References ı Appendix ı Author ı Metadata
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References
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., Naidich, D. P., & Shetty, S. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose CT. Nature Medicine, 25, 954–961. https://doi.org/10.1038/s41591-019-0447-x
Botelho, E. L., Powell, K. R., Kincaid, S., & Wang, D. (2017, May). What sets successful CEOs apart. Harvard Business Review. https://hbr.org/2017/05/what-sets-successful-ceos-apart
Botelho, E. L., & Powell, K. R. (2018). The CEO next door: The 4 behaviors that transform ordinary people into world-class leaders. Currency.
Chen, C., & Cui, Z. (2025). Impact of AI-assisted diagnosis on American patients’ trust in and intention to seek help from health care professionals: Randomized, web-based survey experiment. Journal of Medical Internet Research, 27, e66083. https://doi.org/10.2196/66083
Every Learner Everywhere. (2020). Arizona State University: Scaling student success with adaptive courseware (Case study). https://www.everylearnereverywhere.org
Haynes, A. B., Weiser, T. G., Berry, W. R., Lipsitz, S. R., Breizat, A. H. S., Dellinger, E. P., Herbosa, T., Joseph, S., Kibatala, P. L., Lapitan, M. C. M., Merry, A. F., Moorthy, K., Reznick, R. K., Taylor, B., & Gawande, A. A. (2009). A surgical safety checklist to reduce morbidity and mortality in a global population. New England Journal of Medicine, 360(5), 491-499. https://doi.org/10.1056/NEJMsa0810119
Haynes, A. B., Edmondson, L., Lipsitz, S. R., Molina, G., Neville, B. A., Singer, S. J., Moonan, A. T., Childers, A. K., Foster, R., Gibbons, L. R., Gawande, A. A., & Berry, W. R. (2017). Mortality trends after a voluntary checklist-based surgical safety collaborative. Annals of Surgery, 266(6), 923–929. https://doi.org/10.1097/SLA.0000000000002249
Hölzel, B. K., Carmody, J., Vangel, M., Congleton, C., Yerramsetti, S. M., Gard, T., & Lazar, S. W. (2011). Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Research: Neuroimaging, 191(1), 36–43. https://doi.org/10.1016/j.pscychresns.2010.08.006
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv preprint arXiv:2506.08872. https://arxiv.org/abs/2506.08872
Nadella, S. (2016, August 4). The learn-it-all does better than the know-it-all [Interview]. Bloomberg Businessweek. https://www.bloomberg.com/news/articles/2016-08-04/microsoft-ceo-satya-nadella-on-his-plans-for-a-comeback
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Appendix: Companion Worksheets for Learn-It-All Educator
Reading about teaching with AI is not the same as doing it. The four companion worksheets that accompany this guidebook translate each chapter’s frameworks into hands-on, reflective activities designed for working instructors. They are built for pen-in-hand engagement: self-audits, task mapping, prompt construction, assignment redesign, identity interrogation, and concrete action planning.
Each worksheet follows a consistent structure. Every activity begins with a Concept drawn from the chapter, followed by a Directed Task tied to your specific course, discipline, and students, and closes with a Response Area for written reflection. You do not need to complete every activity. Choose the ones most relevant to your current teaching context.
The worksheets are designed for use in workshops, faculty learning communities, department retreats, or self-guided professional development. They work equally well completed alone at a desk or discussed collaboratively in a group. Several activities explicitly ask you to pair with a colleague from a different discipline—the cross-disciplinary comparisons are where the most productive disagreements tend to surface.
How to Access the Worksheets
All four worksheets are available as free, downloadable PDFs under the same Creative Commons (CC BY 4.0) license as this guidebook:
dataii.com/ai/guidebook/#worksheets and also below each chapter in this book.
You may print them, project them in a workshop, annotate them digitally, or adapt them for your institutional context. Attribution is required; permission is not.
What the Worksheets Ask You to Do
Across the four worksheets, you will encounter over sixty activities. They cluster around several modes of critical engagement:
- Classify and prioritize. Distinguish between work worth delegating and work worth protecting. Map your time. Name the tasks. Decide what stays human.
- Build and test. Construct real AI prompts, run them, evaluate the output, then redesign your own assignments using the same scaffolding principles.
- Audit and verify. Practice a five-step verification protocol on AI-generated content. Develop the editorial eye that distinguishes plausible from accurate.
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- Reflect and confront. Interrogate your own resistance. Label it honestly. Write a Permission to Learn letter. Document a learning transcript that captures the discomfort of being a beginner.
- Commit and act. Every worksheet ends with a concrete action plan—specific, named, and schedulable commitments for the coming week or semester.
A Note on Structure
The worksheets are cumulative but not sequential. Worksheet 1 asks what to delegate. Worksheet 2 asks how to communicate with AI effectively. Worksheet 3 reverses the lens toward student learning and asks when AI should add friction rather than remove it. Worksheet 4 turns inward and asks who you need to become to stay relevant.
You may work through them in order or begin with the chapter that addresses your most pressing concern. Every worksheet ends with an action plan that produces specific, schedulable commitments—not aspirational statements, but concrete next steps you can take this week.
The frameworks in this guidebook are designed to be used, not merely understood. The worksheets are where the using happens.
All four worksheets are available at the companion website: dataii.com/ai/guidebook/#worksheets
© 2026 Szymon Machajewski. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
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Publication Metadata
Keywords:
- Higher education
- Artificial intelligence
- Pedagogy
- Faculty development
- Instructional design
- Cognitive science
- Educational Technology
Resources:
Companion site for updates and resources: dataii.com/ai/guidebook or bit.ly/aigymedu
Abstract:
This guidebook provides practical frameworks for higher education faculty to integrate AI thoughtfully into their teaching practice. Drawing on neuroscience research, educational theory, and real-world implementation experience, it offers four core frameworks: Cognitive Triage (managing educator workload), The Intelligent Gearbox (understanding AI capabilities), The Cognitive Gym (designing learning for brain development), and The Intelligent Simpleton (cultivating a learn-it-all mindset). The guidebook emphasizes training brains rather than replacing them, offering concrete strategies for using AI to enhance rather than diminish critical thinking and deep learning.