Factors that influence users’ adoption of being coached by an ArtificialIntelligence Coach
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.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".