Autoethnographic research reveals that generative AI transforms educational research and practice through a complex interplay of tool adaptation, professional identity shifts, and contradictory organizational norms, requiring educators to orchestrate technology rather than follow prescriptive best practices.

Objective: The main goal of this study was to investigate how generative AI impacts educational research, teaching, and instructional design practices through autoethnography, examining the researcher's own engagement with AI tools across various professional activities over a 5-month period (May-October 2024).

Methods: The researcher employed digital autoethnography, documenting her experiences using generative AI tools in research, teaching, and instructional design through 92 field notes and 93 AI queries over 150 days. Data collection involved saving field notes and AI interactions in Apple Notes, with analysis conducted using AI tools for open coding under human supervision. The activity theory framework was used to analyze how AI tools mediate human activities within their socio-cultural context, focusing on the subject (researcher), tools (AI applications), rules (professional norms), community (professional networks), division of labor, and object/outcome of activities.

Key Findings:

• Generative AI significantly transformed the researcher's writing habits and information behavior, replacing or complementing traditional web searches and changing the use of authoring tools.
• AI was used extensively across all phases of the research process, from ideation to publication, with 41 queries related to data collection and analysis alone.
• For instructional design, AI proved valuable for tasks such as developing course materials, creating multimedia content, producing quizzes, designing scenarios, and enhancing accessibility.
• Contradictions emerged between institutional encouragement to use AI for efficiency and cautions against its use due to ethical concerns.
• The researcher's perception of AI oscillated between seeing it as a trustworthy collaborator and a temperamental tool requiring careful management.
• While AI expedited routine tasks, it proved unreliable for autonomous data analysis and added significant training and professional development needs.
• Gender differences in AI skill development were observed, with male students showing better problem-solving abilities while female students demonstrated improvement in self-learning skills over time.

Implications:

• The study challenges the notion of "best practices" for AI use, suggesting that educators must instead focus on orchestrating technology within their specific contexts.
• Professional roles in education are shifting as AI takes over labor-intensive tasks and participates in creative and evaluative activities.
• The integration of AI into educational practices creates tensions between efficiency goals and concerns about quality, ethics, and professional identity.
• The research demonstrates how AI tools are shaped through "design-in-use" and "instrumental genesis," where users adapt technologies to meet their specific needs.
• Teachers and instructional designers face conflicting expectations to be both AI experts and cautious AI users, navigating between innovation and institutional constraints.
• AI tools may have a polarizing effect, making highly skilled professionals more productive while offering mediocre solutions for those with less expertise.

Limitations:

• The act of field noting and saving queries likely altered the researcher's everyday practices and AI usage patterns.
• The researcher may have unconsciously avoided documenting unproductive AI queries due to the cumbersome documentation process.
• As a single-person autoethnography, the findings reflect one individual's experiences and cannot be generalized.
• The study does not address privacy concerns related to AI use or the broader ecosystem of non-generative AI that shapes digital experiences.

Future Directions:

• Replicate the study with different populations, such as K-12 teachers or preservice teachers, to offer diverse professional perspectives.
• Analyze user agreements and data collection practices of AI tools to better understand privacy implications.
• Explore the less obvious daily interactions with non-generative AI systems that shape educational technologies.
• Investigate how "maker pedagogy" might offer alternatives to AI-dependent approaches by emphasizing the process of creation over efficiency.
• Examine the conflicting normative expectations for teachers and instructional designers regarding AI use in educational settings.

Title and Authors: "How Can (A)I Research This? An Autoethnographic Exploration of Generative AI in Research, Teaching and Instructional Design" by Stefanie Panke.

Published On: 2025

Published By: Journal of Teacher Education, Vol. 76(3), published by the American Association of Colleges for Teacher Education.