An Exploration of the Utility of Appreciative Inquiry for Job Crafting and Wellbeing Promotion
Bibliographic record
Abstract
A thriving society is made possible when educators experience holistic and sustained wellbeing: feeling engaged, purposeful, happy, and effective at work, and in turn, providing an enriching learning environment for students. It is therefore a societal concern that teaching professionals report high levels of employee stress, burnout, and disengagement. The aim of the current dissertation is to not only understand how to alleviate the problems in the education profession, but to improve educators’ sense of wellbeing and work engagement. I conducted two studies exploring how the theory and practice of Appreciative Inquiry (AI) can be applied in brief and longer-term interventions to capitalize on “what’s working well” in education currently, and how educators and institutions can create a more optimal future of education. Secondly, I integrated job crafting theory to advance understanding of how AI can be applied to empower educators to create positive changes in their work, lives, and institutions to promote personal and organizational benefits. This dissertation is presented in manuscript format with an opening chapter summarizing the current state of the AI and job crafting literature. The first manuscript (Chapter 2) is a qualitative study with a sample of 17 Kindergarten to Grade 12 (K-12) teachers in Canada and the US showcasing how a foundational method of AI—the AI interview—can help educators understand how to promote their wellbeing and more personally desirable work experiences. Manuscript 2 (Chapter 3) is a qualitative case study following the experiences of 22 faculty members in a Canadian University as they participated in a 6-month program grounded in AI and job crafting theories. Finally, in Manuscript 3 (Chapter 4), I propose a guiding framework on how the AI process can facilitate job crafting. I also propose eight practical recommendations for planning and implementing an AI-facilitated job crafting intervention to promote employee wellbeing as well as larger-scale positive organizational change initiatives. This dissertation concludes with a summary chapter of all three manuscripts. Together, this dissertation progresses the nascent exploration of how researchers and practitioners alike can promote employee wellbeing and positive organizational change using an integrated AI and job crafting approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".