Student therapists' experiences of learning using a machine client: A proof‐of‐concept exploration of an emotionally responsive interactive client (ERIC).
Background: The use of artificial intelligence (AI) is increasing in many areas of healthcare, including mental healthcare. The automated nature of such technologies has the potential to be developed to work with large numbers of people. This paper examines the way that student therapists experience...
| Published in: | Counselling & Psychotherapy Research Vol. 24; no. 2; pp. 524 - 532 |
|---|---|
| Main Authors: | , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Jun2024
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176586195&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176586195 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14733145 TFX jtl: Counselling & Psychotherapy Research issn: 14733145 maglogo: Y pubinfo: dt: Jun2024 vid: 24 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 176586195 171837609 176586195 176586195 10.1002/capr.12685 176586195 ppf: 524 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Student therapists' experiences of learning using a machine client: A proof‐of‐concept exploration of an emotionally responsive interactive client (ERIC). aug: au: Prescott, Julie Ogilvie, Lisa Hanley, Terry affil: University of Law, Manchester, UK sug: subj: Psychotherapy Student Attitudes Machine Learning User-Computer Interface Emotions Descriptive Statistics Feedback Human Male Female Adolescence Young Adult Adult Middle Age Aged Aged, 80 and Over Data Analysis Software Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: The use of artificial intelligence (AI) is increasing in many areas of healthcare, including mental healthcare. The automated nature of such technologies has the potential to be developed to work with large numbers of people. This paper examines the way that student therapists experience using an interactive text‐based machine client as a training tool. Methodology: Chatbot technology has been used to develop an emotionally responsive interactive client (ERIC). This introduces individuals to concepts of person‐centred therapy (empathy, congruence and unconditional positive regard) by using a series of pre‐programmed scenarios. Twenty‐eight student therapists evaluated ERIC's potential as a learning tool. Individuals were recruited from one university from a postgraduate and an undergraduate counselling programme. Findings: Feedback was generally positive, with all reporting that they enjoyed engaging with ERIC as a learning method. ERIC helped individuals consider their understanding of counselling skills in a non‐judgemental environment. Participants felt the scenarios were realistic and engaging, with many reporting that they felt they were engaging with a real client/person due to ERIC's ability to express emotions. Discussion: ERIC is at the proof‐of‐concept phase. From the feedback presented here, it is evident that it can be a useful learning tool. Further development of ERIC with feedback from a larger sample is, however, required. ERIC is currently a text‐based client, and further development would like to see the intervention be voice‐activated to enhance the experience. ERIC can be further enhanced and adapted to be a useful learning platform for student therapists, as well as for students in other (healthcare‐related) disciplines, whereby a client or patient is required. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|