Promoting the AI teaching competency of K-12 computer science teachers: A TPACK-based professional development approach

Sun, Junmei; Ma, Hongliang; Zeng, Y.; Han, Dawei; Jin, Yunbo

doi:10.1007/s10639-022-11256-5

Cited by 22 publications

(14 citation statements)

References 57 publications

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“…The AI literacy programme was designed using the revised TPACK framework (Sun et al., 2023), and Bloom's taxonomy as extending previous work by Ng, Lee, et al, (2023), as shown in Figure 2. There are two cognition levels: know and understand AI (lower cognition level), and apply, evaluate and create (higher cognition level) across 10 topics.…”

Section: Methodsmentioning

confidence: 99%

Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

Ng,

Wu,

Leung

et al. 2023

Brit J Educational Tech

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Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy. Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy. Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes. What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions. Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy. Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy. Practitioners are able to use the AILQ to assess students' AI literacy development.

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Section: Methodsmentioning

confidence: 99%

Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

Ng,

Wu,

Leung

et al. 2023

Brit J Educational Tech

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“…Teacher professional development workshops have been conducted to integrate AI concepts into STEM classes for high school students . For computer science teachers, a technological pedagogical content knowledge (TPACK) framework was introduced for professional training (Sun et al 2022). Akram et al utilized the Snap programming environment to teach about basic AI concepts in search algorithms which was introduced to teachers in two-week workshops (Akram et al 2022).…”

Section: Related Literature Teacher Professional Development In Ai Fo...mentioning

confidence: 99%

ImageSTEAM: Teacher Professional Development for Integrating Visual Computing into Middle School Lessons

Jayasuriya,

Swisher,

Rego

et al. 2024

AAAI

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Artificial intelligence (AI) and its teaching in the K-12 grades has been championed as a vital need for the United States due to the technology's future prominence in the 21st century. However, there remain several barriers to effective AI lessons at these age groups including the broad range of interdisciplinary knowledge needed and the lack of formal training or preparation for teachers to implement these lessons. In this experience report, we present ImageSTEAM, a teacher professional development for creating lessons surrounding computer vision, machine learning, and computational photography/cameras targeted for middle school grades 6-8 classes. Teacher professional development workshops were conducted in the states of Arizona and Georgia from 2021-2023 where lessons were co-created with teachers to introduce various specific visual computing concepts while aligning to state and national standards. In addition, the use of a variety of computer vision and image processing software including custom designed Python notebooks were created as technology activities and demonstrations to be used in the classroom. Educational research showed that teachers improved their self-efficacy and outcomes for concepts in computer vision, machine learning, and artificial intelligence when participating in the program. Results from the professional development workshops highlight key opportunities and challenges in integrating this content into the standard curriculum, the benefits of a co-creation pedagogy, and the positive impact on teacher and student's learning experiences. The open-source program curriculum is available at www.imagesteam.org.

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“…On the basis of an analysis of AI curricula and resources that was conducted using the TPACK framework, Kim et al (2021) proposed the most crucial teacher competencies for the effective teaching and learning of AI in K-12 education. Sun et al (2023) designed a professional development program based on the TPACK framework to improve the AI teaching competency of in-service computer science teachers, including their AI knowledge, teaching skills, and teaching self-efficacy. Celik (2023) proposed the Intelligent-TPACK framework for effectively integrating AI into education, and they emphasized the importance of teachers' AI-specific ethical knowledge.…”

Section: Tpack Frameworkmentioning

confidence: 99%

Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education

Yue,

Jong,

2024

Educ Inf Technol

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Artificial intelligence (AI) education is increasingly being recognized as essential at the K–12 level. For better understanding teachers’ preparedness for AI education and effectively developing relevant teacher training programs, teachers’ technological pedagogical content knowledge (TPACK) readiness and attitudes toward AI teaching must be determined. However, limited research has been conducted on this topic. To address this research gap, we recruited 1,664 K–12 teachers to obtain a comprehensive view of teachers’ readiness for and attitudes toward teaching AI in K–12 classrooms. These teachers differed in terms of their gender, teaching subject, teaching grade, teaching experience, and experience in teaching AI. The findings of this study indicated that a substantial gap exists in the AI-related content and technological knowledge of the recruited teachers. Moreover, intriguing relationships were found between the teachers’ pedagogical knowledge, content knowledge, and attitudes toward teaching AI. The effects of demographic factors on the teachers’ TPACK and attitudes were also examined. On the basis of the findings of this study, recommendations were formulated for developing effective teacher professional development programs in the field of AI education.

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Promoting the AI teaching competency of K-12 computer science teachers: A TPACK-based professional development approach

Cited by 22 publications

References 57 publications

Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

ImageSTEAM: Teacher Professional Development for Integrating Visual Computing into Middle School Lessons

Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education

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