Evidence map›Paper›PMID 42651607›Full record

ReviewBehavioral sciences (Basel, Switzerland)2026

Artificial Intelligence as a Personal Coach: A Narrative Review of Benefits and Risks in Educational and Health Contexts.

Jason T Potel, Madoka Kumashiro

Abstract readReview
In one paragraph

Review in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jason T PotelDepartment of Psychology and Neuroscience, Goldsmiths, University of London, London SE14 6NW, UK.
Madoka KumashiroDepartment of Psychology and Neuroscience, Goldsmiths, University of London, London SE14 6NW, UK.ORCID 0009-0005-8712-5897

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The last decade has seen a rapid increase in individuals turning to artificial intelligence (AI) for advice related to their personal development, especially with the introduction of general-purpose large language models (LLMs) to the general public in 2022. This narrative review examines the potential benefits and risks of using AI for coaching purposes in educational and health contexts. Given that empirical studies on using general-purpose LLMs in these domains remain limited, this paper first synthesizes findings from purpose-built coaching chatbots designed to perform specific tasks that facilitate personal development in these domains and discusses limitations associated with studies that use older versions of chatbots. The reviewed evidence suggests that purpose-built chatbot coaching systems may have some benefits as they are generally well received and can support short-term motivation and selected behavior change, but effects for sustained, meaningful outcomes are inconsistent. We then reflect on the potential risks of using general-purpose LLMs as a coach without appropriate human oversight, by reviewing features of general-purpose LLMs, such as sycophancy, accuracy, and problematic patterns of use. Synthesizing these findings, we consider their implications before identifying potential directions for future research.

Indexed as

artificial intelligencecoachingeducationhealth/fitnesspersonal development

Identifiers

PMID42651607
PMCPMC13509547

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.