Exploring teachers’ perceptions of integrating artificial intelligence (AI) in STEM education using the TPACK framework: an exploratory case study

Alkubaisi, Moza (2025) Exploring teachers’ perceptions of integrating artificial intelligence (AI) in STEM education using the TPACK framework: an exploratory case study. Discover Artificial Intelligence, 5 (1). ISSN 2731-0809

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Abstract

Background: As artificial intelligence (AI) continues to transform various sectors, including education, the perceptions and attitudes of teachers remain underrated. AI-based tools such as Intelligent Tutoring Systems (ITS) and chatbots have recently gained traction in education, offering innovative ways to enhance teaching and learning. Aims: This case study aims to examine the perceptions and attitudes of STEM teachers in Qatar regarding the integration of AI tools into their practices. This study also aims to assess teachers’ awareness of the ethical concerns associated with AI-based tools, primarily concerns related to fairness, inclusiveness, privacy, and bias. Material and methods: This case study employs an exploratory approach using online questionnaires as a data collection instrument. The TPACK framework guided the questionnaire items to further explore how teachers’ technological, pedagogical, and content knowledge influences their attitudes towards using AI-powered tools in their teaching. The online questionnaires were distributed to three STEM schools in Qatar between April and May 2023. The sample size was 12 teachers. Results: This paper reveals that while STEM teachers perceive themselves as possessing moderate technological knowledge of AI tools, they recognize the potential of AI in delivering personalized and adaptive learning experiences. The findings reinforce existing literature on the pedagogical affordances of AI in enhancing teaching practices and improving student learning outcomes. Moreover, the study underscores a critical need to strengthen teacher readiness for AI integration, highlighting the role of targeted professional development in equipping educators with the skills and confidence necessary to effectively leverage AI in STEM education. Limitations and recommendations: Few limitations affect the findings of this study. Limited sample size may affect the depth and breadth of the findings; therefore, future research is needed to increase the population and expand the findings by involving different stakeholders. Also, self-reporting bias and timeframe constraints may affect the results, thus addressing these in future research will contribute to the ongoing debate about the effectiveness of AI-based tools in education.