How Can (A)I Research This? An Autoethnographic Exploration of Generative AI in Research, Teaching and Instructional Design

The autoethnographic study investigates the transformative impact of generative AI on educational research, instructional design, and teaching practices over a 5-month period (May–October 2024). By integrating AI tools into every phase of the research process, the study examines AI’s role as both a research partner and a subject of inquiry. Field notes, queries, and AI-generated outputs were systematically collected, creating a corpus for analysis. Grounded in activity theory, this research offers a reflective narrative on the evolving work routines of instructional designers and educators, emphasizing the orchestration of technology rather than prescriptive best practices. The study contributes to educational technology research by documenting the use of AI at a specific point in time, providing a foundation for future inquiry into the practical implications of AI in education.

Authors

Institutions

Publication Details

Journal
Journal of Teacher Education
Published
2025-03-29
DOI
https://doi.org/10.1177/00224871251325065
Citations
6
Primary Topic
Innovative Education and Learning Practices
Type
article
Field-Weighted Citation Impact
67.91
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

How Can (A)I Research This? An Autoethnographic Exploration of Generative AI in Research, Teaching and Instructional Design

Stefanie Panke
6 citations
Journal of Teacher Education
Innovative Education and Learning Practices
67.91
article

How Can (A)I Research This? An Autoethnographic Exploration of Generative AI in Research, Teaching and Instructional Design

Stefanie Panke
article en
6 citations

Abstract

The autoethnographic study investigates the transformative impact of generative AI on educational research, instructional design, and teaching practices over a 5-month period (May–October 2024). By integrating AI tools into every phase of the research process, the study examines AI’s role as both a research partner and a subject of inquiry. Field notes, queries, and AI-generated outputs were systematically collected, creating a corpus for analysis. Grounded in activity theory, this research offers a reflective narrative on the evolving work routines of instructional designers and educators, emphasizing the orchestration of technology rather than prescriptive best practices. The study contributes to educational technology research by documenting the use of AI at a specific point in time, providing a foundation for future inquiry into the practical implications of AI in education.

Journal of Teacher EducationVol. 76(3)
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 0%
Innovative Education and Learning Practices
67.91
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

How Can (A)I Research This? An Autoethnographic Exploration of Generative AI in Research, Teaching and Instructional Design — Stefanie Panke · Journal of Teacher Education (2025) | TGRS Research Map | TGRS