Coaching education: Wake up to the new digital and AI coaching revolution!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article we argue that coach education has been through three distinct phases of development over the past three decades: 1990-2020. These phrases reflect changes in the coaching industry, which itself has seen significant change over the same period. These phases include ‘pre-profession’, reflected in ad hoc and non-qualification based training, ‘practice based professionalisation’, which saw a growth in small scale coach providers using professional body competencies, and ‘evidenced-based professionalisation’, which stimulated the growth in university based coach education programmes focused on evidenced based and research informed training. We argue that as we enter the Mid 2020’s we are witnessing a new shift in the coaching industry from ‘professionalisation’ to ‘productization’, with the emergence of large scale, digitally enabled, coaching providers. These new providers employ thousands of home working coaches and are focused on delivering coaching at scale to tens of thousands of workers in enterprise size organisations using digital channels. This industrial change calls for a need to rethink and modernise coach education. We must acknowledge the shift towards the management of industrial scale delivery and the focus on data, alongside a movement towards mastery of the technologies which have enabled coaches to work globally. We conclude by suggesting coach education should offer two new career pathways: one for those commissioning and managing coaching services and a second for those working in digital coaching firms in coaching service management, in roles such as Customer Success and Coach Relations, alongside a revitalised coach training which equips coaches to operate in digital environments through a mastery of the communication platforms, tools and apps which they employ and a deeper understanding of new technologies such as AI, VR and MR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it