Advancing AI Literacy Among In-Service Humanities and STEM/Computer Science K–12 Teachers

• In Event: Integrating AI Education in K–12 Humanities Curricula: Opportunities, Challenges, Design Considerations, and Promising Solutions

Abstract

Objectives or purposes: Inequitable access to Artificial Intelligence (AI) education in K-12 risks perpetuating inequities into future workforces. Early, equal-access exposure to AI concepts has the potential to seed informed interest in AI among young people and ultimately broaden participation in AI workforces of the future. Thus, a driving question in AI education is how to increase accessibility of AI education. While integration into STEM content areas, such as Math and Science, may seem like a good fit; we argue that teachers of English, Social Studies, Library, and Arts (the Humanities) may be as equally well situated to learn AI concepts as their non-Humanities counterparts and thus should be included in teacher AI education initiatives.

Theoretical framework: Findings are drawn from the Everyday AI (EdAI) research project, a study of teacher professional development (PD) designed to develop teacher AI literacy, called Developing AI Literacy (DAILy), in their classrooms. EdAI and DAILy are designed for implementation without prerequisite knowledge in mathematics or computer programming. Instead, they use interactive approaches, key to teaching machine learning in K-12 (Marques et al., 2020), such as kinesthetic learning (Sivilotti & Pike, 2007), participatory simulation (Klopfer et al., 2005), and unplugged activities (Lindner et al., 2019).

Methods and Data Sources: Of the 23 teachers who participated in the 2021-2022 EdAI PD, 39% (n=9) reported that they either were or had been Humanities teachers prior to participating in the study, including English (21%, n=5), Social Studies (17%, n=4), Library (13%, n=3), and Art (>1%, n=1) with some overlap. Teachers completed an AI concept survey administered before and after a component of the EdAI PD called the AI Book Club, which introduced teachers to AI concepts.

Results: Responses to survey items assessing teachers’ general knowledge of AI concepts differed significantly between pre- and post-surveys (pre M = 12.696, SD = 2.265; post M = 14.435, SD = 1.997; t(43.315) = -2.763, p = 0.008, using Welch’s adjustment) with no significant difference between Humanities and non-Humanities teachers’ responses to items assessing general AI concepts on the pre- or post-test (e.g., Humanities post M = 14.222, SD = 1.641; non-Humanities post M = 14.571, SD = 2.243; t(20.531) = -0.430, p = 0.672, with Welch’s adjustment). Only 17 of the original 23 teachers implemented DAILy in their classroom. Of these 17, 25% (n=4) were Humanities teachers. Every teacher who implemented DAILy, even the Humanities teachers, reported doing so in Computer Science, Science, or Math classrooms. For example, a Librarian and Math teacher implemented DAILy as co-teachers in the school’s library, but shifted to using the math classroom mid-way through the academic year.

Significance: Results from this study suggest Humanities teachers studying the DAILy curriculum through the EdAI PD may be as able to learn AI general concepts as well as their STEM/CS counterparts; however, further research is needed to replicate these findings as well as investigate barriers to the implementation and integration of AI education in K-12 classrooms, particularly for Humanities teachers.

Authors

• Katherine Strong Moore, Massachusetts Institute of Technology

• Helen Zhang, Boston College

• Irene A. Lee, Massachusetts Institute of Technology