Modeling AI-TPACK in Practice Insights from Teachers Multi-Agent Workflow Design
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909040993370112 |
|---|---|
| author | Sun, Yimeng Xin, Haiyang Li, Shuang Niu, Qiannan Chai, Ching Sing Huang, Lingyun Chen, Gaowei |
| author_facet | Sun, Yimeng Xin, Haiyang Li, Shuang Niu, Qiannan Chai, Ching Sing Huang, Lingyun Chen, Gaowei |
| contents | This study investigates teachers design behaviors and cognitive underpinnings when designing multi-agent instructional workflows. Analyzing behavioral logs (N=61), cluster and Markov analyses identified three archetypes: Systematic Optimizers iteratively refining complex architectures; Prolific Creators rapidly prototyping pragmatic tools via scaffolding; and Passive Observers exhibiting polarized expert-novice profiles. Subsequent artifact (n=15) and interview (n=12) analyses reveal AI-TPACK integration emerges from a dynamic interplay of systems thinking, pedagogical beliefs, and self-efficacy, not merely from the possession of discrete knowledge. These findings call for differentiated scaffolding responsive to teachers cognitive-behavioral diversity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13906 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modeling AI-TPACK in Practice Insights from Teachers Multi-Agent Workflow Design Sun, Yimeng Xin, Haiyang Li, Shuang Niu, Qiannan Chai, Ching Sing Huang, Lingyun Chen, Gaowei Computers and Society This study investigates teachers design behaviors and cognitive underpinnings when designing multi-agent instructional workflows. Analyzing behavioral logs (N=61), cluster and Markov analyses identified three archetypes: Systematic Optimizers iteratively refining complex architectures; Prolific Creators rapidly prototyping pragmatic tools via scaffolding; and Passive Observers exhibiting polarized expert-novice profiles. Subsequent artifact (n=15) and interview (n=12) analyses reveal AI-TPACK integration emerges from a dynamic interplay of systems thinking, pedagogical beliefs, and self-efficacy, not merely from the possession of discrete knowledge. These findings call for differentiated scaffolding responsive to teachers cognitive-behavioral diversity. |
| title | Modeling AI-TPACK in Practice Insights from Teachers Multi-Agent Workflow Design |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2605.13906 |