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<p>This study investigates how 227 K-12 teachers who taught mathematics, algebra, English Language Arts (ELA), social studies, and economics utilized Artificial Intelligence (AI) tools during graduate coursework, and the instructional goals these tools supported. Teachers used various AI features on a free platform (Teacherserver.com) to design lessons, generate assessments, and tailor instruction. Qualitative data were collected through open-ended survey responses and analyzed using a six-phase thematic analysis process: familiarization, coding, theme development, theme review, theme definition, and final reporting. The analysis is framed by the Technological Pedagogical Content Knowledge (TPACK) framework, which emphasizes the intersections of teachers’ technological, pedagogical, and content knowledge. Results show that, rather than a one-size-fits-all approach, teachers selected AI tools aligned with specific disciplinary outcomes—for example, scaffolding mathematical problem-solving, improving students’ writing, simulating economic scenarios, or analyzing historical texts. Teachers recognized benefits including increased efficiency, personalized learning, and student engagement. However, they also voiced concerns about content accuracy, curriculum alignment, customization limits, over-reliance by students, and unequal technology access. A notable finding was teachers’ strong desire to expand their AI literacy through professional development, peer networks, and self-directed exploration. This suggests that effective integration requires not only tool access but also ongoing, subject-specific training. Grounding the findings in TPACK highlights that meaningful AI integration depends on how teachers negotiate the relationships among technology, pedagogy, and content. The study offers implications for policy and professional learning programs aiming to promote responsible, context-sensitive AI adoption in education. Findings should be interpreted with caution, however, due to reliance on self-reported data and the use of a convenience sample of graduate students, which may limit generalizability.</p>

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Exploring K-12 content-area teachers’ preferences and challenges in using AI tools in graduate coursework

  • 期刊论文

  • 发表日期：

    2025年9月30日

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  • Aslihan Unal,
  • Zafer Unal

摘要

This study investigates how 227 K-12 teachers who taught mathematics, algebra, English Language Arts (ELA), social studies, and economics utilized Artificial Intelligence (AI) tools during graduate coursework, and the instructional goals these tools supported. Teachers used various AI features on a free platform (Teacherserver.com) to design lessons, generate assessments, and tailor instruction. Qualitative data were collected through open-ended survey responses and analyzed using a six-phase thematic analysis process: familiarization, coding, theme development, theme review, theme definition, and final reporting. The analysis is framed by the Technological Pedagogical Content Knowledge (TPACK) framework, which emphasizes the intersections of teachers’ technological, pedagogical, and content knowledge. Results show that, rather than a one-size-fits-all approach, teachers selected AI tools aligned with specific disciplinary outcomes—for example, scaffolding mathematical problem-solving, improving students’ writing, simulating economic scenarios, or analyzing historical texts. Teachers recognized benefits including increased efficiency, personalized learning, and student engagement. However, they also voiced concerns about content accuracy, curriculum alignment, customization limits, over-reliance by students, and unequal technology access. A notable finding was teachers’ strong desire to expand their AI literacy through professional development, peer networks, and self-directed exploration. This suggests that effective integration requires not only tool access but also ongoing, subject-specific training. Grounding the findings in TPACK highlights that meaningful AI integration depends on how teachers negotiate the relationships among technology, pedagogy, and content. The study offers implications for policy and professional learning programs aiming to promote responsible, context-sensitive AI adoption in education. Findings should be interpreted with caution, however, due to reliance on self-reported data and the use of a convenience sample of graduate students, which may limit generalizability.