Affective Factors and AI Instructional Implementation: Insights from a Statewide Survey of K-12 Computer Science Teachers in PennsylvaniaK12
This program is tentative and subject to change.
Artificial intelligence (AI) technologies are rapidly reshaping teaching and learning in K–12 education. Yet, little is known about the factors influencing K–12 computer science (CS) teachers’ attitudes towards AI technologies or their integration of AI-related instruction. This exploratory study examines the relationship between CS teachers’ familiarity with generative AI, their attitudes toward AI technologies in K-12 education, and their integration of instruction on AI concepts into CS courses. Using survey data from 632 K–12 CS teachers across Pennsylvania, we explored (1) how familiarity with generative AI relates to CS teachers’ support for AI integration, efficacy to teach AI concepts, and AI instructional time in CS courses; and (2) whether familiarity with AI and AI instruction time vary across teacher characteristics. Finding 1. Correlational analyses revealed moderate to strong positive relationships between AI familiarity and support for AI integration in K-12 domains (r = .35), efficacy to teach AI concepts (r = .53), and hours of instruction on AI principles in CS courses (r = .32). Finding 2. Regression models indicated that male-identifying teachers reported significantly higher familiarity with generative AI technologies and greater integration of instruction on AI concepts than female-identifying teachers. Additionally, high school teachers reported dedicating more instructional time to AI concepts in their CS courses than CS teachers at other grade levels. Consistent with prior work on computing technology adoption, findings provide early evidence that teachers’ familiarity with AI is closely associated with affective factors that shape their support for and implementation of AI-related instruction.