Factors that influence users’ adoption of being coached by an ArtificialIntelligence Coach
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
The rise of artificial intelligence (AI) is making in-roads into many spheres of life, including workplace coaching. The introduction of a new class of support technologies ('e-coaching systems' or 'AI Coaching') promise to deliver personalised, timely, around-the-clock coaching in a wide variety of domains and to a broad audience. Chatbots as a type of e-coaching system and a form of Weak AI in particular, has the potential to replace or augment human coaches in certain instances, however it seems that speculation and hype is clouding our understanding of its true potential. This is reminiscent of the lack of evidence-based practice in coaching itself. To prevent AI Coaching from following a similar route, empirical research is needed. In this paper we summarise the findings of one of the first ever studies on the use of AI in organisational coaching. We used the Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical framework to examine the determinants associated with individuals' behavioural intention to use an AI Coach (a goal-attainment chatbot called Vicci). A total of 226 users had a coaching conversation with Vicci and then completed the UTAUT survey. Determinants of behavioural intention were measured: performance and effort expectancies, social influence, facilitating conditions, attitude and perceived risk. Structural equation modelling analysis revealed that performance expectancy, social influence and attitude are the main determinants of behavioural intent, while age, gender and level of goal attainment play a moderating role.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it