AI-based high school mathematics lessons exhibit four existing and three newly emerging instrumental orchestration types, with the new types appearing in 36 of 46 episodes¶

Claim · All claims
Evidence · 1 study (2 entries) · qualitative r3 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Across 46 episodes from six lessons, 83 orchestration instances were identified: four existing types (technical-support, explain-the-screen, guide-and-explain, spot-and-show) and three new types (dashboard-monitoring, AI-correcting, AI-mediating). → Yun Hwa Noh & Hee-jeong Kim 2026 q1 i? The three newly derived types appeared in 36 of the 46 episodes, indicating AI-based tools elicit teaching practices not captured by existing orchestration theory alone. → Yun Hwa Noh & Hee-jeong Kim 2026 (2)

Evidence¶

Yun Hwa Noh & Hee-jeong Kim 2026¶

Yun Hwa Noh & Hee-jeong Kim. (2026). Exploring teachers' instrumental orchestration and roles in high school mathematics classes using AI-based digital tools. The Mathematical Education 65(2), 309-335. https://doi.org/10.63311/mathedu.26.6525

q1 · i? · qualitative · r3

Directed content analysis of six recorded lessons from one experienced high school teacher, segmented into 46 episodes by tool transitions. The analysis identified 83 orchestration instances: four existing types and three new types ('대시보드-모니터링', 'AI-수정', 'AI-매개') derived inductively from AI tool affordances.

"분석 결과, 전체 46개 에피소드에서 총 83회 오케스트레이션 유형이 식별되었으며, 기존 유형에 해당하는 것은 '기술-지원하기', '화면-설명하기', '안내-설명하기', '발견-보여주기' 4가지이고, AI 기반 디지털 도구의 기능으로 인해 새롭게 도출된 유형은 '대시보드-모니터링', 'AI-수정', 'AI-매개'의 3가지이다."

Yun Hwa Noh & Hee-jeong Kim 2026 (2)¶

Yun Hwa Noh & Hee-jeong Kim. (2026). Exploring teachers' instrumental orchestration and roles in high school mathematics classes using AI-based digital tools. The Mathematical Education 65(2), 309-335. https://doi.org/10.63311/mathedu.26.6525

q1 · i? · qualitative · r3

In the discussion, the authors report that the newly derived orchestration types appeared in 36 of the 46 episodes across the six lessons, which they interpret as evidence that AI-based tools elicit teaching practices not captured by existing orchestration theory in this case.

"특히 새롭게 도출된 유형들이 6차시 수업의 46개 에피소드 중 36개의 에피소드에서 관찰되었다는 점은, 본 사례에서 AI 기반 도구가 기존 오케스트레이션 이론만으로는 포착되지 않는 교수 실천을 유발하고 있음을 보여준다."

Discussion¶

Related Claims¶

• AI-correcting is a newly observed orchestration type in which the teacher identifies erroneous AI feedback, points it out to students, and re-guides or edits AI prompts (10 of 46 episodes) — a narrower finding that bears on this claim
• Dashboard-monitoring is a newly observed orchestration type in which the teacher uses real-time AI dashboards to grasp student status and adjust lesson direction and pace (14 of 46 episodes) — a narrower finding that bears on this claim
• AI-mediating is a newly observed orchestration type in which the teacher concretizes or supplements correct AI feedback for students in curriculum context (12 of 46 episodes) — a narrower finding that bears on this claim
• Explain-the-screen was the most frequent orchestration type (24 of 46 episodes), and in AI-based lessons it was dynamically reconfigured in real time by AI-generated learning data — related
• Teacher roles vary by orchestration type, with content-related and ethical roles prominent when the teacher intervenes in AI-generated feedback, and a knowledge-transmitter role emerging in explain-the-screen — related
• Instructor roles shift from exclusive information source toward orchestrating and regulating AI-mediated learning activities — related