This work synthesizes findings into a conceptual model explaining how teachers coordinate instructional intent, AI assistance, and judgments of classroom readiness during lesson authoring while preserving teacher control and addressing computing-specific authoring needs.
Abstract
Generative AI is increasingly studied as a tool for supporting lesson authoring; the creation and adaptation of classroom materials, yet little is known about how it should fit into secondary computing teachers'subject-specific practices. We conducted two complementary qualitative studies with eight secondary computing teachers: focus groups examining existing authoring practices and a think-aloud study using LessonBuilder, a structured AI-supported lesson authoring system, as a research probe. We found that teachers'instructional intentions developed through creating, inspecting, and adapting materials, while teachers selectively delegated tasks to AI and retained responsibility for judging classroom readiness. The think-aloud sessions further showed how examining AI-generated materials prompted teachers to clarify and refine their intentions, making authoring an ongoing negotiation between what teachers intended and what the AI produced. In the context of computing lessons, classroom readiness also involved considering how programming materials and supporting guidance matched students'understanding and intended learning goals. We synthesize these findings into a conceptual model explaining how teachers coordinate instructional intent, AI assistance, and judgments of classroom readiness during lesson authoring. We derive design recommendations for supporting this coordination while preserving teacher control and addressing computing-specific authoring needs.
This study explored how pre-service physics teachers design artificial intelligence (AI)-based pedagogical infrastructure to support metacognitive learning in STEM classrooms. A qualitative interpretive design was employed with 30 pre-service physics teachers enrolled in a physics teacher education program at a univers...
Дарын Серикулы Сайынов, Zhadyra Yermekova· Frontiers in Education· 0 citations
This study investigates how generative Artificial Intelligence (AI) can scaffold self-directed learning (SDL) in secondary classrooms. Focusing on the interaction between teacher facilitation and AI affordances, it examines and refines the Content–Response–AI&peers–Fusion–reflecT (CRAFT) framework as a practitioner-inf...
Alwyn Vwen Yen Lee, Timothy Cheng, Joo Seng Melvin Chan· AI in Education· 0 citations
Artificial intelligence (AI) has become increasingly
embedded in EFL teaching, reshaping how teachers plan
lessons, design materials, and provide feedback. However,
while previous research has focused mainly on AI adoption,
competence, and attitudes, less is known about how AI-
mediated teaching influences teacher moti...
T. Dang, T. Nguyen· Vinh University Journal of S...· 0 citations
Drawing on daily individual reflections, group reflections, and post-camp interviews, it is found that teachers' adaptive practices of repair, differentiation, translation, and balancing sit at the intersection of three tensions (technology, learner, and instruction).
Fasika Melese, Rui-Yang Wu, Xin-Yue Cui et al.· 0 citations
Evidence supports a pedagogy-first model in which AI augments rather than substitutes for teacher judgement, materials are grounded in curricular sources, outputs are verified, students are taught to interrogate AI responses, and use is age-appropriate and governed by clear data and accountability rules.
Nibedita Sarma· Asian Journal of Education a...· 0 citations
Lesson study is an educator‐led professional development approach that originated in Japan, in which a group of educators collaboratively plan, teach, observe, reflect, and revise lessons to improve teaching and learning. In this study, five teacher educators collaborated in the context of lesson study to explore...
J. Robinson, Evrim Erbilgin, Jason D. Johnson et al.· Journal of Computer Assisted...· 0 citations
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