Abstract:

In today’s rapidly evolving educational landscape, the integration of Artificial Intelligence (AI) is fundamentally transforming the preparation of teachers for the demands of 21st-century classrooms. This scoping review investigates the incorporation of AI technologies within two essential theoretical frameworks: Technological Pedagogical Content Knowledge (TPACK) and the Unified Theory of Acceptance and Use of Technology (UTAUT), with a specific focus on pre-service teacher education.

Building on the PRISMA-ScR methodology, peer-reviewed studies published from 2015 to 2025 were systematically identified across Scopus, SpringerLink, Web of Science, and ERIC. Preliminary findings reveal several noteworthy patterns. UTAUT-based studies consistently report moderate yet differentiated levels of generative AI acceptance among pre-service teachers, driven primarily by performance expectancy, effort expectancy, and facilitating conditions. Research additionally indicates that dispositional attributes, such as openness, conscientiousness, and agreeableness, enhance willingness to adopt AI tools, while persistent concerns remain regarding academic integrity, overreliance on AI, hallucinated outputs, and the potential erosion of critical thinking.

TPACK-oriented studies show that pre-service teachers tend to privilege technology-focused and curriculum-aligned considerations when designing AI-mediated or AI-adjacent lessons, often underemphasizing learner-centered pedagogical reasoning and inquiry-based dimensions. Emerging work using generative AI as an evaluative partner in lesson design illustrates the capacity of AI dialogue to surface gaps in content knowledge, pedagogical decision-making, and pedagogical content knowledge (PCK), thereby offering opportunities for personalized feedback during teacher preparation.

Importantly, studies that combine TPACK and UTAUT provide a more comprehensive perspective on AI integration. These hybrid investigations demonstrate that technology acceptance factors (e.g., perceived usefulness, ease of use, institutional support) significantly shape the sophistication of teachers’ TPACK enactment. Higher acceptance corresponds with more deliberate integration of AI into lesson planning, critical evaluation of AI-generated outputs, and alignment of AI use with pedagogical and curricular goals. Across these studies, AI literacy, encompassing prompt engineering, bias awareness, and ethical reasoning, emerges as a mediating competence linking acceptance (UTAUT) to pedagogical enactment (TPACK).

Taken together, these preliminary findings highlight the complementary explanatory power of TPACK and UTAUT for understanding AI-supported teacher preparation. The review contributes conceptual clarity to an emerging field and provides foundational insights to inform the design of AI-informed teacher training programs capable of preparing future educators for an AI-intensive educational environment.

Keywords:

Artificial Intelligence (AI), Pre-Service Teachers, Teacher Education, TPACK Framework, UTAUT Model.