anonymized] in their classrooms. Inspired by the energizing and empowering experiences of Hack-a-Thons, the Make-a-Thon was designed to increase the depth and longevity of the educators’ investment in AI education by positively impacting their sense of belonging to the AI community, AI content knowledge, and their self-confidence as AI curriculum designers. In this paper, we describe the Make-a-Thon design, findings, and recommendations for future educator-centered Make-a-Thons.
Thu 16 MarDisplayed time zone: Eastern Time (US & Canada) change
13:45 - 15:00 | AI/ML Literacy, Activities, and FairnessPapers at 715 Chair(s): Jill Westerlund University of Alabama | ||
13:45 25mPaper | Developing Machine Learning Algorithm Literacy with Novel Plugged and Unplugged ApproachesK12In-PersonGlobal Papers Ruizhe Ma University of Massachusetts Lowell, Ismaila Temitayo Sanusi University of Eastern Finland, Vaishali Mahipal University of Massachusetts Lowell, Joseph Gonzales University of Massachusetts Lowell, Fred Martin University of Massachusetts Lowell DOI | ||
14:10 25mPaper | Make-a-Thon for Middle School AI EducatorsK12In-Person Papers Daniella Dipaola MIT Media Lab, Katherine S. Moore MIT, Safinah Ali MIT, Beatriz Perret MIT, Xiaofei Zhou University of Rochester, Helen Zhang Boston College, Irene Lee Massachusetts Institute of Technology DOI | ||
14:35 25mPaper | Towards Machine Learning Fairness Education in a Natural Language Processing CourseK12In-Person Papers Samantha Dobesh Western Washington University, Tyler Miller Western Washington University, Pax Newman Western Washington University, Yudong Liu Western Washington University, Yasmine Elglaly Western Washington University DOI |