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Abstract Artificial intelligence (AI) has gained widespread public interest in recent years. However, as AI literacy remained excluded from the standard academic curricula, AI education in the US was predominantly offered through extra-curricular activities, which limited AI learning exposure to only a select group of students. Given these limitations, the need to integrate AI literacy education into the standard curricula is increasingly evident. This study investigated the integration of AI learning in an advanced biology course. Thirty-seven students participated in four lessons embedding AI learning in biology contexts. The interplay of students’ AI learning and biology knowledge was examined from the quantitative measure of conceptual understanding and qualitative analysis of interdisciplinary reasoning. This concurrent triangulation research design utilized results from both quantitative and qualitative analyses to develop a comprehensive understanding of students’ AI learning in the biology context. The results of the study showed a significant improvement in students’ AI concepts. Students’ biology knowledge had a slight increase, but it was not statistically significant. Both quantitative and qualitative results underscored a close connection between students’ AI learning and their biology knowledge, though the quantitative findings were not conclusive in some lessons. The article concluded with a discussion of the potential reasons for those discrepancies. In addition, suggestions were provided for future research and practitioners who are interested in integrating AI education across curricula. 

This study analyzed 281 lesson plans collected from the producers’ websites of 12 educational physical computing and robotics (ePCR) devices. We extracted and coded five variables from each lesson. They were ePCR functionality, coding skills, computational thinking skills, math knowledge, and activity design. First, a two-step cluster analysis was administered to find how three ePCR-related knowledge: ePCR functionality, coding skills, and computational thinking skills, were integrated to teach students ePCR technology in middle-grade math lessons. Results showed three types of lesson plans, including lessons to use basic ePCR functionality to teach students lower-level CT skills, lessons to teach students basic to intermediate coding skills, and lessons to use the technology at the advanced level. Next, we applied the Technological Pedagogical Content Knowledge (TPACK) framework and conducted a second two-step cluster analysis to identify how the technology (ePCR technology), content (math knowledge), and pedagogy (activity design) were integrated into those lesson plans. Results suggested ten clusters of lesson plans with distinct features. We summarized those ten lesson clusters into five categories: 1) ePCR technology lessons, 2) transdisciplinary problem-based learning lessons, 3) technology-assisted lessons, 4) lessons without real-world connections, and 5) lessons integrating middle-grade math learning into ePCR projects. Implications for educators and researchers were discussed at the end of the article.