Supporting clients via narrative storytelling and artificial intelligence: a practitioner guide for career development professionals
Bibliographic record
Abstract
Purpose In this practitioner-focused essay, we combine traditional narrative storytelling approaches with Artificial Intelligence’s (AI) innovative abilities to enable career development professionals to support individuals across their lifespan. Design/methodology/approach We propose a three-phase career exploration approach, developed and tested in a real-world setting for career development professionals to support their clients to consider various career-related options as well as identify strengths and opportunities for personal development. Findings In phase one, the client recounts 7–10 positive narrative stories about engaging in activities they enjoyed. In phase two, the career development professional uses AI with tailored prompts to generate a personalised client report based on these narrative stories. In phase three, the report serves as the basis for further discussion and exploration with the client. Practical implications The approach provides a practical guide for career development professionals to increase their capability to support their clients in response to technological advancement and the contemporary world of work. A training document incorporating a worked example of the approach is provided in “Supplementary Material Appendix 1”. Originality/value Our approach acknowledges AI as a new actor and career development professionals as undervalued actors in supporting individuals to foster a sustainable career ecosystem.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".